A Claude Code session can reach a state where every attempt to continue it fails the same way. The session shows Service is busy, offers Try again, and the retry counter climbs to Retrying (10/10) before stopping. According to [Anthropic's documentation](https://code.claude.com/docs/en/errors), "Claude Code retries transient failures up to 10 times with exponential backoff before showing you an error." The error card's own advice — try again in a moment, or switch to a different model — is a good first move. On several sessions across 9/3/2026, that approach did not recover the session: repeated retries and repeated model switches, minutes apart, kept failing. We forked one of those sessions instead, and the fork picked up the same conversation and continued normally.

What made that worth paying attention to was what happened next. After the fork was already working, we went back to the original session and pressed Try again. It failed. We switched models there as well, and it failed again, while the fork continued running the same work. That does not tell us why the original session remained stuck, but it does make the result more interesting than a fork that happened to coincide with capacity returning.

![A Claude Code error card headed 'Service is busy', reading 'Try again in a moment, or switch to a different model', with a View details button on the left and a Try again button beside a model picker showing Opus 5 on the right.](/images/mo-service-is-busy-card.webp)
*The error message that prompted the creation of this article. Anthropic recommends retrying or switching models; across several of our 9/3 sessions, neither recovered the original session.*

## What a 529 Overloaded error is

A 529 is the Claude API's `overloaded_error`, which [Anthropic's documentation](https://platform.claude.com/docs/en/api/errors) defines as "The API is temporarily overloaded" and attributes to "high traffic across all users." The [Claude Code error reference](https://code.claude.com/docs/en/errors#api-error-repeated-529-overloaded-errors) is blunter about what it is not: "A 529 is not your usage limit and doesn't count against your quota." Anthropic’s guidance is straightforward: check status.claude.com for a capacity incident, wait a few minutes, and use /model to switch to another model.

So the shortage sits on Anthropic's side, and nothing you do in the client creates capacity.

![The expanded 'Service was busy' detail in Claude Code, showing the text: API Error: 529 Overloaded. This is a server-side issue, usually temporary — try again in a moment. If it persists, check https://status.claude.com.](/images/mo-529-error-detail.webp)
*The same failure with its detail open.*

## When switching models isn’t enough

Switching models is Anthropic's own advice and it did not revive our stalled sessions. The reason Anthropic gives for the advice is that "capacity is tracked per model" — the [error reference](https://code.claude.com/docs/en/errors#api-error-repeated-529-overloaded-errors) says so, and adds that Claude Code raises the suggestion itself when a single model is carrying heavy load, in the form "Opus is experiencing high load, please use /model to switch to Sonnet". We switched models from the error card's dropdown several times, waited minutes between attempts, and got the same failure on the same session each time.

We cannot tell you why a particular session stops accepting requests while the account keeps working elsewhere, because Anthropic does not document a per-session capacity behavior and we have not measured one. What we can tell you is what we do instead of continuing to press Try again.

![A Claude Code status line reading 'Model overloaded · Retrying (4/10) · 23s', with a Try again button above it.](/images/mo-retrying-4-of-10.webp)
*Four attempts in, 23 seconds elapsed. Claude Code is retrying on its own before it shows you anything.*

## Fork the session

A fork writes everything said so far to a second session and puts you in that second session. The session you were in is left exactly as it was. In the [Claude desktop app](https://code.claude.com/docs/en/desktop) and at [claude.ai/code](https://claude.ai/code), right-click the session in the sidebar and choose **Fork session**, or press `Cmd`+`Option`+`O` on macOS and `Ctrl`+`Alt`+`O` on Windows and Linux. From a message rather than a session, the same menu offers **Fork from here**.

From the terminal, Anthropic documents two routes on the [session management page](https://code.claude.com/docs/en/sessions#branch-a-session): run `/branch` inside the session, optionally with a name, or combine a resume flag with `--fork-session`.

Either route ends with two independent sessions. Anthropic's [architecture overview](https://code.claude.com/docs/en/how-claude-code-works#resume-or-fork-sessions) describes the split: forking "copies the history into a new session ID, leaving the original unchanged." The `/branch` confirmation prints both session IDs, and the original stays in the session picker, so you lose nothing if the fork does not help.

## What a fork carries over, and what it does not

Forking carries the conversation into a new session. When you branch from inside the running session with /branch, Anthropic documents several things that carry over with it:

- **Conversation history** is copied into the fork up to the moment you branch.
- **Permission grants you approved with "Allow for this session"** carry over, because `/branch` keeps the same running process. Start the fork as a separate process with `--fork-session` and those approvals are requested again.
- **Anything already running in the background** — a subagent, a background Bash command — carries on, and you see its output in the fork rather than in the session you left.
- **A Remote Control connection** follows you into the fork, so a phone or browser watching the session keeps receiving messages.

Two limits matter. A fork branches the conversation, not the filesystem — edits a forked session makes are real, and visible to anything else working in that directory. And the fork carries the history, so the next request still has to process it: Anthropic notes that a session over roughly 100,000 tokens that has been idle for about an hour has [an expired prompt cache](https://code.claude.com/docs/en/sessions#resume-from-a-summary) and will process the full history once regardless.

## What we can claim, and what we cannot

Forking is not documented by Anthropic as a remedy for capacity errors, and we are not presenting it as one. Anthropic's documented remedies for a 529 are the status page, waiting, and `/model`.

Our claim is first-hand and has one piece of evidence in it worth separating from the rest. The weak version — "we forked and it worked" — is consistent with the shortage ending at that moment on its own. The strong version is what we actually saw: **the original session kept failing after the fork was already working.** Same account, same machine, same directory, same conversation, minutes apart, and one of them ran while the other returned the same error to both Try again and a model switch. The original session kept failing after the fork was already working. That sequence is harder to explain as simply “capacity happened to return while we were forking,” although it does not tell us what differed between the two sessions.

Anthropic does not document any per-session behavior for a 529, and we have not instrumented one — we have a repeated observation, not a mechanism. We also have not run a controlled comparison, cannot give you a success rate, and would not expect a fork to help at all during a platform-wide incident, which the status page is there to tell you about.

We reach for a fork early anyway because it costs almost nothing to try. A fork takes one shortcut, leaves the original session on disk, and either works or leaves you exactly where you were. Ten more minutes of pressing Try again on a session that has already spent a ten-attempt retry budget costs ten minutes and has already failed once.

![A Claude Code status line reading 'Model overloaded · Retrying (10/10) · 3m 42s'.](/images/mo-retrying-10-of-10.webp)
*Anthropic documents the limit as ten retries with exponential backoff*

## What forking costs

A fork is cheap, but it is not free. Anthropic documents three of the costs below; the fourth is a limit of what forking is for.

**A fork carries the whole conversation, so it does not make later requests smaller.** Anthropic's [cost guide](https://code.claude.com/docs/en/costs#why-usage-climbs-in-a-long-session) explains why that matters. Claude Code continues to account for the conversation context on later requests, so a trivial follow-up in a long-running session can still incur input usage based on that much larger history. Forking a long session gives you a second long session. If what you actually want is a smaller context, reach for `/compact` or a fresh session instead — forking gives you the opposite.

**Two sessions now exist on disk and in the picker.** `/branch` copies the transcript, both sessions keep their own IDs, and Anthropic's [session page](https://code.claude.com/docs/en/sessions#branch-a-session) notes that forked sessions appear as separate rows. A second row is the whole point when you want to go back to the original, and clutter when you never do.

The cost that surprised us is the one attached to the *other* remedy. **Switching models forces a cache miss; a same-directory fork can reuse the parent's warm cache.** Anthropic's [prompt-caching page](https://code.claude.com/docs/en/prompt-caching#switching-models) explains that prompt caches are model-specific, so changing models with /model prevents the next request from reusing the cache built for the previous model. A fork behaves differently. Because it begins with the parent's existing context, Anthropic documents that its first request can reuse the prompt cache already associated with that session history.

So the advice everyone reaches for first is the expensive one, and we took it several times on a long session before forking. Cached input is billed at [roughly a tenth of the standard input rate](https://code.claude.com/docs/en/prompt-caching#check-cache-performance), which is the gap each model switch gives up.

One limit on that. Anthropic scopes the cache [per machine and per directory](https://code.claude.com/docs/en/prompt-caching#cache-scope), and says explicitly that this "includes worktrees of the same repository" — so a fork you continue in a different worktree builds its own prefix instead of reading the original's, and the saving above does not apply to it.

None of this makes forking expensive. Forking is cheap in exactly the way people assume a model switch is.

## Two settings that change how often a session stalls

Two configuration options change how Claude Code behaves when a model is overloaded. Neither one changes the capacity behind the error.

**A fallback model list** answers overload without your involvement. Claude Code's [model configuration guide](https://code.claude.com/docs/en/model-config#fallback-model-chains) documents `--fallback-model` for a single session and a `fallbackModel` array in settings to keep the list across sessions. Claude Code tries the listed models in order whenever the model the session is on is overloaded or unavailable, and accepts at most three.

```json
{
  "fallbackModel": ["claude-sonnet-5", "claude-haiku-4-5"]
}
```

**The retry watchdog** suits unattended work. Anthropic's [error reference](https://code.claude.com/docs/en/errors) documents `CLAUDE_CODE_RETRY_WATCHDOG`, which, set to `1`, keeps retrying capacity errors — both 429 throttles and 529 overloads — rather than giving up at the configured attempt limit. Setting it in an interactive session means a session can sit retrying instead of telling you to intervene, so it belongs in continuous integration jobs and scheduled runs, not on your laptop.

## Questions we get

**Does a 529 count against my usage limit?**
No. Anthropic's [Claude Code error reference](https://code.claude.com/docs/en/errors#api-error-repeated-529-overloaded-errors) states that "A 529 is not your usage limit and doesn't count against your quota." A 429 is the rate-limit and spend-cap error, and [Anthropic's API error page](https://platform.claude.com/docs/en/api/errors) documents the two separately.

**How many times does Claude Code retry before showing me the error?**
Up to ten, with exponential backoff, per the [error reference](https://code.claude.com/docs/en/errors) — which is the `(10/10)` in the retry label. Some failure classes get a smaller budget or none; that page lists which.

**Will forking lose my conversation?**
No. A fork writes the conversation to a second session under a new ID and does not touch the session you forked from, per Anthropic's [architecture overview](https://code.claude.com/docs/en/how-claude-code-works#resume-or-fork-sessions). Both sessions stay in the session picker with their own IDs, so you can go back.

