# An agent evaluation framework built for production tasks

Teams comparing agent evaluation frameworks should look past leaderboard scores. AgentClash gives you repeatable workloads, same-task eval runs, replay evidence, and release gates you can audit in git.

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

## What a serious framework includes

A useful agent evaluation framework packages tasks, enforces fair constraints, scores trajectories, and makes failures reusable.

- 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

## Framework 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.

## Evaluate before you commit

Use AgentClash alongside prompt-eval tools when you need end-to-end agent behavior, not single-call scoring.

- [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.
- [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.

## Framework comparison FAQ

### How is AgentClash different from prompt-evaluation frameworks?

Prompt-evaluation frameworks score isolated model outputs. AgentClash is an agent-evaluation framework for multi-turn tool-using runs in a sandbox.

### Can we compare AgentClash with other tools?

Yes. See the compare hub for side-by-side notes with Braintrust, LangSmith, Promptfoo, Langfuse, Arize Phoenix, and OpenAI Evals.

### Does the framework support custom scoring?

Yes. Challenge packs carry scoring rules, validators, and judge configuration so teams can encode domain-specific pass conditions.