As autonomous systems scale in 2026, Observability becomes the primary control plane. Engineers trace end-to-end LLM calls, analyze chain-of-thought logic, and
Agent evaluation and observability is the QA discipline of autonomous AI: scoring multi-step trajectories (not just final answers), tracing tool calls and decisions, regression-testing behavior across model changes, and wiring quality signals into production monitoring. No serious agent ships without it.
Among the fastest-growing AI specialties: every agent deployment needs this and few practitioners exist, evaluation fluency is the most reliable door into senior agent-engineering work.
You score processes, not just answers: did the agent plan sensibly, call the right tools with right arguments, recover from errors, and stay within bounds? Trajectory rubrics and trace analysis replace single-output grading.
Tracing/observability platforms, eval frameworks with judge calibration, and plenty of bespoke harness code: the field is young enough that builders of tooling are as hired as users of it.