**Is forking the same as `/compact` or rewinding?**
No, and Anthropic's [checkpointing page](https://code.claude.com/docs/en/checkpointing) draws the line explicitly: a summarize action stays inside one session and shrinks its context, whereas `/branch` and `claude --continue --fork-session` create a second session and leave the session you started in untouched. A rewind is different from both: a rewind puts code or conversation back to an earlier point inside a single session.

**Does forking cost extra tokens?**
Not for the fork itself. Anthropic's [prompt-caching page](https://code.claude.com/docs/en/prompt-caching#subagents-and-the-cache) explains that a fork can reuse cached context from its parent on its first request. The switch that does cost is `/model`: every model is cached separately, so a model switch pays to process the entire history again. What a fork will not do is make the conversation smaller — it carries the full history, so every later request in it is as large as it was before.

**Should I switch models when a session is overloaded?**
Yes, try a model switch first — a switch is Anthropic's documented advice, and capacity is tracked per model. Just check which kind of switch you got: a `/model` change holds for the rest of the session, while a switch made by a [fallback chain](https://code.claude.com/docs/en/model-config#fallback-model-chains) applies to a single turn and then reverts to your primary model.

## Thank you for your time

If you have any questions or want to connect on anything that I wrote about above, please [email me](mailto:cooper@veyseysoftwaresolutions.com) or [book some time on my calendar](https://calendar.google.com/calendar/appointments/schedules/AcZssZ041TFFhKz0LzQydfOipmZ3l8K-22DadOWnsRMPnLkS8TjaDIreyZ8fFxAimnDxsCeimFSvnY_3). Any and all feedback is of course so appreciated.


When Claude Code reports 'Model overloaded', try forking the session

When ChatGPT, Claude, Perplexity or another AI service answers a question about your business, it may retrieve or rely on content from your website. The AI companies say so themselves: OpenAI documents its crawlers [here](https://developers.openai.com/api/docs/bots), Anthropic lists its crawlers [here](https://support.claude.com/en/articles/8896518-does-anthropic-crawl-data-from-the-web-and-how-can-site-owners-block-the-crawler), and Perplexity records its crawlers [here](https://docs.perplexity.ai/guides/bots). Every one of those fetches reaches your server. These fetches don't necessarily appear via your analytics dashboards and reports.

Last month we built a product for our partners that shows exactly these visits — which AI system fetched which page, when, and which published crawler role it came from. This article explains why the visits are invisible to many ordinary analytics platforms, what our dashboard records, how we collect the data on our own architecture, and how to get comparable visibility if your site runs on Cloudflare or Shopify.

## Why some analytics platforms cannot see these visits

An analytics tag records a visit by running JavaScript in a browser, and that is where this traffic slips through — though not always for the reason people assume. Some crawlers issue a plain server-to-server request for the raw HTML and never execute a script at all; that is what we see in the fetches we record on the sites we maintain. Others genuinely do render, and are still missed — Google's own guidance is that "[client-side analytics may not provide a full or accurate representation of Googlebot and WRS activity on your site](https://developers.google.com/search/docs/crawling-indexing/javascript/fix-search-javascript)", because Googlebot may not fetch reporting requests that do not contribute to page content. Either way the conclusion holds: if your measurement depends on a tag firing in a browser, there is a good chance this category of traffic is invisible to it.

The visits are still in your server logs — requests from the major vendors can often be identified by their published user agents and, where available, their verified IP ranges. A user-agent string on its own can be spoofed, which is why [Cloudflare's verified-bots system](https://developers.cloudflare.com/bots/concepts/bot/verified-bots/) checks IP lists, reverse DNS or cryptographic signatures as well as self-identification. The data is not secret. Turning those log lines into something a human can read is a step someone has to build.

## Three crawler roles, and which vendors publish them

The vendors themselves distinguish why a fetch happens, and the distinction matters commercially. OpenAI is explicit that "[OAI-SearchBot is for search](https://developers.openai.com/api/docs/bots)," that GPTBot exists for training — in OpenAI's words, it "[is used to make our generative AI foundation models more useful and safe](https://developers.openai.com/api/docs/bots)" — and that ChatGPT-User fetches happen when a person asks ChatGPT something that requires reading your page. [Anthropic categorizes things similarly](https://support.claude.com/en/articles/8896518-does-anthropic-crawl-data-from-the-web-and-how-can-site-owners-block-the-crawler): ClaudeBot handles training; Claude-User covers user-initiated requests; and Claude-SearchBot exists, in Anthropic's words, to "improve search result quality". [Perplexity states](https://docs.perplexity.ai/guides/bots) that PerplexityBot indexes for its search results and "is not used to crawl content for AI foundation models," while Perplexity-User fetches on a user's behalf. So the roles are not evenly distributed: OpenAI and Anthropic each publish a crawler for all three, while Perplexity publishes two and states plainly that neither is used for foundation-model training.

Those are three different events for your business:

- **Answering a question.** Somebody put a question to an assistant, and your page was fetched to build the reply. For many businesses, of everything recorded here, this is the nearest thing to a lead.
- **Indexing for search.** Your page is being catalogued so an assistant can surface it later — it is how you become quotable.
- **Training a model.** A crawler whose published purpose includes model training requested your content. I am not at this time aware of any direct benefit for the publisher for this kind of traffic. Something for me to further investigate at a later time.

![A table headed Which assistants, listing crawler names against their role and visit counts: OAI-SearchBot indexing for search with 54 visits, ChatGPT-User answering a question with 51, Claude-User answering a question with 48, Perplexity-User answering a question with 33, ClaudeBot training a model with 15, and further rows for PerplexityBot, Google-Extended, BingBot, GPTBot, GoogleOther and Claude-SearchBot.](/images/ai-traffic-assistants.webp)
*The roles are not an abstraction — each vendor's published crawler names arrive in the log and map onto them directly. Note OAI-SearchBot and ChatGPT-User separating OpenAI's indexing from its user-triggered fetches.*

## What the dashboard shows

Our AI traffic page groups every recorded fetch by those three published roles, using each vendor's own user agents as the classification key. One limit worth stating plainly: this classifies a request by the role its crawler is published under, not by what the result was ultimately used for. OpenAI says that where a site permits both OAI-SearchBot and GPTBot, it may use one crawl for both purposes — so a role is a reliable label for the requester, not a claim about a single exclusive downstream use. Above the log, it answers the questions people actually ask of this data:

- **Totals by reason**, over a selectable 7-, 30- or 90-day window.
- **A by-day chart**, stacked by reason, so a spike is visible and attributable to a day.
- **A vendor filter** — only OpenAI's fetches, only Anthropic's, and so on.
- **A per-page view**: enter one path and see when anything first fetched it and everything that has read it since. For a newly published page, "has any tracked AI crawler or fetcher requested this yet?" is a question with a date-stamped answer.
- **Filters that live in the URL**, so a filtered view is a link a person can send to a colleague.
- **Honest empty states.** If collection has not been set up for a site, the page says so and explains the step — it does not show four zeros and let the reader conclude nothing is happening.

![The AI traffic page's headline tiles, stacked one per row: 274 visits in 30 days, then 132 answering a question, 101 indexing for search, and 41 training a model, each with a plain-English explanation of what that reason means.](/images/ai-traffic-totals-v2.webp)
*Totals for a 30-day window, split by the role of the crawler that made each request. Every screenshot here is our demo Studio — a fictional firm with synthetic data — so no client's figures appear.*

![A stacked bar chart titled By day, one bar per day from Aug 3 to Sep 1, each split into answering a question, indexing, and training. The busiest day is 15 visits, and a colour key below names the three bands.](/images/ai-traffic-by-day-v2.webp)
*The same visits by day, stacked by role, so a spike is attributable to a date rather than lost in a monthly total.*

## How we collect it on our own architecture

Our platform hosts a content studio for multiple client organizations, each isolated as its own tenant. Collection works one of two ways, depending on where the client's website lives:

- **Sites served through our own proxy** log AI fetches directly: the server answering the request records the path and user agent as it responds, so there is nothing to install.
- **Sites on their own hosting** get a small pass-through worker deployed in front of the site. It forwards every request untouched, and for requests whose user agent matches a generous list of AI-vendor tokens, it reports the path and user agent to our platform's ingest endpoint — fire-and-forget, so the site is never delayed or altered, and each site authenticates with its own revocable key.

One deliberate design decision: classification happens on our platform, against a single signature list, not in the worker. The worker's filter is coarse on purpose — a token it forwards unnecessarily costs one tiny request, while a token it misses is an AI read never counted. Keeping the real classification in one place means "AI traffic" means the same thing for every site we operate, and improving the signature list improves every dashboard at once.

## If your site runs on Cloudflare, start there

You do not need a custom platform to get this visibility. [Cloudflare's AI Crawl Control](https://developers.cloudflare.com/ai-crawl-control/) is available on all Cloudflare plans, including the free one, and [its analytics](https://developers.cloudflare.com/ai-crawl-control/features/analyze-ai-traffic) show request volumes, status codes, popular paths, and individual crawlers grouped by operator — OpenAI, Anthropic, Google, Microsoft, Meta and others — with allow and block controls per crawler. Referral metrics, which show traffic arriving from AI services rather than fetches by them, sit on the paid plans. If your site traffic is already proxied through Cloudflare, this is a settings page away, and we would recommend looking there before building anything.

We built our own because our clients' dashboards live inside our platform, scoped per tenant next to their content, search and publishing data — and because several client sites do not run through Cloudflare at all. Different architecture, same goal.

## If your store runs on Shopify

Shopify gives merchants control over crawlers through [the robots.txt.liquid template](https://shopify.dev/docs/storefronts/themes/architecture/templates/robots-txt-liquid), documented in [the Shopify Help Center](https://help.shopify.com/en/manual/promoting-marketing/seo/editing-robots-txt) — that is where allow and disallow decisions for the AI user agents above belong on a Shopify store. Control, however, is not the same as operator-level visibility: robots.txt shapes who may fetch, and tells you nothing about who did. Shopify does now offer broad visibility of its own — a [Human or bot session dimension and filter in Analytics](https://help.shopify.com/en/manual/intro-to-shopify/bots/bot-filtering), applied to incoming data from 7 October 2025. It classifies a session as probably human or probably bot; it does not tell you which operator, so it will not separate OpenAI from Anthropic from an ordinary scraper.

