Insurance AI Governance Checklist for Managing General Agents and MGAs

Why MGA AI governance must be operational, not only policy-based

Managing General Agents and MGAs are applying AI systems and AI agents across underwriting, claims, servicing, distribution, and operations. In those workflows, governance cannot rely only on static policy documents. The control model needs to work while the AI system operates, especially when an agent interacts with underwriting files, claims documents, policyholder communications, broker submissions, or carrier systems.

Operational governance means each AI action is attributable, policy-aware, and reviewable. It also means the organization can show how the use case relates to delegated authority, carrier requirements, consumer impact, and retained evidence. The checklist below translates that operating model into controls that can be tested and maintained.

Core MGA AI governance control areas

The following control areas summarize the governance foundation for AI systems and AI agents used by an MGA.

The insurance AI governance checklist for MGAs

Use this checklist to review whether AI systems and AI agents are governed consistently across underwriting, claims, servicing, distribution, and operations.

• Accountable ownership: name the business, technical, compliance, security, and vendor owners for each AI system or agent.
• Complete inventory: maintain an inventory of AI systems, AI agents, models, vendors, tools, and connected workflows.
• Risk classification: classify AI use cases based on workflow context, consumer impact, delegated authority, and required oversight.
• Least-privilege access: limit AI agents to the data, systems, tools, and actions required for the approved use case.
• Runtime policy enforcement: apply controls while the AI system operates, not only before deployment.
• Tool-call governance: define which tools an agent can use, when approval is required, and what actions must be blocked or escalated.
• Monitoring: monitor AI behavior, exceptions, output patterns, and operational risk signals.
• Audit logging: retain logs that show prompts, model versions, retrieved data, tool calls, outputs, approvals, exceptions, and incidents.
• Vendor oversight: evaluate AI vendors, infrastructure providers, delegated partners, and carrier relationships for governance readiness.
• Change management: review changes to models, prompts, tools, permissions, data sources, and workflows before they are introduced.
• AI-specific incident response: define how AI-related failures, unauthorized actions, incorrect outputs, and control exceptions are reported, investigated, and resolved.
• Carrier and delegated authority mapping: map each AI use case to delegated authority, carrier requirements, consumer impact, and evidence retained over the previous 12 months.

Runtime control architecture for MGA AI agents

MGA AI agent governance should be designed around controlled execution. The agent should not be treated as a trusted employee with broad system access. It should be treated as a software identity with defined permissions, monitored behavior, and explicit limits on the tools it can use. This is especially important when the agent interacts with underwriting files, claims documents, policyholder communications, broker submissions, or carrier systems.

A practical architecture uses a centralized governance layer or control plane to register models and agents, define policies, map identity, approve tool access, capture logs, and export evidence. The goal is not to slow every workflow. The goal is to make each action policy-aware, attributable, and reviewable. For example, an agent may summarize a submission without human approval, but require approval before recommending a decline reason, altering a quote field, sending a customer communication, or creating a claims task.

Third-party AI and delegated authority oversight

Third-party AI oversight should cover AI vendors, infrastructure providers, delegated partners, and carrier relationships. For an MGA, this is tied closely to delegated authority. Each use case should be understood in the context of the authority granted, the carrier requirements that apply, and the potential consumer impact of the AI-assisted workflow.

Vendor oversight should not stop at procurement. The same inventory, permission, monitoring, logging, and change management expectations should apply when AI capabilities are provided by third parties or embedded inside operational systems.

Evidence to maintain for the previous 12 months

Auditability is a central part of the checklist. Evidence should show what the AI system was allowed to do, what it actually did, who approved exceptions, and how issues were handled. For each governed use case, retain evidence showing approvals, prompts, model versions, retrieved data, tool calls, outputs, exceptions, and incidents.

A practical governance workflow

1. Register the AI system or agent

   Record the owner, purpose, connected workflow, model, vendor, data sources, and tools.

2. Classify the use case

   Map the use case to delegated authority, carrier requirements, consumer impact, and operational risk.

3. Define runtime policy

   Set permissions, data-access boundaries, tool approvals, escalation rules, and blocked actions.

4. Monitor execution

   Track prompts, retrieved data, tool calls, outputs, approvals, exceptions, and incidents while the system operates.

5. Export and review evidence

   Maintain reviewable evidence for the previous 12 months and update controls through change management.

How Trussed AI fits into the control model

The control model described here depends on a governance layer that can register models and agents, define policies, map identity, approve tool access, capture logs, and export evidence. For MGAs deploying AI agents, the assessment should focus on whether runtime governance covers identity, permissions, tool governance, monitoring, and audit evidence.

Trussed AI is presented in this page as a resource for AI agent security and runtime governance. The commercial next step is secondary to the checklist itself: first determine where agents operate, what authority they have, which tools they can call, and what evidence is available for review.