# Agent trajectory evaluation with reviewable evidence

The final answer is not enough. AgentClash evaluates the trajectory — tool choices, observations, retries, artifacts, and stop conditions — then preserves replay evidence for auditors and release owners.

Source: https://www.agentclash.dev/agent-trajectory-evaluation
Markdown export: https://www.agentclash.dev/md/agent-trajectory-evaluation

## Why trajectories matter

Two agents can return the same answer while taking wildly different paths. Trajectory evaluation catches unsafe shortcuts, runaway loops, and brittle tool strategies.

- OpenTelemetry-compatible trace import
- Pinned datasets and golden test cases
- Baseline versus candidate regression checks
- Replay trails for tool calls, outputs, and artifacts
- Scorecards for correctness, cost, latency, and evidence
- CI gates for prompt, model, RAG, and tool changes

## Trajectory eval workflow

### 1. Import the evidence

Start from OpenTelemetry traces, curated datasets, support transcripts, or a real failure your team already saw.

### 2. Pin the baseline

Record the current accepted behavior so every prompt, model, RAG, or tool change has a fair comparison point.

### 3. Replay the evidence

Inspect tool calls, outputs, artifacts, latency, cost, and judge evidence when a candidate gets worse.

### 4. Gate the release

Compare candidate and baseline runs, then fail CI before a regression reaches users.

## Inspect runs with docs

Use replay and scorecards to debug trajectory regressions, then encode the workload as a challenge pack for CI.

- [Agent evals](https://www.agentclash.dev/agent-evals): Real-task agent evals with replay evidence and CI gates.
- [LLM agent evaluation](https://www.agentclash.dev/llm-agent-evaluation): Evaluate LLM agents on full trajectories, not one-shot answers.
- [Compare tools](https://www.agentclash.dev/compare): See how AgentClash differs from prompt-eval platforms.
- [Agent replay](https://www.agentclash.dev/features/agent-replay): See how AgentClash preserves replay evidence for reviewers.
- [Datasets overview](https://www.agentclash.dev/docs/guides/datasets-overview): Import examples, record baselines, sync regression suites, and gate CI.
- [Dataset CI gates](https://www.agentclash.dev/docs/guides/dataset-ci-gates): Fail builds when a candidate regresses against a pinned baseline.
- [CI/CD agent gates](https://www.agentclash.dev/docs/guides/ci-cd-agent-gates): Block pull requests when agent behavior gets worse.

## Trajectory evaluation FAQ

### What is agent trajectory evaluation?

Trajectory evaluation scores the sequence of actions and observations an agent took to complete a task, not just the final output string.

### How does AgentClash store trajectory evidence?

Each run keeps replay events, tool calls, logs, artifacts, and scorecards so reviewers can reconstruct the path that produced the result.

### Can trajectory evals gate releases?

Yes. Compare candidate and baseline trajectories via scorecards, then fail CI when correctness, cost, latency, or evidence quality regresses.