One path to visibility on Shopify is putting Cloudflare in front of the store, which historically was a famously risky configuration. [Tony Castillo](https://www.linkedin.com/in/theantoniocastillo/) from [WISLR](https://www.wislr.com/) has written [a careful account of Cloudflare's Orange-to-Orange routing and what it changed](https://www.wislr.com/articles/cloudflare-shopify-o2o-proxy) — which warnings are obsolete, which still bind, and the exact settings that keep certificate renewal working. He reports that with O2O, a proxied CNAME routes requests through your Cloudflare zone first and Shopify's second, which is precisely the position from which Cloudflare's AI crawler analytics can observe a Shopify storefront. I have worked with Tony a few times before, and I can't say enough good things about his technical skills and SEO / AEO / GEO-related expertise. He is a remarkably talented writer, and a total pleasure to work with. Cloudflare documents the Shopify O2O path themselves and states that it can be enabled on any Cloudflare zone plan; [their provider guide](https://developers.cloudflare.com/cloudflare-for-platforms/cloudflare-for-saas/saas-customers/provider-guides/shopify/) is the official companion to Tony's write-up. If you are weighing that setup, read both before touching DNS.

## What this data can and cannot tell you

It can tell you which tracked AI systems requested your pages, what they requested, when the requests began, and — for user-triggered fetchers specifically — which pages were requested in connection with someone's question — first-hand, from your own infrastructure, with no sampling and no third-party attribution model in the way.

It cannot tell you that you will be cited, ranked, or recommended. Google's own guidance on generative AI features says "[optimizing for generative AI search is optimizing for the search experience, and thus still SEO](https://developers.google.com/search/docs/fundamentals/ai-optimization-guide)," and that "[Structured data isn't required for generative AI search](https://developers.google.com/search/docs/fundamentals/ai-optimization-guide)." We take the same line about measurement: a fetch is evidence of attention, not a promise of a citation. What the dashboard changes is that decisions about AI visibility start from recorded visits instead of from guesses.

## Questions we get

**Why doesn't Google Analytics show visits from ChatGPT or Claude?**
Because analytics tags run as JavaScript in a browser. Some AI crawlers never execute scripts at all; others do render pages but may not fetch the reporting requests a tag depends on — Google says as much about Googlebot and its rendering service. Either way the requests are in your server logs, and a browser tag may never record them.

**What is the difference between GPTBot and ChatGPT-User?**
GPTBot is OpenAI's training crawler, and ChatGPT-User is the fetcher that reads a page because a person asked ChatGPT a question requiring it — [OpenAI documents both](https://developers.openai.com/api/docs/bots), along with OAI-SearchBot for search indexing. Blocking one does not block the others.

**Should I block AI crawlers?**
That is a per-purpose decision, and the vendor documentation is built for making it: OpenAI and Anthropic each publish distinct user agents for training, search and user-initiated fetches, so a robots.txt file can, for example, disallow training crawlers while leaving search indexing and user fetches open. Perplexity is the exception worth knowing: it publishes a search crawler and a user fetcher, and states that neither is used to crawl content for foundation models. One caveat from [Perplexity's own documentation](https://docs.perplexity.ai/guides/bots): its user-initiated fetcher "generally ignores robots.txt rules," on the reasoning that a person, not a bot, asked for the page.

**Does being fetched mean being cited?**
No. A fetch means a tracked crawler or fetcher requested the page; whether the page is quoted, cited or recommended is decided elsewhere, and no vendor documents a guarantee. Treat fetch data as an indicator worth paying attention to.

## Ask us for an AI traffic readout

Want to know what is reading your site? [Let us know](mailto:cooper@veyseysoftwaresolutions.com). We can review your hosting architecture, set up first-party AI traffic collection appropriate to it — Cloudflare's tools where they fit, our collection layer where they do not — and share a concise readout of which AI systems are reading your pages, what they read most, and what that suggests about how assistants currently see your business.

## Thank you for your time

If you have any questions or want to connect on anything that I wrote about above, please [email me](mailto:cooper@veyseysoftwaresolutions.com) or [book some time on my calendar](https://calendar.google.com/calendar/appointments/schedules/AcZssZ041TFFhKz0LzQydfOipmZ3l8K-22DadOWnsRMPnLkS8TjaDIreyZ8fFxAimnDxsCeimFSvnY_3). Any and all feedback is of course so appreciated.

The AI crawler traffic that many analytics platforms miss — and how our product captures it

Our team maintains a 27-point SEO + GEO health checklist designed to catch the technical and structural issues that can affect whether search engines and AI assistants can find, read, understand and cite a site. Every check links to the published documentation that says it matters. As of August 2026, the checklist draws from thirty-four resources published by nineteen organizations.

Sixteen of the twenty-seven checks re-run automatically on the first of every month. The remaining eleven require human judgment, so they are reviewed manually, dated, and signed by the person who made the call. The result is not a one-time SEO audit. It is a continuously updated record of what is healthy, what needs attention, what has not yet been checked, and exactly why.

## Strong GEO starts with strong SEO

**Seventeen rows are about search engines, five are about AI assistants specifically, and five count for both.** GEO — generative engine optimization — is the part aimed at AI assistants: whether one can retrieve, read and cite your pages. SEO is the part aimed at the search results themselves. Most of what serves one serves the other, which is why the two live on one list rather than two.

The five assistant rows exist because those levers are documented only by the assistant vendors, and a Google-sourced checklist misses them entirely. A site can be wide open to Googlebot and invisible to ChatGPT, Claude and Perplexity at the same time, because those crawlers are separate and are controlled separately. When a row concerns assistants, it cites the assistants.

## Every row answers the same five questions

Open any row on the checklist and it tells you five things, in the same order, every time:

- **Standard** — what "good" means for this row, stated plainly enough that you can disagree with it.
- **Why it matters** — what it affects, claiming no more than the source claims.
- **Evidence** — what we actually saw: the URL fetched, and what came back.
- **Checked** — automatically once a month, or by a named person on a named date.
- **Source** — the published resource behind the standard, linked.

The verdict beside each row is one of seven, and the distinctions are the point. **Good** and **Needs attention** are the two you would expect. **Not yet audited** is for a row nobody has checked yet, in its own colour, because a checklist that hides its unchecked rows is a checklist that lies by omission. **Not yet connected** is for a check that is red only because a setup step is unfinished — a new site is not broken because nobody has connected Search Console. **Not enough visitor data yet** is for Core Web Vitals on a site Google has not published enough real-visitor numbers about — we will not grade its lab test as though it were the same measurement. **Could not check** is for a fetch that failed, which our engineering team is prompted to investigate. **Not applicable** is also an option.

## Sixteen checks that re-run themselves, eleven a person signs

Sixteen checks require nothing more than a public web address to execute. No plugin, no tracking script, no access to your CMS or your server. A machine reads your `robots.txt`, finds your sitemap wherever it actually lives, fetches pages that the sitemap lists, reads the homepage's `<head>` and response headers, and inspects the certificate. Then it writes down what it saw, with the URL beside it, and does the same thing again next month.

Running the same question against the same site on a schedule is what separates this from a point-in-time resource that turns stale over time. Sites drift. A plugin update adds a directive nobody chose, a redesign drops a canonical tag, a security setting starts blocking crawlers that were welcome last quarter. 

The eleven person-audited rows split into two kinds, and it is worth knowing which is which before you budget for them.

**Five are set up once and then stay done.** Registering in Bing Webmaster Tools, and verifying Instagram, TikTok, X and YouTube as platform properties in Search Console.

**Six are re-judged as the site changes**, because they are conclusions rather than readings: are there pages nothing links to, is any content "thin" enough to be worth removing, does each practice area have a main page with its supporting articles pointing back at it, has the site been reviewed against WCAG. 

## The sixteen automatic checks

Nine of these cover search engines, four cover AI assistants, and three count for both.

| Check | Our standard, and who publishes it |
| --- | --- |
| Sitemap | `sitemap.xml` exists, parses, and lists the site's pages — [Google Search Central](https://developers.google.com/search/docs/crawling-indexing/sitemaps/overview) |
| robots.txt | Crawlers are not blocked from the site — [Google Search Central](https://developers.google.com/search/docs/crawling-indexing/robots/intro) |
| AI assistant crawlers | The crawlers Claude, ChatGPT, Perplexity, Apple, DuckDuckGo and Copilot use to retrieve and cite your site are not blocked in `robots.txt` — [OpenAI](https://developers.openai.com/api/docs/bots) · [Anthropic](https://support.claude.com/en/articles/8896518-does-anthropic-crawl-data-from-the-web-and-how-can-site-owners-block-the-crawler) · [Perplexity](https://docs.perplexity.ai/guides/bots) · [Apple](https://support.apple.com/en-us/119829) · [DuckDuckGo](https://duckduckgo.com/duckduckgo-help-pages/results/duckassistbot/) · [Google](https://developers.google.com/search/docs/crawling-indexing/google-common-crawlers) · [Bing](https://www.bing.com/webmasters/help/webmasters-guidelines-30fba23a) |
| HTTPS redirect | Plain `http://` permanently redirects to `https://` — [Google Search Central](https://developers.google.com/search/docs/crawling-indexing/301-redirects) |
| One address | The www and bare forms of the domain collapse to one host with a permanent redirect — [Google Search Central](https://developers.google.com/search/docs/crawling-indexing/consolidate-duplicate-urls) |
| Canonical tags | The homepage declares a canonical URL on this domain — [Google Search Central](https://developers.google.com/search/docs/crawling-indexing/canonicalization) |
| Page titles and descriptions | Every page listed in the sitemap has a `<title>` and a `<meta name="description">`, and neither is left blank — [Google Search Central](https://developers.google.com/search/docs/appearance/title-link) · [Google Search Central](https://developers.google.com/search/docs/appearance/snippet) |
| Structured data | The homepage carries JSON-LD that parses and has its fields filled in — not a blank template — [Google Search Central](https://developers.google.com/search/docs/appearance/structured-data/local-business) |
| Organization schema and entity links | The homepage identifies the business as an Organization in JSON-LD, and every profile URL that block lists under `sameAs` still resolves — [Google Search Central](https://developers.google.com/search/docs/appearance/structured-data/organization) · [schema.org](https://schema.org/sameAs) |
| Link previews | The homepage carries `og:title` and `og:image`, so a link to it renders as a preview card — [The Open Graph protocol](https://ogp.me/) |
| AI citation directives | No `noarchive`, `nocache` or `nosnippet` directive is suppressing the site in AI answers — [Bing Webmaster Guidelines](https://www.bing.com/webmasters/help/webmasters-guidelines-30fba23a) · [Apple](https://support.apple.com/en-us/119829) |
| SSL certificate | The certificate is valid and not close to expiry — [Google Search Console Help](https://support.google.com/webmasters/answer/11396518) |
| Core Web Vitals | Google's measurements from real visitors to the homepage on a phone meet its published thresholds: LCP 2.5 seconds or less, INP 200 milliseconds or less, CLS 0.1 or less, at the 75th percentile. Where Google has not published enough real-visitor data for the site, the row says so rather than grading a lab test as though it were the same thing — [Google Search Central](https://developers.google.com/search/docs/appearance/page-experience) · [web.dev](https://web.dev/articles/vitals) |
| llms.txt | Reported, never graded: whether a guide for AI assistants exists at `/llms.txt` — [Google Search Central](https://developers.google.com/search/docs/fundamentals/ai-optimization-guide) · [OpenAI](https://developers.openai.com/api/docs/bots) |
| Agent capabilities | Reported, never graded: whether the site publishes a machine-callable capability surface — an API catalogue or an MCP server card — that an assistant can discover — [IETF RFC 9727](https://www.rfc-editor.org/rfc/rfc9727.html) · [Cloudflare](https://blog.cloudflare.com/agent-readiness/) |
| Search Console | Google Search Console is connected and answering queries — [Google Search Console Help](https://support.google.com/webmasters/answer/9128668) |

