Code-first guides for developers — the agent loop, MCP, frameworks, and the four ways to build. Working code in every post, no hype.
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
Is LangChain worth it in 2026? A practitioner's guide to the v1 stack: create_agent, middleware, deploying, observing, and MCP, with an honest verdict.
03 · Know what you want? Browse by topic
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Discover the top open-source AI agent evaluation frameworks in 2026. From DeepEval and promptfoo to agentevals and Strands Evals, see which eval tools to use for trajectory checking, CI gating, red-teaming, and scoring agents in production.
Frontier coding agents are capable but non-deterministic. These are the agent skills for agentic engineering and spec-driven development that keep their work durable across sessions.
Building an AI agent in 2026 isn't one decision — it's four different paths. Here's how to tell which one fits what you're actually trying to do.
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
Skip the search. 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.
$ ask agentailor "which way should I build my agent?"
→ answers grounded in every post here.
→ no hunting through the archive.