# LLM agent evaluation beyond single-turn answers

LLM agents plan, call tools, inspect results, and recover from mistakes. AgentClash evaluates that full loop on your workloads so model swaps and prompt changes do not hide regressions.

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

## Evaluate the agent loop, not just the model

LLM agent evaluation needs the same task, same tools, and preserved evidence — otherwise you are comparing demos, not systems.

- 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

## A practical LLM agent 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.

## Bring your workload into AgentClash

Start with one real failure, encode it as a challenge pack, then scale to model comparisons and CI gates.

- [Agent evals](https://www.agentclash.dev/agent-evals): Real-task agent evals with replay evidence and CI gates.
- [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.

## LLM agent evaluation FAQ

### What should LLM agent evaluation measure?

At minimum: task success, tool strategy, artifacts produced, cost, latency, and whether the agent stayed inside policy. AgentClash captures all of that in a scorecard.

### Can we compare multiple LLM agents fairly?

Yes. AgentClash runs candidates on the same challenge pack with the same tool policy, time budget, and sandbox resources.

### Does AgentClash work with hosted model providers?

Yes. AgentClash routes to major LLM providers and normalizes tool-call shapes so evals stay comparable across models.