## The five you set up once

| Check | Our standard, and who publishes it |
| --- | --- |
| Bing Webmaster | The site is registered and verified in Bing Webmaster Tools — [Microsoft Bing Webmaster](https://blogs.bing.com/webmaster/June-2025/Start-Using-Bing-Webmaster-Tools-to-Improve-Your-Site-Visibility) |
| Instagram in Search | The firm's Instagram account is verified as a platform property in Search Console — [Google Search Console Help](https://support.google.com/webmasters/answer/17148418) |
| TikTok in Search | The firm's TikTok account is verified as a platform property in Search Console — [Google Search Console Help](https://support.google.com/webmasters/answer/17148418) |
| X in Search | The firm's X account is verified as a platform property in Search Console — [Google Search Console Help](https://support.google.com/webmasters/answer/17148418) |
| YouTube in Search | The firm's YouTube channel is verified as a platform property in Search Console — [Google Search Console Help](https://support.google.com/webmasters/answer/17148418) |

Google's platform properties let you see how content you publish on those accounts performs in Google Search, alongside News and Discover. The setup is verifying the account as its own Search Console property, and the row is marked not applicable where a business does not post on that platform. 

## The six a person re-judges

| Check | Our standard, and who publishes it |
| --- | --- |
| Internal links | Every page worth finding was linked from at least one other page at the last audit, and the pages that win work are reachable in a few clicks from the homepage — [Google Search Central](https://developers.google.com/search/docs/crawling-indexing/links-crawlable#internal-links) · [Bing Webmaster Guidelines](https://www.bing.com/webmasters/help/webmasters-guidelines-30fba23a) · [Search Engine Journal, reporting Google's John Mueller](https://www.searchenginejournal.com/google-click-depth-matters-seo-url-structure/256779/) |
| Thin content | No empty or low-value pages stood at the last audit — [Google Search Central](https://developers.google.com/search/docs/fundamentals/creating-helpful-content#content-and-quality-questions) |
| Practice-area hubs | Each practice area has a main page, and its related articles link back to it — [Google Search Central](https://developers.google.com/search/docs/crawling-indexing/links-crawlable#internal-links) |
| Accessibility | The site was reviewed against WCAG, and anything that would stop a screen-reader user completing an enquiry was fixed — [U.S. Department of Justice](https://www.ada.gov/resources/web-guidance/) · [W3C Web Accessibility Initiative](https://www.w3.org/WAI/standards-guidelines/wcag/) |
| Brave Search | The site's pages are turning up in Brave, and its submit page has been used for anything recently changed — [Brave Search](https://search.brave.com/help/brave-search-crawler) |
| Markdown copies of pages | Reported, never graded: whether key pages are also served as clean Markdown, so an assistant reading them gets the words without the page furniture — [The llms.txt proposal](https://llmstxt.org/) · [Google Search Central](https://developers.google.com/search/docs/fundamentals/ai-optimization-guide) |

Brave is on the list because it costs almost nothing to satisfy. Brave states that where Googlebot cannot crawl a page, its own crawler will not either — so the work already done for Google carries over, and there is no account to open, nothing to verify and no second console to keep. Its one lever is a submit page for asking it to re-fetch something you have changed. It matters more than its size suggests, because its index feeds answers beyond its own search box. [Brave documents its crawler and that submit page](https://search.brave.com/help/brave-search-crawler).

## The nineteen organizations behind the list

The checklist is written from published documentation rather than opinion, and no row claims more than its source claims. These are the organizations, and the tables above link the specific resource each row rests on:

**Search engines** — [Google Search Central](https://developers.google.com/search/docs/fundamentals/seo-starter-guide), [Google Search Console Help](https://support.google.com/webmasters/answer/9128668), [web.dev](https://web.dev/articles/vitals), [Bing Webmaster Guidelines](https://www.bing.com/webmasters/help/webmasters-guidelines-30fba23a), [Microsoft's Bing Webmaster blog](https://blogs.bing.com/webmaster/June-2025/Start-Using-Bing-Webmaster-Tools-to-Improve-Your-Site-Visibility) and [Brave Search](https://search.brave.com/help/brave-search-crawler).

**AI assistants, in their own words** — [OpenAI](https://developers.openai.com/api/docs/bots), [Anthropic](https://support.claude.com/en/articles/8896518-does-anthropic-crawl-data-from-the-web-and-how-can-site-owners-block-the-crawler), [Perplexity](https://docs.perplexity.ai/guides/bots), [Apple](https://support.apple.com/en-us/119829) and [DuckDuckGo](https://duckduckgo.com/duckduckgo-help-pages/results/duckassistbot/).

**Standards bodies and specifications** — [schema.org](https://schema.org/sameAs), [the Open Graph protocol](https://ogp.me/), [IETF RFC 9727](https://www.rfc-editor.org/rfc/rfc9727.html), [the W3C Web Accessibility Initiative](https://www.w3.org/WAI/standards-guidelines/wcag/) and [the llms.txt proposal](https://llmstxt.org/).

**Everyone else** — [the U.S. Department of Justice](https://www.ada.gov/resources/web-guidance/) on web accessibility and the ADA, [Cloudflare](https://blog.cloudflare.com/agent-readiness/) on how many sites are ready for agents, and [Search Engine Journal](https://www.searchenginejournal.com/google-click-depth-matters-seo-url-structure/256779/) reporting Google's John Mueller on click depth.

## Two problems nothing warns you about

Both of the faults below leave the rest of a site looking fine: the pages load, the build passes, and no editor says a word. Finding them is the practical reason to write a checklist from the documentation rather than from habit.

**A site can be crawlable and still uncitable.** A `noarchive`, `nocache` or `nosnippet` directive leaves crawling completely intact — so every other check reads clean — while suppressing the site in AI answers. Two vendors document that independently, which is what lifts it from a plausible worry to a checkable fact. Microsoft states that "Content tagged NOARCHIVE will not be included in Bing Chat answers, not be linked to in the answers," and that content tagged NOCACHE "may be included in Bing Chat answers. We will only display URL/Snippet/Title in the answer." [Bing's Webmaster blog states both](https://blogs.bing.com/webmaster/september-2023/Announcing-new-options-for-webmasters-to-control-usage-of-their-content-in-Bing-Chat). Apple names the same family of lever for its own answers: "Web publishers can opt out of their content being used in these broad world knowledge answers by applying the nosnippet meta tag to specific content." [Apple documents that on its Applebot page](https://support.apple.com/en-us/119829). A CMS, a plugin or a well-meaning "stop AI scraping" setting can add any of these without anyone deciding to, so we check both places they can hide: the page's meta robots tag and the `X-Robots-Tag` response header.

The same logic put page titles and descriptions on the list. Nothing warns you when they are blank — no editor warning, no red mark, no failed build. The page goes live looking normal to whoever wrote it, and the only place the omission shows up is a search result nobody at the firm is looking at. On one site we reviewed, nearly every published article had both fields empty. Not one of those was a decision; it was the default, repeated post after post. Google says a good title link makes a result more relevant to searchers, and that "Snippets are primarily created from the page content itself" — so a blank description does not remove the page, it hands the wording to a machine. [Google documents title links](https://developers.google.com/search/docs/appearance/title-link) and [how snippets are created](https://developers.google.com/search/docs/appearance/snippet). That is the shape of defect a monthly machine check earns its keep on, and the shape a human audit risks missing, because catching it means opening seventy-seven pages to read their `<head>`.

## What we can responsibly claim

None of this is a ranking claim, and we do not sell it as one. Google is explicit that "Google does not guarantee that features that consume structured data will show up in search results," and the same restraint applies across the list. [Google states that in its Organization documentation](https://developers.google.com/search/docs/appearance/structured-data/organization).

What a sourced, dated, re-running checklist does buy is narrower and more durable. You can see what was checked and what was not. You can see when, and you can see what each check actually means. You can see who says it matters, and quickly and easily open the source document yourself. And where a row is red, you can tell at a glance whether that is a fault on the site, an unfinished setup step, a fetch that failed on our side, or a measurement Google has not published enough visitor data for us to make.

The list is twenty-seven items today, and it grows when a source justifies a new row.

## Ask for a checklist run on your site

Want to know which of these your site currently passes? [Let us know](mailto:cooper@veyseysoftwaresolutions.com). We can run the sixteen machine-checkable rows against your public URLs and send back each verdict with the URL we fetched, what came back, and the citation beside it.

## Thank you for your time

If you have any questions or want to connect on anything that I wrote about above, please [email me](mailto:cooper@veyseysoftwaresolutions.com) or [book some time on my calendar](https://calendar.google.com/calendar/appointments/schedules/AcZssZ041TFFhKz0LzQydfOipmZ3l8K-22DadOWnsRMPnLkS8TjaDIreyZ8fFxAimnDxsCeimFSvnY_3). Any and all feedback is of course so appreciated.


How we continuously monitor SEO + GEO health across 27 checks

On the afternoon of August 21, 2026, [**Vercel** announced "Is Agentic"](https://x.com/vercel_dev/status/2090857388924661915), a free public tool at [is-agentic.com](https://is-agentic.com/) that grades a website on how easily AI agents can work with it. The scanning and scoring behind it belong to [Ora](https://ora.ai/blog/is-agentic-with-vercel), an agent-experience research company whose fuller ranker scores four separate layers, from discovery and access to usability and payments. Reports are readable in the browser, through a command-line tool, through a read-only JSON API, and through an MCP server.

