Short answer: You evaluate an AI agent by building an evals harness — a graded set of representative tasks that runs automatically on every change — and scoring it on task completion, output accuracy/quality, tool-use correctness, latency, and cost-per-task. Evaluating an agent is harder than evaluating a single model output because agents take multiple steps and choose their own paths, so you measure both the final outcome and the trajectory that produced it. Without evals, you cannot tell whether a prompt tweak, a model swap, or a new tool made your agent better or worse — which is why missing evals is one of the top reasons agents never reach production.
Why agent evaluation is different (and harder)
Evaluating a single model call is relatively simple: given an input, is the output correct? Agents break that simplicity in three ways. First, multiple steps: an agent might call five tools across ten turns, and a failure can hide in step three even when the final answer looks plausible. Second, dynamic paths: two correct runs may take different routes, so you can't just diff against one "right" trajectory. Third, open-ended outputs: many agent outputs have no single correct answer, so exact-match scoring doesn't work. Good evaluation therefore measures both the outcome (did it accomplish the task?) and the trajectory (did it use the right tools, sensibly, at a reasonable cost?).
The metrics that matter
For a production agent, track all of these over time, not once. A change that improves accuracy but doubles cost-per-task may not be a win.
How to build an evals harness, step by step
1. Collect representative cases
Start from real tasks the agent will face — production logs, support tickets, domain experts. Cover the common path and the messy edge cases where agents actually fail.
2. Define success conditions
Write down what "done correctly" means for each case. Some are crisp (the order status matches the database); some need a rubric. Vague criteria produce meaningless scores.
3. Choose scoring methods
Deterministic checks for anything verifiable; rubric / LLM-as-judge for open-ended outputs (with human spot-checks to keep the judge honest); trajectory assertions for tool use.
4. Automate it on every change
The harness must run on every prompt change, model swap, tool addition, and context tweak — ideally in CI — to catch regressions before users do.
5. Track results over time
Store scores so you can see trends. Reliability is a curve you push up over weeks, not a single number.
Evaluating the hard stuff
Multi-step trajectories: inspect the sequence of tool calls, assert on key steps, and flag runs that reach a correct answer through a wasteful or wrong path — those are fragile. Open-ended outputs: use rubric-based scoring with an LLM judge, but calibrate the judge against human labels on a sample and re-check periodically. Production reality: pair offline evals with online observability so your test set keeps absorbing new failure modes real traffic reveals.
Common evaluation mistakes
Why evals are a competitive advantage
Evals are not just QA — they are a moat. A proprietary, domain-specific evaluation set encodes what "good" means in your context, something a better base model cannot replace. Teams that publish before/after reliability numbers from real evals win trust far faster than teams offering testimonials.
How Moai Team uses evals
Moai Team treats the evals harness as the centerpiece of every agentic build, not an afterthought. We define success conditions with domain experts, automate scoring on every change, and report accuracy, completion, latency, and cost-per-task as the headline numbers of a project.
Frequently Asked Questions
How do you evaluate an AI agent?
Build an evals harness: a graded set of representative tasks with defined success conditions that runs automatically on every change, scoring task completion, accuracy, tool-use correctness, latency, and cost-per-task.
What metrics matter most for agent evaluation?
Task completion/success rate, output quality, tool-use correctness, latency, and cost-per-task — tracked over time. Robustness and safety adherence matter for production.
Why is evaluating agents harder than evaluating a model?
Agents take multiple steps, choose their own paths, and often produce open-ended outputs, so you must evaluate both the final outcome and the trajectory that produced it.
What is an evals harness?
An automated, graded test set of real tasks that runs on every change to the agent, catching regressions before they reach users and quantifying reliability over time.
A proper evals harness is the difference between a demo and a product. Moai Team builds it into every engagement. Schedule a call.