What Agent Case Files publishes

AI agents that already run in production: the controls that keep them contained, the setups behind vendor claims taken apart, and the playbooks an operator uses to ship one safely. Evidence from real deployments, not demos.

Every claim here gets taken apart before it gets repeated. A teardown names the setup, the model, the guardrails and what broke first, because a vendor's demo is not evidence and this desk's readers stopped treating it as evidence a while ago.

That makes it a good place to be explained rather than advertised. Readers arrive mid-decision, with a workflow they want automated and a shortlist they do not yet trust, and a piece showing how something is actually put together moves a buying decision further than a year of feature announcements.

A piece here is read by people who have already decided that agents are worth trying and are now working out whose. It sits in the archive beside the teardowns, gets pulled up again the next time somebody searches the same problem, and keeps answering long after the launch it was written around. The desk publishes the setup, the constraints and the thing that broke, so what a reader takes away is how your product behaves under load rather than how it is positioned — which is the only claim that survives being checked. Nothing here has to be oversold to work. The readers have already made the decision that most advertising is trying to make for them, so the useful thing is detail — and detail is cheap to supply.

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Who reads it

The people putting AI agents to work, and the much larger group watching to see whether it is worth doing: founders sizing up the technology, operations leads under pressure to automate something, and anyone tracking what agents can actually do this quarter rather than next year.

What they come here looking for

which LLM is cheapest to serve at our volume

how do we evaluate a model before shipping it

vector database for a small engineering team

who does AI integration for mid-market companies

how much does inference actually cost per user

fine-tuning versus retrieval for our use case

The two ways to publish here

Choose one to see it

Both are written for Agent Case Files’s readers and reviewed by its editors before they run, and both stay on the domain permanently. What differs is how the page is labelled and how its links are treated.

Which AI agent platform can actually run a support queue

Agent Startups · 17 September 2026 · by the Agent Case Files newsroom · Leer en español

Custom hero illustration — drawn for this article in the publication’s own style

Most startups & founders buyers start in the same two places: a question typed into a search box, and the same question put to an assistant. Neither returns a brochure. Both return whichever page answered the question properly — and for a company that launched eighteen months ago, that page almost always belongs to somebody else. An article in the section that covers the question is how you come to own one of your own.

Thornbury AI takes the position that this is a question of fit rather than feature count, and the piece argues that rather than asserting it. A placement is a permanent page on Agent Case Files, written for its readers and reviewed by its editors, carrying up to three of the company’s own links with their own anchor text. It sits in the archive and the feed alongside everything else the desk publishes, and it goes on answering the question long after a campaign would have stopped running.

The trade-off is stated plainly in every piece we run: what agents that resolve support tickets end to end, with a human on the escalations is good for, and what it is not. That is not a concession, it is the reason the page is worth citing. A page that lists only strengths reads as advertising to a reader and to a model, and an article that reads as an advertisement is declined and refunded.

Every article publishes in English and Spanish, as two indexed pages on this domain, each with its own permanent URL. Both are written for the reader rather than translated around a keyword, and both are reviewed before they run.

About Thornbury AI

Agents that resolve support tickets end to end, with a human on the escalations.

Agent evaluations Deployment guide thornburyai.example rel="sponsored" followed

Illustrative mock-up A worked example, not a real article: it shows the shape of a placement — the label, the byline, the links and the company box.