## What the score measures, in plain English

[The scoring page](https://is-agentic.com) describes its subject as "the public parts of a website that AI agents can discover, retrieve, understand, and use." [Two tiers of checks](https://is-agentic.com/) produce the number:

- **Essential checks** are where the bulk of the points live. They cover what the page calls "fundamentals such as server-rendered content, correct HTTP behavior, clear document structure, recoverable errors, and usable controls."
- **Recommended checks** switch on only when the scan finds evidence of "an API, OAuth flow, GraphQL endpoint, MCP server, developer portal, or commerce surface." A check that does not apply is dropped from the calculation rather than marked failed, so a content site loses no points for lacking a checkout or a developer portal.

Reports also attach what Is Agentic calls an "observed agent journey," a recorded attempt by a single agent to use the site, along with the friction it ran into. That recording sits outside the number, since a single task cannot stand in for every agent or every goal, and it is often the most legible evidence in the report.

The reason this class of measurement exists at all: assistants and coding agents fetch pages the way crawlers do. They read the raw HTML response, and most do not execute JavaScript. A page whose content only appears after scripts run, or whose error pages only render client-side, is partly invisible to them regardless of how it looks in a browser.

## What we shipped

The announcement landed on Friday afternoon. By Sunday morning, the following changes were live on one of our partner sites. Each of these is measured, current work, not a plan.

**Markdown from the same URL.** Public pages now return Markdown when a client sends the `Accept: text/markdown` request header, at the same URL that serves HTML. The implementation follows the [Accept: text/markdown convention](https://acceptmarkdown.com/): a `Vary: Accept` response header so shared caches keep the two representations separate, a `406` response when a client accepts neither format, and quality-value handling per [RFC 9110's content-negotiation rules](https://www.rfc-editor.org/rfc/rfc9110#section-12.5.1). Pages also advertise a `.md` sibling through `<link rel="alternate" type="text/markdown">`, because some agents follow an advertised link rather than negotiate. Is Agentic's own report pages serve Markdown at the same canonical URL, which is a reasonable signal of where the convention is heading.

**Real 404 documents.** A dead URL returned a not-found page whose visible text, before JavaScript ran, was 27 characters. A complete prerendered 404 document existed the whole time; the routing configuration never let an unmatched path reach it. Dead URLs now serve that full document, with recovery links an agent can read in the initial HTML response. The status code was always correct. The document behind it was not.

**An `llms.txt` file with boundaries.** We added a when-to-use section listing the work the business takes on, and, as deliberately, what it does not do, since a stated boundary routes an agent as usefully as a stated capability. Two honest labels belong on this item: [`llms.txt` is a proposed convention](https://llmstxt.org/), not a standard, and [Google states it does not use `llms.txt`](https://developers.google.com/search/docs/fundamentals/ai-optimization-guide) for its AI features. We ship it as low-cost signage for the agents that do read it, and claim nothing more.

**One identity node, not two.** An automated recommendation from a scan suggested adding an `Organization` block to a site whose existing JSON-LD already declared a `LocalBusiness` node. On [the schema.org type hierarchy](https://schema.org/LocalBusiness), `LocalBusiness` is a subtype of `Organization`: the node already is one. Adding a sibling would have created two competing identity nodes for the same business. We kept one node and recorded why, then reported the check itself: on August 21 we sent Vercel the report URL, the affected endpoint, the expected behavior and a curl reproduction, which is what [its contact page asks for](https://is-agentic.com/contact). An automated reply told us that published address is not monitored for product reports, so we cannot say the correction reached anyone. This is the pattern worth generalizing: a scanner's finding is a claim to verify against the primary source, not an instruction to follow.

**And the score moved.** The site scored 74 out of 100 when we started on 21 August, and 87 when we checked again on 24 August.

Two things belong beside any number a scanner produces. It measures what the scanner can observe about a site, which is not the same as whether an agent succeeds at what a visitor actually wanted. And the rubric is young enough that a future scoring change could move this number without the site changing at all — which is why the dashboard below stores the scoring-model version beside every score, rather than the score on its own.

## Putting a third party's score on a client dashboard

We maintain a platform for our partner sites that we will be announcing in the coming weeks, and it keeps a standing checklist for every site on it: robots.txt resolved per crawler token, sitemap health, structured data read rather than counted, Markdown availability. This week that checklist gained a monthly Is Agentic scan, with the score displayed on the same page each partner already reviews.

Three decisions made a third party's number safe to display, and they generalize to any external score:

1. **Store the evidence, not a summary.** Every check in the report is stored verbatim beside the score. A recomputed summary can disagree with itself month to month; the stored report cannot.
2. **Version the rubric, and refuse false comparisons.** Scoring models change. We record the scoring-model version with every run, and when two runs were scored under different models, the dashboard says the model changed instead of displaying a month-over-month difference that would compare two different rubrics.
3. **Render the unknown visibly.** A check status our code does not recognize renders in neutral grey, never in green. A new status vocabulary should arrive visible, not silently pass.

One operational note from building it, measured on August 22: the service keeps its most recent finished report on hand and serves that, and its own guidance tells you to rescan once a fix ships. A monthly job therefore has to request a fresh scan rather than re-read the stored result. A dashboard number that never moves reads as "nothing regressed," which is worse than no number.

## The benefit, without overstating it

Is Agentic states its own limit plainly, telling readers to "use the report as a prioritized technical review, not as a guarantee that every model or browser automation system will complete every task." We would add the equivalent restraint on outcomes. Nothing in this article claims that a higher agent-readiness score produces rankings, traffic, citations, or inclusion in AI answers. [Google's guidance for AI features](https://developers.google.com/search/docs/fundamentals/ai-optimization-guide) directs site owners back to ordinary search fundamentals, and that matches what we see in practice.

Our own server logs support the same caution about Markdown specifically. Most assistant fetches we observe request HTML, and [the convention's agent matrix](https://acceptmarkdown.com/status) currently lists the major consumer assistants as HTML-only, with the Markdown-negotiating agents concentrated in developer tools. We shipped same-URL Markdown because the marginal cost was near zero, since the content already exists as Markdown, and because the interfaces reading it are multiplying. That is optionality, honestly priced, not a traffic projection.

## Review your site's Is Agentic report with someone from the Veysey Software Solutions team

If you have any questions or would like to review your site's Is Agentic report with someone from the Veysey Software Solutions team, please [email us](mailto:cooper@veyseysoftwaresolutions.com) or [book some time on our calendar](https://calendar.google.com/calendar/appointments/schedules/AcZssZ041TFFhKz0LzQydfOipmZ3l8K-22DadOWnsRMPnLkS8TjaDIreyZ8fFxAimnDxsCeimFSvnY_3). 

Vercel's "Is Agentic" score: what it measures, and what we shipped

At 5:04 a.m. Eastern time on August 21, 2026, **Google Search Central** published a [LinkedIn notice about a change to Google's JSON-LD extraction](https://www.linkedin.com/posts/googlesearchcentral_to-bring-our-parser-up-to-json-and-other-activity-7496492350907596801-qK3H). This was a Search Central technical announcement, not an announcement from the Google Search Console product.

Google now applies one pass of HTML unescaping when it extracts JSON-LD. A value that was encoded twice, such as `&amp;amp;`, will no longer be repeatedly decoded until it becomes the intended `&`. Google advised site owners to use standard JSON or Unicode hexadecimal escapes such as `\u0026` instead of relying on repeated HTML decoding. [Google Search Central announced the parser behavior directly](https://www.linkedin.com/posts/googlesearchcentral_to-bring-our-parser-up-to-json-and-other-activity-7496492350907596801-qK3H).

## What this means in plain English

JSON-LD is a JSON-based format for linked data, commonly embedded in a page inside a `<script type="application/ld+json">` element. For Google Search features, Google recommends JSON-LD in most cases because it keeps machine-readable data separate from visible HTML and is easier to manage across larger sites. [Google explains its supported formats and JSON-LD recommendation](https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data#supported-formats), while the [W3C JSON-LD recommendation defines the format](https://www.w3.org/TR/json-ld11/).

Suppose the intended organization name is **Research & Development**. These JSON strings preserve that text:

```json
"name": "Research & Development"
```

```json
"name": "Research \u0026 Development"
```

An ampersand may appear directly in a JSON string, and a Unicode escape may represent it as `\u0026`; JSON requires quotation marks, reverse solidus characters, and control characters to be escaped, not ampersands. [Section 7 of RFC 8259 defines JSON string and Unicode escape rules](https://www.rfc-editor.org/rfc/rfc8259#section-7).

This version is the risk Google called out:

```json
"name": "Research &amp;amp; Development"
```

After one HTML-unescape pass, `&amp;amp;` becomes `&amp;`, not the intended ampersand. A site that depended on multiple decoding passes can therefore send Google the wrong text even when a browser page looks normal. [Google's notice identifies double-encoded named and numeric character references as affected patterns](https://www.linkedin.com/posts/googlesearchcentral_to-bring-our-parser-up-to-json-and-other-activity-7496492350907596801-qK3H).

## What we audited today

We audited every sitemap-discoverable URL across different partner sites where we maintain or review structured data. Every URL in the audit, more than a thousand in total, returned HTTP 200; hundreds of pages contained more than a thousand JSON-LD blocks. We found zero nested HTML entities matching the cases in Google's announcement.

The check performed five steps:

1. Read each site's public sitemap, including child sitemap files.
2. Fetch the HTML returned for every advertised URL.
3. Extract every `application/ld+json` script.
4. Parse each block with a standards-based JSON parser.
5. Inspect every nested string value for double-escaped named and numeric HTML entities.

Those steps test both code and content. That distinction matters on content-managed sites because a template can be correct while an editor-entered value introduces an encoding problem later. Google likewise recommends testing structured data during development and monitoring it after deployment because templating or serving issues can break it later. [Google's structured-data introduction describes that development and post-deployment validation workflow](https://developers.google.com/search/docs/appearance/structured-data/intro-structured-data#structured-data-guidelines).

The crawl also surfaced a small number of ordinary JSON parsing failures unrelated to nested HTML entities. We separated those into remediation work rather than describing this scan as a universal structured-data all-clear.

We also added the same check as a local, on-demand regression script. It is not a continuous GitHub Actions workflow and uses zero GitHub Actions minutes; it can be rerun after code or content changes when a structured-data release warrants a full crawl.

