Building Agent Evaluation Pipelines
How to test agent behavior continuously so you catch regressions before users do.
Building Agent Evaluation Pipelines
Why evaluation matters
Agents change behavior when models update, prompts drift, or tool schemas shift. Without a repeatable evaluation pipeline, the first sign of a regression is often an angry user or a failed task in production.
The three levels of evaluation
1. Unit tests for tools
Test each tool independently. Given a valid input, does it produce the expected output? Given invalid input, does it fail gracefully?
- Mock external APIs and filesystems.
- Test schema validation and edge cases.
- Run these on every commit.
2. Task-level benchmarks
Define a set of representative tasks with expected outcomes. Run the agent on each task and score pass/fail, latency, token cost, and output quality.
- Start with 10–20 tasks that cover your most common workflows.
- Include adversarial or ambiguous inputs.
- Version the tasks so you can compare model or prompt changes over time.
3. Human review loop
Automatic scores miss nuance. Reserve a sample of production runs for human review and use the findings to update your benchmarks.
- Review a random sample weekly.
- Track disagreement between human judgment and automatic scores.
- Feed the worst failures back into the benchmark set.
Building the pipeline
- Capture traces. Record every agent run: input, model calls, tool calls, outputs, and metadata.
- Define metrics. Choose metrics that match your use case: task success rate, tool accuracy, hallucination rate, cost, latency, user satisfaction.
- Run on changes. Trigger evaluations on prompt updates, model swaps, and dependency changes.
- Compare to baseline. Treat the current production version as the baseline and reject changes that regress it.
- Alert on drift. Set thresholds for metrics and alert the owner when they slip.
Tools that help
- Langfuse, LangSmith, or Weave for tracing and evaluation.
- LiteLLM or Portkey for routing and cost tracking.
- Custom test harnesses for task-level pass/fail scoring.
Honest limitations
- Evaluations can game the benchmark if the tasks are too narrow.
- Synthetic tasks may not match real user behavior.
- Human review is expensive but necessary.
Getting started
This week, pick your three most important agent tasks. Write a deterministic pass/fail test for each. Run them daily. That alone will catch most embarrassing regressions.
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