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- Grade the outcome first, then use the step-by-step transcript to diagnose failures.<\/li>\n\n\n\n
- Run each task several times. An agent that succeeds once in three tries is not ready for customers.<\/li>\n\n\n\n
- Start with 20 to 50 tasks drawn from real failures, not hundreds of synthetic ones.<\/li>\n\n\n\n
- LLM-as-judge scoring scales well but has known biases, so calibrate it against human labels.<\/li>\n\n\n\n
- Wire evals into CI\/CD so a prompt or model change cannot ship without passing them.<\/li>\n<\/ul>\n\n\n\n
<\/span>What Is AI Agent Evaluation And Why Do Agents Need Different Testing?<\/span><\/h2>\n\n\n\n
AI agent evaluation is the practice of scoring how well an agent completes multi-step tasks, using a fixed set of test tasks, graders and pass criteria. It differs from regular software testing because agents are non-deterministic: the same input can produce different paths and different results on each run.<\/p>\n\n\n\n
It also differs from evaluating AI models<\/a> on their own. A model eval asks whether one response is accurate. AI agent testing asks whether a sequence of decisions, tool calls and actions reached the right end state without causing harm along the way.<\/p>\n\n\n\n
Three things make agents harder to test:<\/p>\n\n\n\n
- \n
- Many valid paths.<\/strong> An agent might solve a refund request in four steps or six. Both can be correct, so tests that demand one exact sequence break for no good reason.<\/li>\n\n\n\n
- Actions with side effects.<\/strong> Agents write to databases, send emails and update records. A wrong answer in a chatbot is embarrassing; a wrong action can cost money.<\/li>\n\n\n\n
- Compounding errors.<\/strong> A small mistake in step two, such as reading the wrong order ID, carries through every step after it.<\/li>\n<\/ul>\n\n\n\n
Evaluation also has a partner. AI agent observability<\/a> records what an agent did in production. Evaluation grades whether it was right. Strong teams use production traces to find new failures, then turn those failures into test cases.<\/p>\n\n\n\n
<\/span>What Should You Test In An AI Agent?<\/span><\/h2>\n\n\n\n
Test four layers: the final outcome, the path the agent took, each tool call and its safety boundaries. The outcome tells you whether the agent worked. The other three tell you why it failed and whether it got there safely.<\/p>\n\n\n\n
<\/span>1. The Final Outcome<\/span><\/h3>\n\n\n\n
Check the end state, not just the reply. If the task was to cancel order 4471 and refund the customer, confirm the order status changed and the refund record exists. Anthropic’s engineering team recommends<\/a> grading the outcome rather than a fixed sequence of steps, because agents often find valid approaches that test designers did not expect.<\/p>\n\n\n\n
<\/span>2. The Trajectory<\/span><\/h3>\n\n\n\n
The trajectory is the full record of the agent’s reasoning and actions. Grade it for efficiency and judgment: did it take a sensible route, avoid loops and ask for clarification when the request was ambiguous? Score trajectories with ranges, such as under eight steps, not one exact path.<\/p>\n\n\n\n
<\/span>3. Tool Calls<\/span><\/h3>\n\n\n\n
Most agent failures we see start at the tool layer. Check three things for each call: the right tool was chosen, the arguments were correct, and the agent handled errors properly when the tool failed. In multi-agent systems<\/a>, add the handoffs between agents to this layer.<\/p>\n\n\n\n
<\/span>4. Safety And Policy<\/span><\/h3>\n\n\n\n
Test what the agent must never do: issue a refund above its limit, expose another customer’s data, or act on instructions hidden in a document. These tests should be pass or fail with no partial credit. If your agent retrieves documents, also test retrieval quality, as covered in our guide to production RAG architecture<\/a>.<\/p>\n\n\n\n
<\/span>Which AI Agent Evaluation Metrics Matter Most?<\/span><\/h2>\n\n\n\n
The AI agent evaluation metrics that matter most are task success, reliability across repeated runs, tool-call accuracy, safety violations, efficiency and cost. Track them per task type, because an agent can be excellent at order lookups and weak at refunds.<\/p>\n\n\n\n
- Actions with side effects.<\/strong> Agents write to databases, send emails and update records. A wrong answer in a chatbot is embarrassing; a wrong action can cost money.<\/li>\n\n\n\n
- Many valid paths.<\/strong> An agent might solve a refund request in four steps or six. Both can be correct, so tests that demand one exact sequence break for no good reason.<\/li>\n\n\n\n
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