Evaluation tells you whether an AI agent can work; observability tells you how it is working. Evaluation scores the agent on real cases before you ship — task completion, correct tool calls, safety, cost. Observability traces what the agent actually does in production — decisions, tool calls, errors, latency. In 2026 most teams already have observability (~89%) but few run evals (~52%), and that is exactly why agents pass the demo and then break in production. You need both, and evaluation is the one most teams are missing.
An agent that "seems to work" is not the same as an agent you can trust. The only way to know is to measure it — before you ship and continuously afterwards. Below is the difference between evaluation and observability, and how to set up both for production agents.
Evaluation is scoring an agent's behavior on a set of real, representative cases with known correct outcomes. It answers "does this agent do the task correctly, safely, and within budget?" — measured, not guessed. A good eval suite covers task completion, correct tool calls, policy compliance, cost and latency, and regressions, and runs on every change so you know whether an edit made the agent better or worse.
Observability is the ability to see what an agent actually does in production: full traces of the reasoning chain and tool calls, metrics, errors, and the decision behind each action. Evaluation is a controlled test before and alongside deployment; observability is live visibility during deployment. Evaluation asks "can it work?"; observability asks "is it working correctly right now, and why did it do that?". The two are complementary — evaluation catches failures before users hit them, observability catches the ones only real traffic reveals.
Because observability without evaluation only tells you something broke, not whether the agent was ever actually good. If you've never scored the agent on real cases, you're shipping on gut feel — and monitoring only shows you failures after they reach users. The 2026 gap is clear: teams wire up dashboards but skip the evals that could have caught failures before launch. Observability is necessary; evaluation is what most teams are missing.
Build the eval suite first, from real cases, and run it on every change; add full tracing and decision-level logging so production behavior is visible; and close the loop by feeding production failures back into the eval suite. This is the discipline that separates a demo from a system that survives real traffic (see from pilot to production), and it goes together with the authorization and audit of agent governance. If you can't measure whether your agent is getting better, you're not operating it — you're hoping.
Evaluation and observability are core to how we ship reliable agents. See how we approach AI agent development, or send us your agent and we'll help you measure whether it actually works.