# AI agent testing that starts from real failures

AI agent testing should feel closer to software testing than prompt tweaking. AgentClash turns traces, golden datasets, and escaped failures into repeatable tests with baseline comparisons.

Source: https://www.agentclash.dev/ai-agent-testing
Markdown export: https://www.agentclash.dev/md/ai-agent-testing

## What production-grade agent testing covers

Testing agents means checking behavior across the whole run, including tool calls, retrieval, cost, latency, and artifacts.

- 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

## Test loop

### 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.

## Start testing with docs

Import a trace or dataset, run an eval, inspect replay, then wire the same workload into 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.
- [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.

## AI agent testing FAQ

### Is AI agent testing different from LLM testing?

Yes. LLM testing often means scoring one response. AI agent testing evaluates plans, tool calls, artifacts, recovery behavior, and whether the task actually finished.

### Can non-ML engineers review agent test failures?

Yes. Replay timelines, artifacts, and scorecards are designed for reviewers who need to understand what changed without reading raw model traces alone.

### How do we keep tests from getting stale?

Promote escaped production failures into datasets, challenge packs, and regression suites so the same mistake stays covered after the next model swap.