Code-first guides for developers — the agent loop, MCP, frameworks, and the four ways to build. Working code in every post, no hype.
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All reports →Not as a straight swap. As a confident first pass in front of Sonnet, it matches Sonnet's accuracy at about a sixth of the cost.
New here? Start with these
01 · Not sure which way to build?
The right approach isn't about what sounds most sophisticated. It's about where ownership should sit: who builds, who runs, who decides. Answer three questions and Agentailor stitches a recommendation across the four ways to build.
Find your path02 · Ready to read? Start with the complete guide
An agent trace holds every prompt, tool argument and tool result, which makes it the most sensitive thing your agent produces. A guide to tracing an agent with OpenTelemetry's GenAI conventions, keeping content out by default, and running the backend yourself, built around a real finance agent.
03 · Know what you want? Browse by topic
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WebMCP lets a web page hand tools to AI agents, and most coverage sells it as your website becoming an API. The spec, WebKit, and even ChatGPT say something the pitch leaves out: an agent cannot verify what a page tool does, and it runs with your users logged-in session. I shipped both sides of WebMCP on Agentailor, red-teamed them, and deleted one. Here is what that taught me.
Add Agent Skills support to your custom AI agent: SKILL.md discovery, progressive disclosure, tool loading, and evals, with TypeScript and Python examples.
An eval harness is four components: the case, the runner, the capture, and the graders. A guide to what each one has to do for an AI agent, built around two real harnesses, so you can decide whether to build one, adopt a framework, or combine both.
Use with AI
Reading through ChatGPT, Claude, or your own agent? Every post is machine-readable via llms.txt, a full-text dump, and a Markdown twin for every URL. Hand it a prompt and let it reason over everything at once.
I'm new to building AI agents. Fetch https://blog.agentailor.com/llms-full.txt — the full text of the Agentailor blog — and design me a structured 4-week curriculum from its articles, fundamentals to production.
Ask Agentailor
Ask Agentailor anything about building AI agents — frameworks, MCP, coding agents, or which path fits — and get a direct answer, grounded in every post on this site. Then run /handoff and it packages what you decided into a build spec your coding agent can act on.
$ ask agentailor "which way should I build my agent?"
→ answers grounded in every post here.
$ /handoff
→ # Build spec — MCP server, TypeScript
→ ## Decisions — SDK v2, JWT via headers
→ ## Not verified — RLS policies untested
paste into Claude Code, Cursor, whatever builds.