Course

Ship AI agents that actually work in production

If your current workflows pass in demos but fail with real users, this course gives you the controls to ship reliably.

What gets in the way

You can demo an agent quickly, but it breaks under real traffic and real edge cases.
Output drifts over time with no clear way to monitor or test behavior.
You are forced to choose between shipping fast and shipping safely.

Learn production architecture patterns for contracts, memory, and state handling.
Build evaluation, fallback, and recovery loops that keep agents predictable.
Ship with observability, security, and cost controls so reliability scales.

Design agent contracts, roles, and runtime boundaries that stop behavior drift.

Implement tool use with strict schemas and retry/recovery strategies.

Set up evaluation harnesses that catch regressions before users do.

Apply memory and orchestration patterns for multi-step workflows.

Track cost, latency, and quality so systems stay trustworthy at scale.

Do I need to be an AI researcher to follow this course?

No. You need practical coding ability and a willingness to build real systems one step at a time.

Will this help with production reliability, not just demos?

Yes. The course focuses on contracts, evaluation, observability, and safeguards that keep agents production-ready.

You can make agents that work sometimes, but trust and consistency are still missing for real customers.

Your week is leaking value on repetitive work you can automate with simple AI workflows.

You may have strong ideas and demos, but your AI work still has not become a repeatable business product.