Learn AI agent engineering in a course built only for you. And actually retain it.
Move past chat wrappers. Learn the architecture of real AI agents: tool calling, orchestration loops, memory, evals, and the failure modes that kill agent products.
Free to start · no account needed
Built around your goal, not a catalog
What your course could open with
lrnit interviews you first (what you know, why you’re learning, how you think), then generates every lesson for that. Sample lessons from a course like yours:
Measured, not watched
By the end, you’ll be able to
- Design agent architectures that degrade gracefully, not catastrophically
- Write tool definitions that models call correctly
- Stand up an eval harness before your users become your eval harness
What a capability check looks likebeforenow
Design a tool schema+62
Ground and verify outputs+63
Stand up an eval harness+65
Every lesson ends in assessment. Mastery is tracked per concept with spaced review, and the course adapts to what you actually retain. Finishing means a timed final exam and a verifiable certificate, not a completion badge.
“It felt like the course already knew what I did for a living.”
Questions people ask
- What should I already know?
- Working programming experience and comfort calling an API. You do not need a machine-learning background; the model side you need is covered.
- Does this go past building a chat wrapper?
- Yes. It covers the agent loop, tool design, grounding and verification, and the evals that keep an agent honest at scale, which is where most agent products break.
- Is it tied to one framework?
- No. It teaches the architecture and failure modes, so the patterns transfer whether you use LangChain, a vendor SDK, or your own loop.
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Free to start · no account needed
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