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AI|Course

Operating in production · Advanced

Deploy Reliable AI Agents in Production

A demo that works once is not a product. Learn to measure agent reliability, trace and debug runs, survive failures, defend against prompt injection, control cost and ship changes safely.

7 lessons · 16 hr 45 min · Free

You will learn to

  • ▸Measure reliability with evals and repeated-trial metrics, not single runs
  • ▸Trace agent runs end to end and alert on the right signals
  • ▸Handle timeouts, retries, idempotency, crashes and runaway loops
  • ▸Reduce prompt-injection risk with least privilege and structural limits
  • ▸Control cost and latency, and roll changes out and back safely

Before you start

  • ·You have built an agent that calls tools
  • ·Comfortable with Python or TypeScript and basic web services
  • ·Helpful: the Persistent Memory course, for context and cost techniques

Syllabus

  1. 01What reliable means for agentsWhy agents are hard to run, and how to measure consistency.45 min
  2. 02Evals: knowing it worksBuilding test sets, graders and validated LLM judges.2 hr 30 min
  3. 03Tracing and observabilitySee what the agent saw, decided and did.2 hr
  4. 04Failure handling and durabilityTimeouts, retries, idempotency, caps and human approval.2 hr 30 min
  5. 05Security: injection and excessive agencyThe lethal trifecta, least privilege and safe tool design.2 hr
  6. 06Cost, latency and safe releasesUnit economics, caching, rollouts and runbooks.2 hr
  7. 07Capstone projectHarden an action-taking agent and prove it is ready to ship.5 hr