Free Course Teaches Context, Harness, and Loop Engineering for AI Agents

A freely available course called "Agentic Engineering for AI" is gaining traction among developers building LLM-based agents. Structured around four nested disciplines — prompt, context, harness, and loop — the 2026 edition reorganizes the field into nine modules covering everything from prompt design and RAG to agent evaluation, security, and self-modifying "meta-harness" systems.

What sets it apart is its emphasis on falsifiable claims: the course ships a dependency-free Python reference harness with 134 unit tests, ten auto-graded exercises, and a self-validation script that checks its own content for consistency. Optional live-model labs, costing under $1 total, demonstrate real-world non-determinism and prompt injection that offline mocks can't replicate.

For engineers, the course formalizes lessons often learned the hard way in production: context windows degrade and must be managed, verification must happen outside the agent being tested, capability should be scoped tightly, and nothing can be improved without measurable evals. It takes roughly 35-44 hours to complete and requires only basic Python and API familiarity — no ML background needed.