Evaluation & observability

A demo proves it can work once

The hardest question in agentic AI isn’t “can I build it” — it’s “how do I know it works, and how would I know if it stopped.” A demo proves an agent can succeed once, on a path you chose. A system proves it succeeds on paths you didn’t, and tells you the moment it regresses. The bridge between the two is measurement.

Evaluation: the numbers that tell the truth

• Golden sets, not vibes. A curated set of real tasks with known-good outcomes, run on every change. If a prompt tweak or a model swap moves the numbers, you see it before your users do.
• Eval harnesses in the pipeline. Evaluation is continuous, not a launch-day checklist. Every change is scored automatically — accuracy, task completion, tool-use correctness, refusal and hallucination rates — and a regression blocks the release.
• Task metrics over token metrics. Did the agent finish the job, correctly, within budget? End-to-end outcome beats per-call cleverness. Public benchmarks are useful; your own task suite, built from your real workload, is what actually matters.
• Adversarial evals. Jailbreaks, injection, malformed inputs, failing tools. An agent that’s only graded on the happy path is graded on the wrong thing.

Observability: seeing what the agent actually did

• Trace every step. One trace pipeline through every model call, tool hop, retry, and handoff. When an answer is wrong, you reconstruct exactly what the agent saw, decided, and did — in minutes, not days.
• Production signals tied to action. Drift, latency, cost-per-task, success rate, escalation rate — monitored live, with thresholds that trigger something. A dashboard nobody acts on is decoration.
• Replayable runs. Taking a real failure and replaying it against a fix is the fastest debugging loop there is. It also turns every incident into a new golden-set case.

Why the two are one discipline

Evaluation tells you whether it works. Observability tells you why, and whether it still does. Apart, each is half a system: evals without traces can’t explain a failure; traces without evals can’t tell you a failure happened. Together they’re the feedback loop that lets an agent improve safely instead of drifting quietly.

I lean on established measures where they fit — DORA for delivery, SPACE for developer experience — but the agent-specific work is building the eval suites and trace infrastructure that turn “it seemed to work” into a number you can defend.

What it delivers

Confidence you can act on. When the evals are green and the traces are clean, you ship. When they’re not, you know precisely where to look. That’s the discipline that separates a demo you hope holds up from a system you know does.