Evaluating AI agents is harder than evaluating standard language models, and teams that do not invest in it early pay for it later through production failures they cannot diagnose. The challenge is that agent quality is multidimensional: an agent can produce a correct final answer via a broken reasoning path, or produce a wrong answer via a sound process, and you need to know which failure mode you have to fix it. Evaluation is also dynamic — agents take sequences of actions, and quality can degrade at any step in the sequence. This guide covers what to measure, how to measure it, and how to build an evaluation system that scales with your agent’s complexity.
The Three Dimensions of Agent Quality
Agent evaluation needs to cover three distinct dimensions, each of which can fail independently. Task completion: did the agent accomplish the goal? This is the end-to-end metric — for a coding agent, did the code pass the tests; for a research agent, did it answer the question accurately; for a booking agent, was the booking made correctly. Process quality: did the agent take appropriate actions in the right sequence? An agent can complete a task via an unnecessarily long or expensive path, or via a path that would have failed on a slightly different input. Evaluating process quality catches agents that are brittle or inefficient even when they currently succeed. Safety and constraint adherence: did the agent stay within its defined scope, avoid prohibited actions, and handle edge cases appropriately? An agent that completes tasks correctly 95% of the time but takes a destructive action in the other 5% is not production-ready, regardless of its task completion rate.
End-to-End Evaluation with Test Cases
The most direct form of agent evaluation is end-to-end testing: run the agent on a set of test cases with known correct outcomes and measure the success rate. For a coding agent, the test cases are software engineering tasks with test suites (SWE-bench is the public benchmark; your own codebase is the production benchmark). For a data agent, the test cases are analysis tasks with verified correct answers. For a customer support agent, the test cases are conversations with defined correct resolutions. The key challenge in building a useful test suite is representativeness: the test cases need to cover the distribution of tasks the agent will encounter in production, including the difficult edge cases that most commonly cause failures. A test suite of only easy cases will give you optimistic metrics that do not predict production performance.
Trajectory Evaluation
Trajectory evaluation assesses the sequence of actions the agent took, not just the final outcome. For each test case, record the full action trace — every tool call, every model response, every intermediate state. Then evaluate whether the trajectory was correct: did the agent call the right tools, in the right order, with the right parameters? Did it avoid unnecessary steps? Did it backtrack appropriately when it hit a dead end? Trajectory evaluation is more expensive than end-to-end evaluation (it requires examining every step, not just the outcome) but catches a class of problems that end-to-end evaluation misses — agents that succeed on the test set via paths that would fail on slightly different inputs, or agents that are far more expensive than necessary because they take many redundant steps.
Figure 1 — Agent evaluation: what to measure at each layer
LLM-as-Judge for Subjective Tasks
For agent tasks where the correct answer is not binary — research summaries, analytical reports, customer support responses — automated evaluation using a separate LLM as a judge is the practical approach. The judge LLM receives the task, the agent’s output, and a rubric, and scores the output on dimensions like accuracy, completeness, and appropriateness. LLM-as-judge evaluation is scalable (much cheaper than human review), reasonably reliable for clear quality dimensions, and sufficient for catching major quality regressions. Its limitations: judges can be biased toward outputs that are confident and well-structured regardless of accuracy, and judge performance degrades on tasks where the judge lacks domain expertise. For high-stakes tasks, validate LLM judge scores against human ratings on a sample to calibrate how much to trust the automated evaluation.
Unit Testing Individual Agent Components
Beyond end-to-end evaluation, unit testing individual components of the agent system catches bugs before they propagate into production. Tool implementations should have unit tests that verify correct behaviour given valid inputs and appropriate error handling given invalid inputs. Prompt templates should be tested with representative inputs to verify that the agent interprets them as intended. Context management logic should be tested with long conversations to verify that truncation or summarisation does not drop critical information. Retrieval quality for memory and RAG components should be tested with queries that exercise both easy retrievals (exact keyword matches) and hard retrievals (semantic near-misses). Unit tests do not replace end-to-end evaluation but are faster to run and easier to debug, making them the first line of quality assurance in a continuous integration pipeline.
Red-Teaming and Boundary Testing
Production agents encounter adversarial inputs — users who probe the edges of what the agent will do, test whether it can be manipulated into taking prohibited actions, or inadvertently provide inputs that trigger unexpected behaviour. Red-teaming systematically explores these failure modes before deployment. For a customer support agent, red-team tests include: attempts to get the agent to reveal internal system prompts, requests for actions outside its defined scope, attempts to manipulate the agent by claiming special permissions, and edge-case inputs that stress the tool use decision logic. For a coding agent, red-team tests include: requests to execute system commands, requests to read or write files outside the defined workspace, and prompts that try to inject instructions through code comments or strings. Red-teaming results feed directly into guard rail design — the constraints that prevent the agent from taking prohibited actions regardless of instruction.
Production Monitoring
Offline evaluation on test suites is necessary but not sufficient — production inputs differ from test cases in ways that are hard to anticipate. Production monitoring closes this gap: instrument the agent to log every action trace, measure task completion rate on samples of production traffic, track latency and token cost per task, and alert on anomalies. Tools like LangSmith, Braintrust, and Arize provide observability dashboards purpose-built for LLM application monitoring. The critical metric to track is success rate over time: as the world changes (new user patterns, updated tools, model version changes), agent performance can drift without any code change. A monitoring system that detects this drift early — before users encounter widespread failures — is what separates reliably deployed agents from ones that surprise you in production.
Regression Testing on Model Updates
Language model providers update their models regularly, and model updates can change agent behaviour even when you have made no changes to your code. Maintaining a regression test suite — a fixed set of test cases with known correct outcomes — allows you to detect model-update-induced regressions before they reach production. Run the full regression suite whenever the underlying model changes. Compare results against the previous baseline. Investigate cases where performance changed — both regressions (tasks that previously succeeded now fail) and improvements (tasks that previously failed now succeed, which is worth understanding too). This practice is standard in software engineering and essential for any production agent system that relies on a frequently updated external model API.
Agent evaluation is an investment that pays dividends in production reliability and debugging speed. The teams that build comprehensive evaluation frameworks early — end-to-end test suites, trajectory review, LLM judges for subjective tasks, red-team suites, and production monitoring — ship more reliable agents faster than teams that discover failure modes in production. Start with the end-to-end test suite covering your most common and most critical tasks, add trajectory review as the system matures, and build production monitoring before the first production deployment. The evaluation infrastructure is as much a part of the agent system as the agent itself.
A practical sequencing note: prioritise safety and constraint evaluation before worrying about efficiency metrics. An agent that occasionally does the wrong thing is a liability; an agent that reliably does the right thing but takes an extra step is just a cost optimisation target. Get the safety tests passing first, then tune for efficiency, then scale the task completion rate improvements from there.