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Use Claude Code to help engineer and maintain a claims-support application—not as the application that adjudicates claims. Deploy a separate service with a narrow, verifiable task, limited tool permissions, an auditable record, and human approval for consequential decisions. Keep claim evidence and authoritative policy rules outside the model’s unverified output.
What Claude Code should—and should not—do
Claude Code is an engineering assistant for building software. The production claims agent should be a separately deployed application with its own model interface, orchestration, tools, policies, and runtime environment. Anthropic’s April 9, 2026 guidance on trustworthy agents describes an agent as four components: model, harness, tools, and environment. Each component can add capability and create risk; securing only the prompt or model leaves the rest of the system unaddressed.
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For an initial release, use the model to organize evidence for an authorized claims professional rather than decide coverage or payment. Examples of bounded assistance include extracting dates, parties, amounts, and document references; summarizing claim documents with references back to the records; identifying evidence relevant to damage assessment; and flagging missing, ambiguous, or conflicting information for review. The authoritative claim file and applicable rules should remain in controlled systems, not be replaced by generated text.
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1. Define one task and its authority boundary
Write down what the agent may produce, who will use the result, and what it must not decide or do. A useful first task has an answer an adjuster can check against source documents. For example, an extraction task can return a field, its value, the document reference supporting it, and an uncertainty or missing-evidence status. A summary should distinguish what a document says from what the system infers.
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The National Association of Insurance Commissioners (NAIC) identifies estimating repair costs and assessing damage from photos and historical data as areas where insurers use or explore AI. That does not establish the accuracy, suitability, or readiness of any particular system, nor does it provide a performance threshold for your application.
2. Keep orchestration and records authoritative
Use deterministic application logic where practical to select the workflow, validate inputs and outputs, enforce policy checks, and decide when to stop or escalate. Treat model output as a proposed result, not as an authoritative update to a claim. Store accepted facts and final dispositions in the system of record through controlled application logic; preserve references that let a reviewer inspect the underlying evidence.
3. Route uncertainty to a person
Escalate when evidence is missing, documents conflict, the model cannot support a field with a source, or the request concerns coverage, liability, claim denial, settlement, or payment. A confidence score alone is not proof that an answer is correct. Set escalation rules around the task and its consequences, then verify those rules using representative cases before deployment.
What should an AI claims agent be allowed to decide?
Separate reading information from changing claim state. Give each workflow only the tools it needs, and require authorized human approval before consequential changes. Do not rely on a prompt telling the model to behave: enforce access and approval rules in the application and deployment environment.
| Activity | Suggested boundary | Reason |
|---|---|---|
| Extract or summarize claim evidence | Allow as decision support, with references to source records and review for material use. | Generated claims can sound plausible while being wrong; the source remains the basis for verification. |
| Flag missing or contradictory information | Allow the agent to identify and route issues; have an authorized person resolve material conflicts. | The agent can help surface evidence gaps without treating its interpretation as final. |
| Change claim status or send an external communication | Require an explicit approval step and a scoped, auditable action path. | These actions can affect a customer or the claim record. |
| Determine coverage, deny a claim, or authorize payment | Keep the decision with an authorized human or governed process; do not delegate it to an unverified model response. | These are consequential decisions with legal and consumer-protection implications. |
Claude Code Plan Mode can present an intended plan for review. Anthropic also describes permissions and policy controls for managing agent actions. These can inform development and operational controls, but they do not prove that a separately built claims application is safe by default. Implement and test authorization at the application boundary, including what happens when a tool call is refused, incomplete, or outside the permitted scope.
How to protect the system from hostile or irrelevant claim content
Claim documents, emails, images, and attached text are untrusted input. They may contain irrelevant instructions or adversarial prompt-injection content. A document should provide evidence for the task, not gain authority to change system policy or issue commands to connected tools.
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- Keep retrieved claim content separate from trusted system instructions and application policy.
- Limit tool permissions to the specific records and actions required for the workflow.
- Constrain where tools can send data and what systems they can change.
- Validate tool inputs and model outputs in application code before taking action.
- Log relevant retrieved material references, decisions, and tool calls so a reviewer can reconstruct what happened.
These safeguards are engineering responses to the risks of prompt injection and unintended agent actions identified in Anthropic’s agent guidance. They reduce exposure; they do not establish that an application is immune to attack.
