Agentic AI in Production: A Field Guide

Autonomous agents are the most demanding workload most engineering teams have ever shipped. Unlike a classic ML model, an agent makes a chain of decisions, each compounding the uncertainty of the previous one. That changes how we design, test, and operate these systems.

In our engagements, the teams that succeed treat evaluation as a product surface: every agent action is logged, scored, and fed back into a regression suite. The teams that struggle treat evaluation as a launch checkbox.

This guide distills the operating patterns we use across client programs: bounded autonomy, human escalation paths, cost ceilings per task, and the observability stack that makes all of it debuggable.