Outlines the capabilities needed to evaluate and monitor multi-step AI agents: distributed tracing across workflows, deterministic trace replay for debugging non-deterministic failures, tool-use analysis, and reasoning-chain validation via LLM-as-a-judge. Covers the platforms that provide them and the functional, operational, and behavioral metrics for assessing agent reliability.
Evaluating an agent is different from evaluating a single response. What matters is whether a multi-step workflow reaches its goal: the right tools called in the right order, coherent intermediate reasoning, and consistent state across a long interaction. That shifts the tooling from output grading to trace analysis.
Core capabilities
| Capability | What it does | Representative Tools |
|---|---|---|
| Distributed Tracing | Captures the full agent workflow — LLM calls, tool invocations, decision points. | Langfuse, Arize Phoenix, LangSmith, Maxim AI |
| Trace Replay | Deterministically re-runs a historical agent run by substituting recorded LLM/tool responses, isolating non-deterministic failures. | Braintrust, LangSmith, custom harnesses |
| Tool-Use Analysis | Tracks which tools an agent calls, success rates, parameter correctness, and the link between tool use and task success. | Weights & Biases Weave, LangSmith, Maxim AI |
| Reasoning-Chain Validation | Grades intermediate decisions — plan coherence, tool selection — usually with an LLM-as-a-judge. | Braintrust, Maxim AI (node-level), DeepEval |
| Agent Goal Accuracy | Measures task completion against user intent, reference-based or reference-free. | Ragas (agent_goal_accuracy), Coval |
Performance metrics
Agent metrics fall into three groups — what it achieved, what it cost, and how it behaved under pressure.
| Type | Metric | What it captures |
|---|---|---|
| Functional | Task Completion Rate | Share of goals successfully reached in a session. |
| Functional | Tool Selection Precision | Accuracy of choosing the correct tool or API for the task. |
| Operational | Latency per Agent Run | Total time for a multi-step workflow to complete. |
| Operational | Token Cost per Goal | Economic efficiency of completing a task. |
| Behavioral | Context Retention | Ability to hold relevant information across turns. |
| Behavioral | Error Recovery Rate | Ability to handle ambiguity or tool failure without breaking the workflow. |
Reasoning-chain validation and goal accuracy both lean on judge models — see LLM-as-a-Judge Methodology. For the platforms that provide agent tracing, see LLM Evaluation and Observability Platforms.
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