What an auditable claims record should contain
Design traceability into the application rather than expecting a model transcript to serve as a complete audit trail. For each task, capture the information needed to connect the generated result to evidence, system behavior, review, and disposition.
- Claim or task identifier, with access restricted to authorized users.
- Model and prompt or configuration versions used for the run.
- References to source documents and the extracted facts or summaries produced.
- Tool invocations, relevant results, and policy checks or outcomes.
- Human approvals, edits, overrides, and the identity or role responsible for them.
- The final disposition recorded in the claims system.
Protect logs from inappropriate access and set retention according to applicable legal and business requirements. Anthropic’s Claude Code enterprise guidance describes environment-level observability and audit capabilities, including OpenTelemetry metrics, audit logging, and configuration-change auditing. Those controls do not replace application-level claim traceability; design and validate that record separately.
How to evaluate the system before expanding its authority
Do not treat a successful demonstration as validation for production claims work. Build an evaluation set from representative data that the organization is permitted to use, and assess both the quality of outputs and the system’s behavior when it should not answer or act.
- Offline evaluation: Test extraction correctness, whether summaries are supported by cited evidence, unsupported assertions, and escalation behavior. Include incomplete claims, contradictory documents, edge cases, and adversarial document content.
- Shadow operation: Run the system alongside the established workflow without allowing it to make claim changes. Compare outputs with human-reviewed outcomes and investigate systematic errors or uneven performance.
- Bounded production assistance: If evaluation and governance reviews support it, enable only the narrowly defined assistance task. Keep consequential actions behind the approval boundary.
- Ongoing review: Monitor error patterns, human edits and overrides, tool use, and changes in incoming data. Re-evaluate when the model, prompt, tools, policy, or operating environment changes.
These are recommended engineering practices, not reported test results for a particular claims system. The NAIC and Anthropic materials cited here do not establish validated thresholds or certify that a proposed application is accurate enough for a given use.
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How to handle claim data and retention
Map the complete data path before using production claim information: the Claude product or model endpoint, any cloud provider or integration, the application, logs, and connected claim systems. Identify which entity processes each category of data and what retention terms apply to that exact route. Do not assume that terms for one Anthropic product or deployment route also apply to another.
Anthropic’s API retention documentation says data retained through the API is not used to train models without express permission, and that conversation content is generally not retained by default, subject to exceptions and feature-specific retention models. The same page states that local session transcripts from Claude Code and Cowork are stored for six years by default unless a finite custom organization retention period is configured. It describes separate HIPAA-ready arrangements and zero data retention (ZDR) coverage, with exclusions. These statements are product- and feature-specific; confirm current terms, eligibility, configuration, and exclusions before handling claim data.
For deployments through Amazon Bedrock or Google Cloud’s Agent Platform, Anthropic says the cloud provider is the data processor, so the provider’s corresponding retention and compliance documentation also applies. Review the entire processing path rather than treating an Anthropic API statement as a blanket guarantee for cloud-hosted alternatives, Claude Code interfaces, consumer plans, or third-party integrations.
Anthropic’s enterprise page lists administration capabilities such as role-based access, audit logs, a Compliance API, OpenTelemetry, custom retention, customer-managed encryption keys, and connector governance. Availability depends on the product, account, and configuration. Having access to a control does not by itself establish compliance with insurance requirements.
What insurance governance requires
The NAIC says insurers remain responsible for compliance with applicable insurance laws, regulations, standards, and consumer-protection rules when using AI, including fairness and accuracy. State regulators may ask insurers to explain AI use in claims decisions. Its Model Bulletin on the Use of Artificial Intelligence by Insurance Companies was adopted in December 2023. The NAIC’s AI topic page also reported ongoing work on an AI Systems Evaluation Tool and a pilot involving 12 states as of March 2026, with possible adoption anticipated at the 2026 Fall National Meeting. That was an anticipated milestone, not confirmation of adoption; check for a later official update before relying on a current status.
NAIC survey results issued across 2022–2025 and summarized on its AI topic page show that 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 percentages describe the survey respondents, not the entire insurance market, and combine current use with plans to use or explore.
For a real deployment, involve legal, actuarial, claims, security, and privacy reviewers in defining the permitted use and controls. Regulatory applicability depends on jurisdiction and use case; a general architecture guide cannot determine the obligations for a particular insurer or claim workflow.
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