AI Agent Fundamentals
What an agent is, what a harness is, and when a problem actually needs an agent — and when it does not.
- 4 lessons
- 1h 13m
- 4 simulations
Context, permissions, sandboxing, errors and tracing — everything that makes an agent trustworthy outside a demo.
This is the real simulation from the module. No account needed.
A suggestion, never a lock — you can start here.
Every lesson's title and summary is public. The lessons themselves open once you own the track.
Every lesson in Building AI Agents: Harness EngineeringTrimming, summarising and offloading when the task runs long — without losing the facts the next step needs.
Allowlists, human approval and isolated environments for risky tools — enforced by the harness, not by the model's good behaviour.
Error messages that help the model correct itself, retries that do not make things worse, and when to give up.
Logging every step, token and cost so failures can be traced — and so you know what a normal run looks like.
What an agent is, what a harness is, and when a problem actually needs an agent — and when it does not.
Connecting agents to APIs, databases and files — with MCP as the standard interface.
Orchestrators and workers, subagents and context isolation — when splitting the work helps, and when it hurts.
Task-level evals, trajectory analysis, and reliability and cost metrics.