Agents that take
action, not chatbots
that answer.
7 micro-lessons · ~66 min · Real Docker images
Agentic AI / AI agents
Build agents that take action, not chatbots that answer.
Real skills, real career delta.
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.