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Agent Evaluation Beyond Vibes: How We Test AI Agents

A practical framework for evaluating AI agents in production — from unit-level prompt tests to end-to-end conversation scoring.

ServoAgent Team1 min readMarch 15, 2026
evaluationtestingagentsquality

The Evaluation Problem

Most teams "evaluate" their agents by trying a few prompts and seeing if the output looks good. This works for demos. It does not work for production systems handling thousands of conversations a day.

At ServoAgent, every agent goes through a structured evaluation pipeline before it reaches a single customer.

Three Levels of Agent Evaluation

Level 1: Prompt-Level Tests

These are the unit tests of agent evaluation. For each critical intent, we maintain a set of input/output pairs and test that the agent produces the expected behavior. Not just the text — the actions, tool calls, and state transitions.

Level 2: Conversation-Level Scoring

Individual turns can look fine while the full conversation goes off the rails. We score complete conversations on:

  • Task completion: Did the agent achieve the user's goal?
  • Coherence: Did the conversation flow naturally?
  • Safety: Did the agent stay within its guardrails?
  • Efficiency: How many turns did it take?

Level 3: Production Monitoring

Evaluation doesn't stop at deployment. We continuously sample live conversations and score them against the same rubrics. Drift gets caught early, before it becomes a customer escalation.

Building Your Evaluation Pipeline

ServoAgent's evaluation tools are available to every workspace. You can define test suites, run batch evaluations against new agent versions, and set up automated gates that prevent regressions from reaching production.