AI Agent Governance for Workers' Compensation Claims Adjudication

Why Claims Adjudication Introduces Distinct Governance Requirements

Workers' compensation claims adjudication involves tasks with direct financial and legal consequence: reviewing medical records, calculating benefit amounts, flagging suspected fraud, and drafting correspondence that may include denial rationale. When an AI agent performs or assists with these tasks, it is not simply retrieving information. It is taking actions inside systems that hold protected health information, benefit calculation logic, and case history tied to a specific claimant.

This matters for governance because the risk is not only incorrect output. It is unauthorized or unscoped action: an agent that can query more medical records than a given task requires, invoke a benefit calculation tool for a claim it was not assigned to review, or generate correspondence without a record of what data informed the decision. These are permissioning and enforcement problems, not accuracy problems, and they require controls that operate at the level of what the agent is allowed to do, not just what it produces.

Agent Identity: Separating the Agent From the Adjudicator

A foundational governance decision is establishing an agent identity that is distinct from the human adjudicator's credentials. If an agent acts under a shared or borrowed identity, it becomes difficult to determine, after the fact, which actions were taken by a person and which were taken by an automated process. This distinction is central to any later reconstruction of how a claim decision was reached.

Agent identity should be established at deployment and verified at runtime for every action the agent takes, not assumed for the duration of a session. This allows an organization to answer a basic but often unanswerable question in claims disputes: exactly which system, human, or agent performed a specific step in the adjudication process.

Scoping Tool-Call Permissions to the Task, Not the Role

AI agents integrated into claims systems typically need access to multiple tools: medical record repositories, benefit calculation engines, fraud-flagging models, and correspondence generation systems. A common failure mode is granting an agent broad, role-based access to all of these systems rather than narrow, task-based access scoped to what a specific adjudication step requires.

Over-permissioning creates exposure that is disproportionate to the task. An agent assisting with benefit calculation does not need standing access to the full medical record repository. An agent flagging potential fraud does not need write access to correspondence generation. Least-privilege design for claims agents means defining permission sets at the level of individual tool calls and data categories, tied to the specific step in the adjudication workflow the agent is executing, and revoking that access once the step is complete.

Runtime Enforcement Versus Design-Time Configuration

There is an important distinction between configuring permissions at design time and enforcing them at runtime. Design-time configuration defines what an agent should be allowed to do. Runtime enforcement determines what actually happens when the agent attempts an action, including whether an out-of-policy tool call is blocked in the moment rather than discovered later in a log review.

For claims adjudication, this distinction has direct consequences. An agent that is correctly scoped on paper but not actively constrained at runtime can still attempt an out-of-scope action if its configuration is misapplied, if a workflow changes without a corresponding permission update, or if the agent's own reasoning leads it to invoke a tool outside its intended task. Runtime policy enforcement intercepts these attempts as they occur, which is a materially different control than reviewing what happened after a decision has already reached a claimant.

Audit Trails for Contested Claims

Workers' compensation claims are frequently contested, appealed, or reviewed under state dispute-resolution procedures. When an AI agent has influenced a decision, whether a benefit calculation, a fraud flag, or a denial recommendation, the record of that involvement becomes part of what may be examined in a dispute.

An audit trail sufficient for this purpose needs to capture more than a final output. It should show which agent identity acted, what data it accessed, which tools it invoked, what permissions governed those actions, and whether any action was blocked or modified by policy enforcement. Producing this record as a byproduct of runtime governance, rather than reconstructing it after the fact from disparate system logs, reduces the risk of gaps that become apparent only when a decision is challenged.

Operational Practices for Governing Claims Agents

• Scope tool access per adjudication task rather than per agent role
• Treat agent identity as distinct from and traceable to the human adjudicator it supports
• Enforce policy at the point of tool invocation, not only through periodic review
• Generate audit records automatically as part of each agent action, not as a separate reporting step
• Review and revoke standing permissions as claims workflows change
• Test enforcement mechanisms against out-of-policy scenarios before deployment, not only in normal operation