How to Build an Evaluation System for AI Agents

1. Why AI Agents Need Dedicated Evaluation

In software engineering, “test before shipping” is almost a consensus. However, when the system under test changes from deterministic code to an autonomous AI Agent, the difficulty of testing is significantly amplified.

The core value of an Agent — autonomous decision-making, multi-step reasoning, tool invocation, modifying environmental state, and dynamically adjusting based on intermediate results — is also exactly what makes it hard to evaluate. An Agent often needs multiple interaction turns to complete a task. Any deviation at an intermediate step can propagate along the call chain and be amplified in later steps.

Without evaluation, teams can easily fall into a passive vicious cycle:

• Problems are exposed only in production, by which time they may already have affected real users.
• Fixing one defect introduces new behavioral changes, and the next round of production incidents is needed to confirm them.
• After every model change or prompt adjustment, there is no objective basis for determining whether performance has worsened; teams can only rely on subjective impressions.

The real value of evaluation is to intercept problems before they reach users. It makes behavioral changes observable, comparable, and regression-testable, and this benefit spans the entire Agent lifecycle: from early prototype validation, to pre-launch quality gating, to continuous regression protection during iteration. The more solid the evaluation, the more confidence a team has when releasing Agents.

2. The Essence of Agent Evaluation and Two Levels

The essence of evaluation is automated testing of an AI system: given an input, let the system under test produce an output, and then use scoring logic to determine whether the task succeeded. The evaluation discussed in this article specifically refers to automated evaluation that can run during development without real user participation.

To understand what makes Agent evaluation special, it helps to distinguish two levels:

Single-turn evaluation is straightforward: one prompt, one response, and one scoring logic. In early applications that were mostly single-turn and non-Agentic, this was the main evaluation method: input goes in, an answer comes out, and rules or model-based scoring determine whether it is correct. Its scoring boundary is clear and its results are deterministic.

Multi-turn Agent evaluation is much more complex. A typical coding Agent receives a tool set, a task description, and a runnable context environment. It then repeatedly executes the “Agent loop” — reasoning, invoking tools, observing returned results, and deciding the next action — while continuously updating environmental state with generated code or operations. When it believes the task is complete, an independent verification method, such as unit tests or API probes, confirms whether the artifacts are actually usable.

A more concrete illustration of this loop is shown below:

def run_agent_eval(task, tools, env, max_steps=20):
    for step in range(max_steps):
        action = agent.decide(task, tools, env.state)  # reason and select a tool
        result = tools.invoke(action, env)             # produce real side effects in the environment
        env.update(result)                             # update state with intermediate results
        if agent.is_done(result):                      # determine whether the task is complete
            break
    # Key: artifacts must be verified by an independent verifier, not by the agent itself
    return verifier.check(env.artifacts)

This loop reveals a core distinction: Agent evaluation is no longer just judging whether a single output is good. It must determine whether an entire process correctly converged to the goal. The object being scored changes from “one answer” to “one trajectory plus the final artifacts.”

3. Four Sources of Agent Evaluation Complexity

1. Path space caused by multi-turn execution

Single-turn evaluation has only one input-to-output mapping, while each step of a multi-turn Agent may face multiple reasonable choices. The same task may have multiple successful paths, and it may also have multiple wrong paths that “look like they are making progress but are actually deviating.” Evaluation must accommodate this diversity rather than checking for a single golden answer like a deterministic function.

2. Real side effects of tools and environments

Agents modify files, write to databases, call external APIs, and change system state. This means the evaluation environment must support reset and isolation; otherwise, one evaluation may pollute the next, or leave unrecoverable traces on production data.

3. Propagation and accumulation of errors

An early judgment mistake can be treated as a fixed premise by later reasoning, causing errors to be amplified along the chain. Single-point evaluation makes it difficult to locate the root cause; evaluation methods need to mark the position where the deviation first appears in a multi-turn trajectory.

4. Models may bypass the evaluation design

Frontier models have strong exploration capabilities and may sometimes find creative solutions beyond the evaluation’s assumptions. For example, in a booking task involving rule constraints, a model may discover loopholes in the task rules themselves and produce a solution that “satisfies the evaluation metrics but violates the real business intent.” This suggests that evaluation metrics themselves also need to be scrutinized. Scoring logic should stay as close as possible to real user expectations instead of relying on rules that can be easily gamed.

4. Layered Evaluation: Match Method Complexity to System Complexity

The key to handling these complexities is not to seek a single “perfect evaluation,” but to combine multiple techniques so that the complexity of the evaluation method matches the complexity of the system under evaluation.

• Simple or deterministic scenarios: When task boundaries are clear and outputs can be enumerated, rule-based assertions and exact matching are sufficient. This approach has the lowest cost and fastest feedback.
• Complex multi-turn scenarios: When an Agent needs to call tools and operate on an environment, evaluation must be upgraded to “multi-turn evaluation with environments and tools,” supported by independent verifiers such as unit tests and integration tests to determine whether the final artifacts are valid.
• Subjective or open-ended scenarios: When outputs are difficult to judge with hard rules, model-assisted scoring methods such as LLM-as-judge can be introduced. However, their stability and consistency need to be calibrated so that the scorer itself does not become a new source of uncertainty.

The principle is clear: be deterministic when possible, and combine methods when necessary. Applying one method to every scenario often leads to over-engineering in simple cases and insufficient coverage in complex cases.

5. Engineering Practices for Implementing an Evaluation System

For evaluation to run reliably and remain useful over time, it needs supporting engineering infrastructure:

• Reproducibility of data and environments: Prepare an independent sample set and resettable runtime environment for evaluation so that every run starts from a consistent baseline. Container platforms can encapsulate the Agent runtime environment and tool sandboxes, while object storage can manage evaluation datasets and artifact snapshots.
• Reliability of scoring: Scoring logic should be versioned and auditable, with a clear definition of “success.” Subjective scorers should be checked periodically for consistency.
• Asynchronous execution and batch processing: Multi-turn Agent evaluation can take a long time. Message queues can help with task orchestration and peak shaving, allowing large-scale regression evaluation to run stably.
• Observability and alerting: Connect trajectories at every step, scoring results, and failure modes to logging services and monitoring alerts. If an evaluation metric falls below a threshold, an automatic notification can be triggered, forming a closed loop.
• Continuous regression: Integrate evaluation into the CI pipeline. Every model or prompt change should automatically trigger a regression run, using historical results as a baseline to prevent “fix one thing, break another.”

Conclusion

Evaluation is not a one-time action before launch; it is a continuous engineering capability that spans the entire Agent lifecycle. Understanding the essential difference between Agent evaluation and traditional single-turn evaluation, recognizing the sources of complexity caused by multi-turn execution, selecting and combining evaluation methods according to system complexity, and consolidating them with reliable engineering infrastructure — this combined approach is the solid foundation that enables teams to confidently release and continuously iterate on AI Agents.