## Examples of JSON-LD we implement

The markup has to describe the content readers can actually find on that URL. Google requires it to represent the page's main content accurately rather than introduce information hidden from readers. [Google's general structured-data guidelines define those quality requirements](https://developers.google.com/search/docs/appearance/structured-data/sd-policies#quality-guidelines).

### Local businesses

For multi-location businesses, we have implemented location-specific `LocalBusiness` objects on individual location pages, using supported facts such as the canonical location URL, address, hours, geographic coordinates, and price range. Google documents these properties for local business pages and may use them in business details shown in Search or Maps; complete and accurate data remains more important than filling every optional field. [Google's LocalBusiness documentation lists the supported business details](https://developers.google.com/search/docs/appearance/structured-data/local-business).

### Products, offers, and product ratings

For ecommerce pages, we have implemented `Product` and `Offer`, adding `AggregateRating` only when the rating is specific to that product and visible on the page. Google says product structured data can make a page eligible for richer appearances that include price, availability, review ratings, shipping information, and other product details. [Google's Product documentation describes those search appearances](https://developers.google.com/search/docs/appearance/structured-data/product), and [Google's review guidelines require marked-up ratings to be visible and tied to a specific item](https://developers.google.com/search/docs/appearance/structured-data/review-snippet#technical-guidelines).

### Organizations

We have implemented `Organization` data using the official name, URL, logo, and verified public profile relationships rather than blank or speculative values. Google says Organization markup can help it understand and disambiguate an organization, and that properties such as `logo` can influence visual elements in Search results and knowledge panels. [Google's Organization documentation explains those uses](https://developers.google.com/search/docs/appearance/structured-data/organization).

### Articles and breadcrumbs

On publication and professional-services sites, we have implemented `Article` or `BlogPosting` data for the headline, author, date, and representative image, plus `BreadcrumbList` data for the page's position in the site hierarchy. Google says Article markup can help it understand article details and show better title, image, and date information, while breadcrumb markup helps Google categorize a page in search results. [Google documents Article structured data](https://developers.google.com/search/docs/appearance/structured-data/article) and [Breadcrumb structured data](https://developers.google.com/search/docs/appearance/structured-data/breadcrumb).

## The benefit, without overstating it

Correct structured data can clarify a page's meaning for Google and establish eligibility for supported enhanced search features. Eligibility is not a promise that a feature will appear; if Google applies a structured-data manual action, the consequence concerns rich-result eligibility rather than the page's ordinary web-search ranking. [Google states both limitations in its general structured-data guidelines](https://developers.google.com/search/docs/appearance/structured-data/sd-policies).

The same restraint applies to claims about AI-generated search answers. For AI Overviews and AI Mode, Google directs site owners back to ordinary Search requirements; it does not prescribe an AI-specific Schema.org vocabulary. Structured data should still agree with the visible page. [Google's guidance for AI features states those requirements directly](https://developers.google.com/search/docs/appearance/ai-features#seo-best-practices).

Our recommendation is therefore narrow: produce valid JSON-LD from accurate, visible content; use supported types where they describe the page; and test the rendered output whenever templates or content systems can change it. Today's parser change did not require a live rewrite across the audited partner sites, but it justified a complete rendered-site check and a repeatable regression tool.

## Request a JSON-LD findings report

Want a clearer picture of the JSON-LD your site is serving? [Let us know](mailto:cooper@veyseysoftwaresolutions.com). We can crawl the public URLs listed in your sitemaps, parse the JSON-LD returned in the page HTML, and share a concise report of parsing failures, encoding problems, conflicting markup, and prioritized next steps.

## Thank you for your time

If you have any questions or want to connect on anything that I wrote about above, please [email me](mailto:cooper@veyseysoftwaresolutions.com) or [book some time on my calendar](https://calendar.google.com/calendar/appointments/schedules/AcZssZ041TFFhKz0LzQydfOipmZ3l8K-22DadOWnsRMPnLkS8TjaDIreyZ8fFxAimnDxsCeimFSvnY_3). Any and all feedback is of course so appreciated.

Google's JSON-LD parser changed: what we checked

## Introduction

Last week I shared some reflections from my first few weeks using agentic coding tools [(link)](/blogs/agentic-systems-first-impressions?utm_source=coding_agent_notifications&utm_medium=blog_link). Today I am going to share some details about a problem that I encountered repeatedly while using these tools, as well as a solution that I built to address that problem.

## What prompted the creation of this tool?

You assign a coding agent a long-running task and switch to Slack to answer some messages. You start and finish a huddle and eventually return to your IDE to find that the coding agent has been sitting idle for several minutes, because it requires your direction on a task or authorization to use a tool. If you had known that the agent required your input, then you would have provided the required input quickly and had the agent back to work several minutes prior. Instead material time and output have been lost to unnecessary latency.

When Claude Code requires input from a human, by default that is communicated exclusively via Claude Code outputting a message to the terminal. It is not otherwise announced that Claude Code requires feedback in order to continue its work. No sound plays, no visual notification displays outside of what is displayed via the terminal.

That means that if a human is not actively monitoring their terminal, there is a delta between when an agent requires feedback and when a human becomes aware of that. Repeated dozens of times across a workday, the cumulative cost of these idle intervals adds up. A few minutes here and there compounds into hours of idle agent time, which can prove extremely expensive as measured by throughput.

This was happening to me often enough that I was compelled to explore how other folks had accounted for this. The consequences felt significant enough that I was sure that a quality solution to the problem must exist already. I was excited to find and start benefitting from that solution.

I validated that I was not the only person to experience this problem, for example [this Reddit thread](https://www.reddit.com/r/ClaudeAI/comments/1ll8f3d/can_claude_code_play_a_sound_when_it_requires_an/) is dedicated to the problem and features comments like:

> Sometimes (Claude Code) works for a couple of minutes and I don't want to stare at the screen waiting for it to (stop) inevitably and ask me something to approve etc. Is there a way to play a sound when it requires my attention?

> Getting a sound alert when Claude Code needs your approval would totally keep you from staring at the screen all day waiting for the next prompt.

I learned that several coding agent alert solutions exist, but none were exactly what I was looking for. Some require manual JSON editing. Others offer audio alerts but not visual notifications, which is not viable for folks like myself that are frequently on calls and often need to silence their audio notifications. So I decided to build what I was looking for.

## What does the tool do?

- Our tool helps ensure that you are alerted as early as possible whenever a coding agent might benefit from your input. It does this via customizable audio and visual notifications that are triggered by different coding agent events.
- Our tool frees you from having to actively monitor whether or not a coding agent requires your input.

## How does it work?

Our solution takes advantage of Claude Code hooks to deliver notifications when designated events occur. Anthropic defines these hooks as "user-defined shell commands that execute at various points in Claude Code's lifecycle. Hooks provide deterministic control over Claude Code's behavior, ensuring certain actions always happen rather than relying on the LLM to choose to run them" ([source](https://docs.claude.com/en/docs/claude-code/hooks-guide)).

**Hook events that our solution currently accounts for**

- [Notification](https://docs.claude.com/en/docs/claude-code/hooks-guide#hook-events-overview) – Runs when Claude Code sends notifications.
- [Stop](https://docs.claude.com/en/docs/claude-code/hooks-guide#hook-events-overview) – Runs when Claude Code finishes responding.
- [PreToolUse](https://docs.claude.com/en/docs/claude-code/hooks-guide#hook-events-overview) – Runs before tool calls (permission prompts).
- [PostToolUse](https://docs.claude.com/en/docs/claude-code/hooks-guide#hook-events-overview) – Runs after tool calls complete.
- [SubagentStop](https://docs.claude.com/en/docs/claude-code/hooks-guide#hook-events-overview) – Runs when subagent tasks complete.

**Some details on what happens when our app is installed**

Script files are added to ~/.claude/scripts/:

- smart-notify.sh — Parses hook event metadata, invokes afplay for audio playback and
  terminal-notifier for macOS notifications.
- select-sound.sh — Determines which audio file to play based on event type and project.
- read-config.sh — Loads notification preferences from audio-notifier.yaml, and exports
  them as shell environment variables.
- audio-notifier-uninstall.sh — Removes hooks from settings.json, deletes installed
  files, creates a timestamped settings.json backup.

The below files are added to ~/.claude/:

- audio-notifier.yaml — Configuration file storing event-specific notification settings,
  audio file paths, and Focus mode behavior.
- .sounds-enabled — Flag file checked by smart-notify.sh to enable/disable notifications.
- voices/global/\*.mp3 — Pre-generated audio files for default notification sounds.
- terminal-notifier.app — macOS utility for delivering native system notifications via
  NSUserNotificationCenter.

## Customization options

We created a <a href="https://v2.tauri.app/" target="_blank" class="inline-link">Tauri</a> app to make it as easy as possible for users to customize their coding agent notifications experience. Via our GUI:

- Visual notifications can be turned on and off on a per event basis.
- Audio alerts can be turned on and off on a per event basis.
- You can arrange for different audio to play on a per event basis.
- You can upload custom audio for your alerts.
- You can customize how [Apple "Focuses"](https://support.apple.com/guide/mac-help/change-focus-settings-mchlff5da36d/mac) (Do Not Disturb, Work, Sleep) affect your notifications.

These settings are stored locally in YAML and can be modified either through the GUI or directly via the config file.

## Video that showcases the tool

<div style="position: relative; padding-bottom: 56.25%; height: 0; overflow: hidden; max-width: 100%; margin: 20px 0;">
  <iframe
    style="position: absolute; top: 0; left: 0; width: 100%; height: 100%;"
    src="https://www.youtube.com/embed/KT1uzZtKu-8?si=N0HREHVEeP74QAKi&vq=hd2160&quality=highres"
    title="Coding Agent Notifications Demo - Visual and Audio Alerts for Claude Code"
    frameborder="0"
    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
    referrerpolicy="strict-origin-when-cross-origin"
    allowfullscreen
    loading="lazy">
  </iframe>
</div>

## Installation

It takes less than 60 seconds to install and start using our solution. The app needs to be opened once in order to enable notifications, after that the app does not need to be running in order for notifications to fire. Claude Code sessions that were started before the application is run for the first time will need to be restarted, due to the fact that ~/.claude/settings.json is read once at session startup.

