Introduction#
Deploying an AI agent is straightforward. Knowing whether it actually works is the hard part.
Unlike traditional software, AI agents are probabilistic — the same input can produce different outputs across runs. An agent that completes a task correctly in manual testing may fail 30% of the time in production. Without a structured evaluation process, you are flying blind.
This guide walks through everything you need to build a rigorous AI agent evaluation pipeline: the right metrics to track, how to design test suites, when to use automated versus human evaluation, and which frameworks help you catch regressions before they reach users.
For a broader understanding of how agents operate before you evaluate them, see our tutorials index and the agent loop glossary entry.
Why Evaluation Matters for AI Agents#
Software testing has a long history of mature tooling — unit tests, integration tests, linting, type checkers. Agent evaluation is still catching up, and the stakes are higher because failures are often silent. An agent can return a syntactically valid response that is factually wrong, off-task, or harmful without raising any errors.
Consider a customer support agent that resolves tickets. If it closes tickets without actually solving problems, your CSAT scores will crater weeks later. Without evaluation, you would not know until real damage was done.
Structured evaluation serves three purposes:
- Regression detection: Catch when a model update or prompt change breaks behavior that previously worked.
- Confidence before deployment: Quantify how well an agent performs before putting it in front of users.
- Continuous improvement: Identify failure patterns you can address with better prompts, tools, or retrieval.
This connects directly to agent observability — evaluation is what you do before production, observability is what you do during.
Prerequisites#
Before building an evaluation pipeline, you need:
- A working agent with defined inputs and expected outputs
- A representative sample of real or synthetic tasks (minimum 50, ideally 200+)
- A clear definition of what "correct" means for your use case
- Basic familiarity with at least one agent framework (see LangChain vs AutoGen for framework options)
Step 1: Define Your Evaluation Criteria#
Before writing a single test, decide what "good" means for your agent. The right metrics depend on your use case, but most agents should be measured across four dimensions.
Task Completion Rate#
The percentage of tasks the agent completes end-to-end without getting stuck, erroring out, or requesting human intervention. This is your most fundamental metric. An agent with an 85% completion rate has a 15% failure rate — unacceptable for most production use cases without a fallback.
Answer Accuracy / Correctness#
For agents that produce factual responses, you need to measure whether the answer is correct. This is harder than it sounds. For structured outputs (JSON, SQL, code), you can compare against expected values programmatically. For natural language responses, you need either human raters or an LLM-as-judge approach.
Latency#
Track both median and p95 latency. A slow agent frustrates users even when it is correct. For multi-step agents, break down latency by component — tool calls, LLM inference, and retrieval — so you know where to optimize.
Cost Per Run#
Every agent run has a cost: LLM tokens, API calls, compute. Measure this early. An agent that costs $0.50 per task might be fine for high-value workflows and completely unacceptable for commodity tasks. See how to measure AI agent ROI for connecting costs to business value.
Step 2: Build Your Evaluation Dataset#
Your evaluation dataset is the foundation of everything. A poor dataset produces misleading results.
Collecting Real Examples#
The best eval data comes from real usage. Collect agent runs from production or beta testing, label them as pass/fail, and use them as your ground truth. Aim for diversity — edge cases, failure modes, and unusual inputs should be well-represented, not just the happy path.
Generating Synthetic Examples#
When you lack real data, generate synthetic examples. Use a separate LLM to create diverse task variations, then manually review a sample to confirm quality. Synthetic data works well for capability coverage but misses the long tail of real-world inputs.
Dataset Versioning#
Version your eval datasets like code. When you add new examples or fix labels, commit the change with a description. This lets you track whether improvements on newer dataset versions actually reflect real gains or just teaching to the test.
Step 3: Choose Your Evaluation Method#
There are three main approaches to evaluation, each with different trade-offs.
Automated Deterministic Evaluation#
For structured outputs, use exact match or programmatic checks. If your agent produces JSON, validate the schema. If it writes SQL, execute it and compare results. If it calls tools, verify the tool calls match expected parameters. This is fast, cheap, and objective — use it wherever possible.
LLM-as-Judge Evaluation#
For natural language outputs, prompt a separate LLM (often GPT-4 or Claude) to score responses on criteria like relevance, completeness, and accuracy. This scales better than human evaluation but introduces its own biases. Always calibrate your LLM judge against human ratings on a sample set before trusting it at scale.
Frameworks like RAGAS (for RAG-heavy agents) and Braintrust provide pre-built LLM judge prompts and scoring pipelines.
Human Evaluation#
For high-stakes use cases, nothing replaces human review. Use human evaluation to calibrate your automated methods, to evaluate on criteria that are hard to automate (tone, safety, appropriateness), and to periodically audit production traffic. Human eval is expensive — reserve it for where it counts most.
Step 4: Set Up Your Evaluation Framework#
Several frameworks exist specifically for agent evaluation:
LangSmith is the most widely used for LangChain-based agents. It provides tracing, dataset management, and evaluation pipelines with a UI for inspecting individual runs. Strong for teams already on LangChain.
