# Evaluate customer support agents on real resolutions

Support agents must resolve tickets safely, use the right tools, and leave an audit trail. AgentClash evaluates full support trajectories and keeps replay evidence when tone, policy, or resolution quality regresses.

Source: https://www.agentclash.dev/use-cases/support-agent-evaluation
Markdown export: https://www.agentclash.dev/md/use-cases/support-agent-evaluation

## Support eval signals

Score resolution correctness, policy adherence, tool usage, escalation behavior, and customer-facing artifact quality.

- 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

## Support 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.

## Start with escaped tickets

Promote real support failures into challenge packs and regression suites so the same mistake cannot return after a model update.

- [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.

## Support agent evaluation FAQ

### Can AgentClash evaluate multi-turn support flows?

Yes. Multi-turn challenge packs support scripted, simulated, and human phases for realistic support conversations.

### How do teams measure policy adherence?

Challenge packs encode required actions, forbidden tool use, and validator checks so scorecards reflect policy—not just friendly language.

### Can support evals gate releases?

Yes. Compare candidate and baseline scorecards in CI before deploying a new support agent or model route.