AGNTCourse

Agentic AI / AI agents

Lessons7modules
Total65mfull study
Quick7mtrailer
Projects7docker labs

Skills you'll gain

08
  • Build the smallest agent that existsWorking

    Loop + tools = agent. Author the 12-line ReAct loop from memory and explain why it isn't a chatbot.

  • Author production-grade toolsProduction

    Tight JSON Schema + idempotent side effects + descriptions the model can route on. Three tools beat fifteen.

  • Trace and debug the ReAct loopProduction

    THINK before ACT, parse the model's reasoning, log ACT pre-dispatch — turn invisible failures into readable trace lines.

  • Wire memory & state correctlyWorking

    Facts → SQL, similarity → vector, conversation → msgs[]. Sliding-window N + similarity floor 0.7.

  • Plan-then-execute past 5 stepsProduction

    Persist the plan to disk; reactive ReAct fails past 5 hops. Plan-then-execute beats reactive at scale.

  • Bound cost in productionProduction

    Per-session token budgets, cheaper model for routing, cached deterministic tool results, Prom metric in dollars.

  • Layer agent safety end-to-endAdvanced

    Input filter → sandbox → output judge → audit log. Output judge runs on a SEPARATE model role. Adversarial inputs in CI.

  • Ship a guarded agent to productionProduction

    Tool schemas + step cap + audit log + budget cutoff + Prom-exposed metrics + a different-model output guard.