# Agent evaluation for insurance support and compliance

Insurance agents must follow policy, escalate correctly, and leave an auditable trail. AgentClash evaluates full support trajectories and preserves replay when resolution quality or compliance signals regress.

Source: https://www.agentclash.dev/industries/insurance
Markdown export: https://www.agentclash.dev/md/industries/insurance

## Insurance eval signals

Measure policy adherence, escalation behavior, artifact completeness, multilingual quality where needed, and whether the agent finished with a defensible resolution.

- 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

## Insurance 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 claims

Promote real claim or policy failures into challenge packs so the same mistake cannot return after a prompt or 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.
- [Enterprise pilot](https://www.agentclash.dev/enterprise): Stand up governed eval for support and compliance agents.
- [Support agent evaluation](https://www.agentclash.dev/use-cases/support-agent-evaluation): Use-case overview for ticket resolution eval.
- [Challenge pack glossary](https://www.agentclash.dev/glossary/challenge-pack): How packs encode insurance workflows.
- [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.

## Insurance agent evaluation FAQ

### Can AgentClash evaluate multi-turn claims conversations?

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

### 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.

### Does AgentClash replace compliance sign-off?

No. AgentClash supplies evaluation evidence and gates. Final compliance and underwriting decisions remain with your organization.