Agent Evaluation - Assessing AI Agents With Multi-Step Flows

Agent evaluation refers to the systematic assessment of AI agents - systems that do not just produce a single answer but plan across multiple steps, call tools, and process intermediate results to reach a goal. It is the agent-specific form of AI Evals.

The difference from evaluating a single LLM call is fundamental: for a single call, only the quality of the answer counts. For an agent, the path leads through many intermediate steps, and even a result that is correct in the end may have come about via a fragile, expensive, or risky path. Conversely, a wrong final result can trace back to a single faulty step that you never find without looking into the flow.

What Is Checked Beyond the Answer

• Trajectory: The sequence of steps - did the agent choose a sensible path, or did it get stuck in detours and repetitions?
• Tool use: Were the right tools called at the right time with the right parameters? An agentic workflow stands and falls with correct tool calls.
• Goal achievement: Was the actual task solved - and not just a plausible-sounding answer produced?
• Efficiency: How many steps, how many tokens, and what cost were needed? An agent that reaches the goal via an expensive detour is a problem in operation.

Assessing Final State vs. Trajectory

1. Final-state assessment: Only the result counts - was the goal reached? Simple to check, but says nothing about how it came about.
2. Trajectory assessment: The entire path is considered, often against an expected or reference path marked as good. This reveals where an agent makes unnecessary, risky, or wrong steps, even when the final result is correct.

In practice both are needed: the final state shows whether it works, the trajectory shows why. For assessing hard-to-check intermediate steps, LLM-as-a-Judge is often used.

Regression Tests on Model and Prompt Updates

Agents are especially sensitive to change. A new model, a reworded prompt, or a changed tool can shift the flow at a point you did not test - with consequences running through the whole chain. A regression test set with typical tasks and known problem cases is therefore rerun on every change. This shows as a number whether an update improved the agent or degraded it elsewhere, before it affects users. With multi-agent systems this need grows further, because errors can propagate between agents - see multi-agent system.

From Testing to Operation

Agent evaluation delivers the testing side; ongoing operation is secured via LLM observability and the broader approach AgentOps, which monitors quality, cost, and latency in live operation. Testing and operation interlock: anomalies from operation become new cases in the test set.

Agent Evaluation at Elasticbrains

At Elasticbrains we assess agents along trajectory, tool use, and goal achievement, and build regression test sets that secure model and prompt updates. See how this fits together with ongoing operation on our service page testing & operating AI agents.