<p style="margin-top: 20px; margin-bottom: 8px;"><strong>Option 1</strong></p>

<style>
.download-button {
  display: inline-flex !important;
  align-items: center !important;
  justify-content: center !important;
  gap: 6px !important;
  padding: 14px 28px !important;
  background-color: #000000 !important;
  color: #ffffff !important;
  text-decoration: none !important;
  border-radius: 12px !important;
  font-weight: 600 !important;
  font-size: 16px !important;
  transition: all 0.2s ease !important;
  margin: 20px 0 !important;
  border: 1px solid #000000 !important;
  line-height: 1.5 !important;
}

.download-button:hover {
  background-color: #1a1a1a !important;
  color: #ffffff !important;
  transform: translateY(-1px) !important;
  box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15) !important;
  text-decoration: none !important;
}

.download-button:visited {
  color: #ffffff !important;
}

.download-button svg {
  flex-shrink: 0 !important;
  margin-left: -2px !important;
}
</style>

<a href="https://github.com/cwveysey/coding-agent-notifications-releases/releases/latest/download/Coding-Agent-Notifications-Installer.dmg" class="download-button" target="_blank" rel="noopener noreferrer">
  <svg width="20" height="20" viewBox="0 0 20 20" fill="currentColor" xmlns="http://www.w3.org/2000/svg" aria-label="Download">
    <path d="M2.66821 12.6663V12.5003C2.66821 12.1331 2.96598 11.8353 3.33325 11.8353C3.70052 11.8353 3.99829 12.1331 3.99829 12.5003V12.6663C3.99829 13.3772 3.9992 13.8707 4.03052 14.2542C4.0612 14.6298 4.11803 14.8413 4.19849 14.9993L4.2688 15.1263C4.44511 15.4137 4.69813 15.6481 5.00024 15.8021L5.13013 15.8577C5.2739 15.9092 5.46341 15.947 5.74536 15.97C6.12888 16.0014 6.62221 16.0013 7.33325 16.0013H12.6663C13.3771 16.0013 13.8707 16.0014 14.2542 15.97C14.6295 15.9394 14.8413 15.8825 14.9993 15.8021L15.1262 15.7308C15.4136 15.5545 15.6481 15.3014 15.802 14.9993L15.8577 14.8695C15.9091 14.7257 15.9469 14.536 15.97 14.2542C16.0013 13.8707 16.0012 13.3772 16.0012 12.6663V12.5003C16.0012 12.1332 16.2991 11.8355 16.6663 11.8353C17.0335 11.8353 17.3313 12.1331 17.3313 12.5003V12.6663C17.3313 13.3553 17.3319 13.9124 17.2952 14.3626C17.2624 14.7636 17.1974 15.1247 17.053 15.4613L16.9866 15.6038C16.7211 16.1248 16.3172 16.5605 15.8215 16.8646L15.6038 16.9866C15.227 17.1786 14.8206 17.2578 14.3625 17.2952C13.9123 17.332 13.3553 17.3314 12.6663 17.3314H7.33325C6.64416 17.3314 6.0872 17.332 5.63696 17.2952C5.23642 17.2625 4.87552 17.1982 4.53931 17.054L4.39673 16.9866C3.87561 16.7211 3.43911 16.3174 3.13501 15.8216L3.01294 15.6038C2.82097 15.2271 2.74177 14.8206 2.70435 14.3626C2.66758 13.9124 2.66821 13.3553 2.66821 12.6663ZM9.33521 3.33333C9.33521 2.96606 9.63298 2.66829 10.0002 2.66829C10.3674 2.66847 10.6653 2.96617 10.6653 3.33333V10.8939L12.8625 8.69661L12.967 8.61165C13.2252 8.44092 13.5766 8.46925 13.804 8.69661C14.0633 8.95628 14.0634 9.37744 13.804 9.63704L10.47 12.97C10.3453 13.0947 10.1765 13.1653 10.0002 13.1654C9.82388 13.1654 9.65425 13.0948 9.52954 12.97L6.19653 9.63704L6.11157 9.53255C5.94101 9.27441 5.96924 8.9239 6.19653 8.69661C6.42382 8.46932 6.77433 8.44109 7.03247 8.61165L7.13696 8.69661L9.33521 10.8949V3.33333Z"></path>
  </svg>
  Download for macOS
</a>

<p style="margin-top: 20px; margin-bottom: 8px;"><strong>Option 2 (terminal)</strong></p>

<div class="minimal-code-block">
  <code>curl -fsSL https://cooperveysey.com/install.sh | bash</code>
  <button class="minimal-copy-btn" onclick="copyCommand('curl -fsSL https://cooperveysey.com/install.sh | bash', this)" aria-label="Copy to clipboard">
    <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-linecap="round" stroke-linejoin="round" stroke-width="1.5">
      <rect width="13" height="13" x="9" y="9" rx="2" ry="2"/>
      <path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"/>
    </svg>
  </button>
</div>

<style>
.minimal-code-block {
  display: flex;
  align-items: center;
  gap: 8px;
  margin: 16px 0;
  padding: 10px 12px;
  background: #fafafa;
  border-radius: 4px;
  font-family: 'SF Mono', 'Monaco', 'Consolas', monospace;
}

.minimal-code-block code {
  flex: 1;
  font-size: 13px;
  color: #24292f;
  background: transparent;
  padding: 0;
}

.minimal-copy-btn {
  background: transparent;
  border: none;
  cursor: pointer;
  padding: 4px;
  display: flex;
  align-items: center;
  opacity: 0.6;
  transition: opacity 0.2s;
}

.minimal-copy-btn:hover {
  opacity: 1;
}

.minimal-copy-btn svg {
  color: #57606a;
}
</style>

<script>
function copyCommand(text, button) {
  navigator.clipboard.writeText(text);
  const originalHTML = button.innerHTML;
  button.innerHTML = '<svg width="18" height="18" viewBox="0 0 16 16" fill="none"><path d="M13.78 4.22a.75.75 0 010 1.06l-7.25 7.25a.75.75 0 01-1.06 0L2.22 9.28a.75.75 0 011.06-1.06L6 10.94l6.72-6.72a.75.75 0 011.06 0z" fill="#57606a"/></svg>';
  setTimeout(() => {
    button.innerHTML = originalHTML;
  }, 2000);
}
</script>

## Accounting for [Apple Focuses](https://support.apple.com/guide/mac-help/change-focus-settings-mchlff5da36d/mac)

Our visual notifications are delivered via <a href="https://github.com/julienXX/terminal-notifier" target="_blank" class="inline-link">terminal-notifier</a>, a utility for delivering native macOS notifications. By default, visual notifications are suppressed if a Focus (Do Not Disturb, Work, Sleep) is active. Follow the instructions below if you would like for our visual notifications to display regardless of whether a Focus is active.

- <a href="x-apple.systempreferences:com.apple.Focus-Settings.extension" class="inline-link">Open Focus Settings</a>.
- Select the Focus that you want to allow coding agent visual notifications for.
- From there select **Allowed Apps** from the **Allow Notifications** section, and add terminal-notifier to that list.

## Future plans for the tool

If other folks wind up using the tool and have feedback, I will definitely consider accounting for said feedback. I am also open to open-sourcing the tool.

I hope that the tool proves useful to at least a few other individuals — that would bring me a ton of joy. I enjoyed building this and learned a lot in the process. I continue to be amazed by what these coding agent tools can do.

**Backlog**

- Support for additional coding agents
- Support for Linux
- Allow for per-project customization
- Notification badges, subtle dock indicators
- Slack notifications, phone notifications (e.g. see https://justin.searls.co/posts/notify-your-iphone-or-watch-when-claude-code-finishes)
- "Smart" escalation rules (e.g. if no response after x time, ping y other channel)

## Thank you for your time

If you have any questions or want to connect on anything that I wrote about above, please [email me](mailto:cooper@veyseysoftwaresolutions.com) or [book some time on my calendar](https://calendar.google.com/calendar/appointments/schedules/AcZssZ041TFFhKz0LzQydfOipmZ3l8K-22DadOWnsRMPnLkS8TjaDIreyZ8fFxAimnDxsCeimFSvnY_3). Any and all feedback is of course so appreciated.

Receive notifications for designated coding agent events

> Like any truly disruptive technology, AI agents have the power to reshuffle the deck. Done right, they offer laggards a leapfrog opportunity to rewire their competitiveness. Done wrong—or not at all—they risk accelerating the decline of today's market leaders. This is a moment of strategic divergence. While the technology will continue to evolve, it is already mature enough to drive real, transformative change across industries.
>
> — [McKinsey, "Seizing the agentic AI advantage" (June 2025)](https://www.mckinsey.com/capabilities/quantumblack/our-insights/seizing-the-agentic-ai-advantage)

## What prompted me to start using agentic coding tools?

Earlier this month I attended a [Claude Code](https://docs.claude.com/en/docs/claude-code/overview) workshop that [Alex Lieberman](https://x.com/businessbarista) and [Arman Hezarkhani](https://x.com/ArmanHezarkhani) from [Tenex](https://www.tenex.co) hosted. Prior to that event I had never actually experimented with Claude Code or watched a formal demo of the tool, despite having seen it mentioned countless times online. What I observed during the workshop motivated me to install Claude Code and explore its capabilities firsthand. I was particularly interested in how my coding efforts might benefit from:

- Parallelism (agents working through separate tasks concurrently, as opposed to an individual task being worked through one step at a time via a chat interface like ChatGPT)
- Autonomous task completion (e.g. via sharing requirements with an AI, having the AI write tests in accordance with said requirements, having the AI execute the aforementioned tests and iterate on implementation approaches until all relevant tests are passing)
- Providing AI with expanded codebase context (providing an AI access to, except where I restrict things purposefully, an entire project rather than just snippets that I manually paste into a chat interface)
- Providing AI with a wider range of tooling capabilities, as compared to what it has access to via a traditional chat interface (empowering the AI to execute scripts, capture and analyze screenshots across window sizes, directly edit files, create and analyze browser logs, download and make use of designated third-party packages, take advantage of [MCPs](https://www.anthropic.com/news/model-context-protocol), etc)

I assigned Claude Code some tasks and put it to work on some portfolio site improvements that I had been meaning to get around to. I was stunned by

- How significantly the aforementioned agentic capabilities impacted my development experience.
- Claude Code's performance as measured by task completion quality and speed.

Claude Code has since become a tool that I use every day, and agentic AI systems have dramatically changed my approach to writing code, building software, and to completing work more generally.

