Use Claude Code to help engineer a separate, tightly bounded claims service—not as the service that adjudicates claims. Start with a verifiable task such as extracting facts or summarizing evidence, keep coverage and payment decisions under authorized human control, and make every model result traceable to the claim record that supports it.
What Claude Code should—and should not—do
Claude Code is an engineering assistant for building and maintaining software. A production claims agent is a different system: it has a model, a harness that governs how the model is used, tools it can call, and an environment in which those tools and data are available. Anthropic describes all four as sources of capability and potential oversight. The deployment environment and accessible systems affect the system’s risk, so a safe prompt alone is not a safety boundary.
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Build and deploy the claims service separately, with an explicit model interface, deterministic orchestration where practical, a documented policy layer, scoped tools, and an environment isolated from unrelated data and services. Claude Code’s Plan Mode can help a developer review an intended plan during development; it does not, by itself, provide runtime authorization or prove that a deployed claims application is safe.
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Which claims tasks are appropriate to start with?
Choose work where an adjuster can check the output against source material and where a mistake does not silently become a claim decision. The NAIC identifies damage assessment and repair-cost estimation using photos and historical data as areas where insurers use AI. That is not evidence that a particular system is accurate, or that it is ready to decide a claim.
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- Extract structured facts: identify dates, parties, amounts, and document references, then present them for verification.
- Summarize claim materials: produce a concise account of the submitted documents and cite the underlying records for each material fact.
- Surface damage evidence: organize relevant observations from photographs or other materials for an adjuster to assess.
- Route exceptions: flag missing evidence, conflicting documents, ambiguous facts, or requests that require an authorized person.
Keep the authoritative claim file and rules engine outside unverified model output. The model may help organize evidence; it should not turn its own generated text into the authoritative record.
What should the agent be allowed to decide or do?
Define authority in application policy and tool permissions, not just in instructions to the model. Separate read access from write actions, give the agent only the tools its task needs, and require human approval for consequential changes.
| Action or output | Suggested boundary |
|---|---|
| Extracting facts or summarizing documents | Allow as decision support; show source references so a claims professional can verify the result. |
| Updating claim status or other consequential record fields | Require an authorized human approval before the change is committed. |
| Communicating externally | Require review and approval before sending messages to a claimant, provider, or other outside party. |
| Coverage determinations or payment actions | Keep under authorized human control; do not let unverified model output execute the decision or transaction. |
Anthropic documents permission controls and Plan Mode for reviewing intended actions, while its zero-trust guidance describes managed policy controls and settings. Treat these as controls to configure, adapt to the application boundary, and test—not as proof that an application is safe by default. The insurer remains accountable for applicable legal compliance, fairness, accuracy, and consumer protection when AI supports decisions.
How should the system handle claim documents and prompt injection?
Treat submitted documents, emails, images, and extracted text as untrusted input. A document may contain irrelevant or adversarial instructions; the agent must not treat those instructions as policy or authorization. Anthropic identifies prompt injection and unintended agent actions as agent risks.
- Keep retrieved claim content distinct from trusted system policy and application rules.
- Constrain tool access, permitted destinations, and the data each tool can retrieve or change.
- Do not let document text grant new permissions or override approval requirements.
- Record relevant model decisions and tool calls so unexpected actions can be investigated.
These are engineering safeguards for the described threat model, not a guarantee that prompt injection can be eliminated. Test the actual workflow with adversarial and irrelevant content before expanding its access.
What should the audit trail contain?
Design traceability into the application record rather than relying on a transcript alone. For each task, capture enough to connect a generated result to its evidence, execution, review, and disposition.
- Claim or task identifier and references to the source documents used.
- Model, prompt, and configuration versions.
- Extracted facts or generated findings, along with the evidence supporting them.
- Tool invocations and policy outcomes.
- Human approvals, edits, or overrides, and the final disposition.
Protect logs against inappropriate access and set retention according to legal and business requirements. Anthropic’s Claude Code environment guidance describes OpenTelemetry metrics, audit logging, and configuration-change auditing; those environment capabilities do not automatically create claim-level traceability. The application must implement and validate its own link between model output and the claim record.
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How should the team evaluate and roll out the agent?
Do not expand authority because a few examples look convincing. The NAIC warns that generated answers can sound correct while being wrong; evaluation must check outputs against source evidence and behavior under difficult conditions.
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- Assemble representative, permissioned cases. Include ordinary claims as well as incomplete submissions, contradictory documents, edge cases, distribution shifts, and adversarial content.
- Measure task-specific behavior. Check extraction correctness, unsupported assertions, whether evidence references actually support the output, escalation behavior, and human override patterns.
- Run offline evaluation. Compare outputs with appropriately reviewed answers before connecting the agent to operational claim actions.
- Use shadow operation. Let the service process cases without changing claim records or communicating externally; compare its output with normal handling.
- Start bounded production assistance. Enable only the verified assistive task and retain approval gates for consequential actions. Reassess before adding tools or decision authority.
No validated performance threshold or deployment result for a proposed claims system is established here. Set acceptance criteria with claims, legal, actuarial, security, and privacy teams for the actual task and jurisdiction; do not treat this design outline as a validation result.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do privacy, retention, and deployment route affect the design?
Before sending claim data into development or inference, map the full data path: prompts, outputs, claim documents, local session transcripts, application logs, connectors, and any cloud service processing them. Confirm the processor, retention terms, and any zero-data-retention (ZDR) coverage for the exact product and route. Do not assume terms for one Anthropic product apply to Claude Code enterprise interfaces, consumer plans, third-party integrations, or cloud-hosted alternatives.
Anthropic’s API retention page says retained data is not used for model training without express permission and that conversation content is generally not retained by default, subject to exceptions; retention varies by feature. It also states that local Claude Code and Cowork session transcripts are stored for six years by default unless a finite custom organization retention period is configured. The page describes separate HIPAA-ready arrangements and ZDR coverage with exclusions. For Amazon Bedrock and Google Cloud’s Agent Platform, the cloud provider is the data processor, so its relevant retention and compliance documentation applies. Confirm current terms and applicability during implementation rather than generalizing these statements to another product or route.
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What insurance governance applies?
The NAIC says insurers remain responsible for compliance with applicable insurance laws, regulations, standards, and consumer-protection rules, including fairness and accuracy. State regulators may ask insurers to explain AI use in claims decisions. The NAIC Model Bulletin on the Use of Artificial Intelligence by Insurance Companies was adopted in December 2023. Its AI topic page also reports ongoing work on an AI Systems Evaluation Tool and a 12-state pilot as of March 2026, with possible adoption anticipated at the 2026 Fall National Meeting. That anticipated adoption should not be treated as completed without a later official update.
The NAIC’s AI topic page summarizes survey results issued across 2022–2025: 88% of 193 responding auto insurers, 70% of 194 responding home insurers, 58% of 161 responding life insurers, and 92% of 93 responding health insurers said they use, plan to use, or plan to explore AI/ML models in their operations. These are respondent figures, not a census of the insurance market, and they do not establish the effectiveness or readiness of any individual claims agent.
Regulatory applicability depends on jurisdiction and use case. A real deployment needs review against relevant state requirements and the insurer’s own obligations; the NAIC’s model guidance is not a substitute for that analysis.
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