Production-Ready Systems with LLMs and Agents: An Intensive for Engineers

Source: https://maven.com/gazar/production-ready-systems-with-llms-and-agents-an-intensive-for-engineers

You can wire up an LLM and demo an agent that wows the room. Then it meets production, and everything you weren’t taught shows up at once: costs spiral, latency balloons, the model does something confidently wrong in front of a user, and you have no evals to catch it and no traces to debug it. The demo was the easy part.

If you’re the engineer now expected to ship AI features, that gap is yours to own, and the field moves faster than anyone can teach it. So you improvise, lean on framework tutorials that stop at the happy path, and quietly hope it holds.

This intensive teaches the part the tutorials skip: the architecture decisions that make LLM and agent systems survive real traffic, real cost ceilings, and real failure. Where to draw the boundary between code and model. How to bound cost and latency. How to design for non-determinism, evals, observability, and human oversight.

You leave with production-grade artifacts you can use at work on Monday: an agent architecture doc, a cost and latency budget, an eval harness plan, and a failure-mode runbook. Not another demo. A system that holds.

It’s not my rip

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