## What exactly are AI agents—and what does "agentic AI" mean?

I'll include some relevant definitions from OpenAI and Google below. [OpenAI defines agents as](https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf):

> systems that independently accomplish tasks on your behalf. A workflow is a sequence of steps that must be executed to meet the user's goal, whether that's resolving a customer service issue, booking a restaurant reservation, committing a code change, or generating a report.
>
> ...an agent possesses core characteristics that allow it to act reliably and consistently on behalf of a user:
>
> 1. It leverages an LLM to manage workflow execution and make decisions. It recognizes when a workflow is complete and can proactively correct its actions if needed. In case of failure, it can halt execution and transfer control back to the user.
> 2. It has access to various tools to interact with external systems—both to gather context and to take actions—and dynamically selects the appropriate tools depending on the workflow's current state, always operating within clearly defined guardrails.

[Google defines agentic AI as](https://cloud.google.com/discover/what-is-agentic-ai?hl=en):

> an advanced form of artificial intelligence focused on autonomous decision-making and action. Unlike traditional AI, which primarily responds to commands or analyzes data, agentic AI can set goals, plan, and execute tasks with minimal human intervention. This emerging technology has the potential to revolutionize various industries by automating complex processes and optimizing workflows.

## How does using agents differ from using ChatGPT?

Let's use an analogy to illustrate the difference.

**Baking a cake without agentic tools**

- A human describes their desired result to ChatGPT. ChatGPT replies with step-by-step instructions that the human is responsible for executing. The human procures the ingredients, preps the kitchen, completes all required measuring and mixing, operates the oven and oversees the baking process, and completes all required cleanup.
- If something goes wrong, the human describes the problem to ChatGPT, ChatGPT provides feedback, and the human takes action in accordance with said feedback.

In this example, AI (ChatGPT as a traditional AI chat interface) serves as a guide and provides direction - a human is required to execute the AI's instructions. In this example the AI is reactive (it replies to prompts), it does not actively monitor progress and it generates text as its output.

**Baking a cake with agentic tools**

- A human shares their desired result with an agent, and provides the agent with access to all relevant tools (e.g. car keys for driving to the store, a credit card for purchasing groceries, an alarm code for entering the house). The agent takes things from there and identifies viable recipes, procures the ingredients, and completes all required prep, baking, and cleanup.
- Before the agent presents the finished cake to the human, it validates that the cake meets the success criteria that were defined by the human. If during its testing the agent identifies any issues, at that point the agent will iterate until the cake satisfies all success criteria.

In this example, AI completes work autonomously, it actively monitors progress and makes adjustments as needed, and generates a cake that meets the specified success criteria as its output.

## The Goldman Sachs CIO on their first experience using "one of the new agentic coding tools"

> Early models, for all their fluency, felt like they were operating with a kind of impostor syndrome, optimized for credibility over correctness. But then came the inflection point, a step-change brought about by a new class of "reasoning" models. Suddenly, we weren't just conversing with a statistical machine that predicted the next word in a sequence;
> we were getting a peek into a nascent form of cognition.
>
> The first time I experimented with one of the new agentic coding tools, I felt that familiar spark of magic. I tasked it with a personal project I'd never found the time for: migrating a charity website from a simple web builder to a proper, modern CI/CD environment. For the next twenty minutes, it went to work, asking clarifying questions, requesting credentials, and providing status updates. It felt less like using a tool and more like collaborating with a junior developer. When it presented me with a fully deployable package, complete with impeccable documentation and unit tests, I was floored.
>
> Of course, it wasn't perfect. It made mistakes. It got stuck. It required my supervision and, crucially, my judgment to steer it back on course. The experience drove home a lesson I've learned the hard way over a long career: you cannot afford to trust blindly. Yet, the process was fascinating. Peeking into its "chain of thought" was like watching a mind at work—messy, non-linear, full of starts, stops, and self-corrections, not unlike our own human reasoning. It wasn't a straight line; it was a random walk toward a solution. Here was the kernel of something new: not just an intelligence that could generate content, but one that could generate a plan.
>
> This is the promise of agentic frameworks. It's the difference between a static subway map and a dynamic GPS that reroutes you in real-time. A classic rules-based automaton follows a fixed path; when it encounters an unexpected obstacle, it breaks. An AI agent, powered by a reasoning model, has the potential to observe, adapt, and find another way. It possesses a form of digital common sense that allows it to navigate the countless edge cases of reality. It represents a shift from simply telling a computer "what" to do, to explaining "why" we need something done and trusting it to figure out the "how."
>
> — Marco Argenti, CIO of Goldman Sachs, excerpted from Antonio Gullí's ["Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems"](https://docs.google.com/document/d/1PWhaXD_UNKgJaxYe3JBxRFRt3_B8Wm67CFxtSBQ4LkU/edit?tab=t.0)

## Further reading

Because there is broad consensus that agentic AI qualifies as a paradigm shift and a historically significant innovation, fortunately there is no shortage of high-quality agentic systems-related learning material for anyone looking to get up to speed. I'll share some resources below for both engineers and non-engineers.

**An accessible introduction to Claude Code and agentic coding for non-engineers**

I thought that [this "Claude Code for Non-Coders | A Complete Beginner's Guide" YouTube tutorial](https://www.youtube.com/watch?v=S5ULIoThOIM) was notably well done (topics covered, pace, structure, thoroughness, ease of understanding, audio quality). It walks viewers through installing and setting up the software that is required to take advantage of Claude Code (e.g. an IDE like Cursor, Node.js), showcases how to interact with Claude Code using natural language to facilitate your desired result, and demonstrates how to "save progress" to GitHub and deploy an application to publicly accessible URLs via Vercel.

If while watching the tutorial you find yourself confused by any terms or concepts, try sharing a description of what you find confusing with ChatGPT or another LLM. Also please feel free to [drop me a line](mailto:cooper@veyseysoftwaresolutions.com).

**Agentic coding resources for folks that have an engineering background**

- I would recommend experimenting with Claude Code if you have not done so already. [Here](https://docs.claude.com/en/docs/claude-code/overview) is a link to the Claude Code developer documentation. And [here](https://anthropic.skilljar.com/claude-code-in-action) is a link to a free course that Anthropic put together, that "provides comprehensive training on using Claude Code for software development tasks, covering the underlying architecture of AI coding assistants, practical implementation techniques, and advanced integration strategies."

  For reference, Anthropic is the company that created and maintains Claude Code. In September 2025 Anthropic announced a $13B raise at $183B post-money valuation. Anthropic is considered one of the leading AI labs, as measured by e.g. model performance and product innovation.

- I haven't completed [this "Agentic AI" course](https://www.deeplearning.ai/courses/agentic-ai/) but decided to share it here anyways. It was created by [Andrew Ng](https://www.linkedin.com/in/andrewyng). Andrew founded Coursera (NYSE: COUR), was the Director of the Stanford AI Lab, and started the Google Brain team. The course is intended to provide "a fundamental understanding and practical knowledge to develop production-ready agentic applications, from design patterns to deployment and evaluation."

**Some agent-focused pages from leading tech companies**

[Amazon](https://aws.amazon.com/blogs/machine-learning/enabling-customers-to-deliver-production-ready-ai-agents-at-scale), [Google](https://google.github.io/adk-docs/), [IBM](https://www.ibm.com/think/topics/ai-agents), [Meta](https://metait.ai/agentic-ai/), [Microsoft](https://learn.microsoft.com/en-us/shows/ai-agents-for-beginners/), [NVIDIA](https://blogs.nvidia.com/blog/what-is-agentic-ai/), [OpenAI](https://academy.openai.com/public/clubs/builders-etkn1/videos/unlock-agentic-power-with-the-agents-sdk), [Oracle](https://www.oracle.com/artificial-intelligence/generative-ai/agents/), [Salesforce](https://www.salesforce.com/agentforce/ai-agent-course/), [Snowflake](https://www.snowflake.com/resource/a-practical-guide-to-ai-agents)

**Additional resources**

- The print version of "Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems" by Antonio Gullí is scheduled for release in December 2025. [Here](https://www.amazon.com/Agentic-Design-Patterns-Hands-Intelligent/dp/3032014018) is an Amazon link for the print version, and [here](https://docs.google.com/document/d/1rsaK53T3Lg5KoGwvf8ukOUvbELRtH-V0LnOIFDxBryE/preview) is a link for a free digital version that is available now.

  The book "provides the essential knowledge and practical examples to not only understand the 'what' and 'why' of agentic systems, but also the 'how'" ([quote from Google VP Saurabh Tiwary](https://docs.google.com/document/d/18Q9kfZuCTL37ztrSjLxwf8Elr5UfAiAavmnj0IqSpbU/edit?tab=t.0)). Gullí is a "Distinguished Engineer" at Google, he leads strategic initiatives for Google Cloud's "Office of the CTO."

- ["How Agentic AI is Transforming Enterprise Platforms"](https://www.bcg.com/publications/2025/how-agentic-ai-is-transforming-enterprise-platforms) from the Boston Consulting Group (October 2025).

- ["Real AI agents and real work"](https://www.oneusefulthing.org/p/real-ai-agents-and-real-work) by Wharton professor Ethan Mollick (September 2025).

- ["Seizing the Agentic AI Advantage"](https://www.mckinsey.com/capabilities/quantumblack/our-insights/seizing-the-agentic-ai-advantage) from McKinsey (June 2025).

- ["The agentic commerce opportunity: How AI agents are ushering in a new era for consumers and merchants"](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-agentic-commerce-opportunity-how-ai-agents-are-ushering-in-a-new-era-for-consumers-and-merchants) by McKinsey (October 2025).

- "What Is an AI Agent?" is the title and subject of [this May 2025 "What Is an AI Agent?" podcast](https://open.spotify.com/episode/4bdl6uzpdQbaXLFdO8XHMF?si=4454c3ea8bdf4c47) by Andreessen Horowitz partners [Guido Appenzeller](https://x.com/appenz), [Matt Bornstein](https://x.com/BornsteinMatt), and [Yoko Li](https://x.com/stuffyokodraws).

- [r/AI_Agents](https://www.reddit.com/r/AI_Agents) - A subreddit dedicated to AI agents that as of 10/19/2025 has 159k members.

## Thank you for your time

If you have any questions or want to connect on anything that I wrote about above, please [email me](mailto:cooper@veyseysoftwaresolutions.com) or [book some time on my calendar](https://calendar.google.com/calendar/appointments/schedules/AcZssZ041TFFhKz0LzQydfOipmZ3l8K-22DadOWnsRMPnLkS8TjaDIreyZ8fFxAimnDxsCeimFSvnY_3). Any and all feedback is of course so appreciated.

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