Braintrust is framework-agnostic and focuses on experiment tracking. It stores eval results over time, making regression detection easy. Supports custom scoring functions and LLM-as-judge out of the box.
RAGAS specializes in retrieval-augmented generation (RAG) evaluation. It measures faithfulness, answer relevance, context recall, and context precision — the four dimensions most critical for RAG agents.
Promptfoo is open-source and popular for testing prompts before they reach production. Good for early-stage evaluation before you have a full agent pipeline.
For a comparison of the frameworks powering these tools, see our open-source vs commercial AI agent frameworks comparison.
Step 5: Integrate Evaluation Into CI/CD#
Evaluation only catches regressions if you run it consistently. The goal is to run your eval suite automatically on every meaningful change — model updates, prompt edits, tool modifications, retrieval changes.
Set up a GitHub Actions or similar CI pipeline that:
- Runs your deterministic eval suite on every pull request
- Blocks merges if task completion rate or accuracy drops below your threshold
- Runs the full eval suite (including LLM-judge evals) on merge to main
- Sends alerts when production metrics deviate from eval baselines
Define explicit pass/fail thresholds for your key metrics. A common starting point: reject changes that drop task completion rate by more than 2 percentage points or accuracy by more than 3 percentage points from baseline.
Step 6: Track Regressions Over Time#
Point-in-time evaluations tell you where you are. Trend tracking tells you whether you are improving. Log every eval run with its timestamp, the version of the agent (prompt hash, model version, tool versions), and all metric scores.
Review these trends weekly. Watch for:
- Gradual accuracy drift (often caused by model updates from the provider)
- Latency increases as agent complexity grows
- Cost per run creep as token usage expands
The agent observability glossary covers the production-side complement to this tracking work.
Common Mistakes in Agent Evaluation#
Testing only the happy path. Most developers write evals for the standard successful case. Real users hit edge cases constantly. Deliberately include malformed inputs, ambiguous instructions, and adversarial examples.
Using the same model for judging and generation. If you use GPT-4 to generate responses and GPT-4 to judge them, you are measuring self-consistency, not correctness. Use a different model or human raters for ground truth.
Setting thresholds arbitrarily. Base your pass/fail thresholds on business requirements, not gut feel. If a 90% task completion rate means your support team gets 10 extra tickets per 100 interactions, decide whether that is acceptable before setting the threshold.
Ignoring cost in evals. An agent that is more accurate but twice as expensive is not automatically better. Evaluate cost-efficiency alongside quality metrics.
Not versioning eval datasets. Adding new eval examples without versioning makes it impossible to compare old results against new ones.
Best Practices for AI Agent Evaluation#
- Start with 50 examples, not 500. A small, high-quality eval set with accurate labels beats a large set with noisy labels.
- Label failures, not just successes. Every failure in production is an eval example. Build a system to capture and label production failures automatically.
- Separate capability evals from regression evals. Capability evals test what the agent can do. Regression evals test that it still does what it used to do. Run them separately.
- Invest in your labeling process. Inconsistent labels corrupt your eval signal. Define clear labeling guidelines and review label consistency across raters.
- Evaluate tools independently. Before evaluating the full agent, evaluate each tool in isolation. This isolates failures and speeds up debugging.
For an example of how multi-agent systems complicate evaluation, see the LangGraph multi-agent tutorial.
Conclusion#
AI agent evaluation is not a one-time activity — it is an ongoing discipline. The teams building reliable agents in production are running evals continuously, tracking regressions religiously, and using failure data to drive improvement.
Start simple: define your metrics, build a small labeled dataset, run deterministic checks, and integrate into CI. Then layer in LLM-as-judge evaluation and human review as your system matures. The investment compounds quickly — every regression you catch in eval is a production incident you avoid.
For more on building and deploying agents, visit the tutorials index and explore the AutoGen Studio setup guide.
Frequently Asked Questions#
What is the minimum number of examples needed for a useful AI agent eval set?
Fifty well-labeled examples is a practical starting point for catching major regressions. For statistical significance on metrics like accuracy, aim for 200+ examples. Prioritize label quality over dataset size — 50 carefully reviewed examples outperform 500 with noisy labels.
How do I evaluate an AI agent that produces different outputs every run?
Use LLM-as-judge scoring with rubric-based criteria rather than exact match. Define criteria like task completion, factual accuracy, and relevance, then score each response against the rubric. Average scores across multiple runs to account for variance, and set thresholds on the average rather than individual runs.
What is the difference between AI agent evaluation and observability?
Evaluation is pre-production: you run your agent against a test dataset and measure performance before deploying. Observability is post-production: you monitor agent behavior on real traffic in real time. Both are necessary. Evaluation prevents known failure modes from reaching production; observability catches the unknown failure modes that slip through.
Should I build my own evaluation framework or use an existing tool?
Start with an existing tool. LangSmith, Braintrust, and RAGAS cover the majority of use cases and save weeks of engineering effort. Build custom tooling only when you have highly specific requirements that existing frameworks cannot handle.