Agentailor

For agents

Point your AI assistant at everything Agentailor knows.

The four paths, 38+ articles, and the OSS repos are all machine-readable. Copy a prompt below into ChatGPT, Claude, or your own agent — and let it reason over the whole thing.

Try this first

learn to build agents
I'm a developer 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 and open-source repos. Order the readings from fundamentals to production, and after each week tell me exactly what to build to practice it.

More to try

Each one puts your assistant to work across the whole corpus — not just one page. Copy, paste, tweak for your situation.

Decide my path
Read https://agentailor.com/llms.txt and Agentailor's four path pages (build-yourself, coding-agent, open-source, managed). I'm a solo founder who wants to ship fast but keep control of the architecture. Walk me through the four ways to build an agent, then recommend one for my situation with the trade-offs I'd be accepting.
Compare & pick a stack
Using the Agentailor blog (https://blog.agentailor.com/llms-full.txt), compare the AI agent frameworks it covers and recommend a stack for a TypeScript team building a production, MCP-based agent. Cite the specific articles you drew each point from.
Zero to shipped this weekend
Fetch Agentailor's agent roadmap (https://blog.agentailor.com/posts/agent-development-roadmap.md) and its MCP guides from https://blog.agentailor.com/llms.txt. Give me a concrete step-by-step plan to build and deploy my first MCP server this weekend using their create-mcp-server scaffold, linking each step to the relevant article.
Brief me like a senior engineer
Read Agentailor's articles on agent evaluation, observability, and standards (find them in https://blog.agentailor.com/llms.txt). Brief me the way a senior engineer would brief a new hire: what actually matters for production agents in 2026, what's hype, and where to go deep.

How it works

Those prompts work because everything is published in a form an agent can read:

llms.txt
An index of what to read — hand the URL to your assistant and let it choose. One for the site, one for the blog:
https://agentailor.com/llms.txt
https://blog.agentailor.com/llms.txt
llms-full.txt
The whole blog in one file, for tools that load everything into context at once:
https://blog.agentailor.com/llms-full.txt
add .md to any URL
Every page and post has a clean, HTML-free Markdown twin — append .md to any URL (works on the blog and this site).
fetch any page as markdown
curl https://blog.agentailor.com/posts/agent-development-roadmap.md

Coming next

Pasting URLs works today. Next, to wire your assistant to Agentailor directly:

  • soonMCP server — connect your assistant to Agentailor over the Model Context Protocol, no copy-pasting.
  • soonSkills — drop-in capabilities your agent can load to work with Agentailor content.

//Building the agent that would use all this? Start with what an agent is and the four paths.