Credo AI vs Trussed for Insurance: Documentation vs Runtime Enforcement
Credo AI is a documentation-first governance platform that produces policy maps, risk assessment scores, and compliance artifacts. Trussed provides runtime enforcement that governs AI agent behavior during live operation, including tool calls, data access, and permissions. Insurance carriers generally need both: compliant governance records and enforced controls over what deployed agents actually do.
Documentation-based governance and runtime enforcement address different points in the AI lifecycle. Documentation records intended behavior for audit and review; runtime enforcement controls what an agent actually does, in real time, while it operates. Insurance carriers evaluating governance platforms should determine whether one, or both, are required to meet regulatory and internal risk expectations.
Two Governance Layers, Two Failure Modes
Documentation and runtime enforcement solve different problems. Understanding the distinction is the starting point for evaluating either approach for insurance AI agents.
Documentation Governance
Policy mapping, risk scoring, and compliance artifacts aligned to NAIC and NIST framework structure.
Runtime Enforcement
Live control of agent permissions, tool calls, and data access during operation.
Insurance AI Agents
Underwriting automation, claims processing, and policy servicing tools that require both records and enforced controls.
Where the Gap Appears in Insurance AI Agent Workflows
Insurance AI agents increasingly operate across underwriting automation, claims processing, and policy servicing, each involving real-time data access and tool invocation. An underwriting agent may query external data sources and internal risk models during a live session. A claims processing agent may need to access policyholder records, adjust reserve estimates, or call adjacent systems to validate a claim. A policy servicing tool may modify account records or issue disclosures based on agent-generated recommendations.
Documentation artifacts describe how these agents are intended to behave, but they do not constrain what the agent actually does during a live session. If an agent calls a tool outside its documented scope, or queries a data source not listed in its approved permissions, a compliant risk assessment record does not detect or prevent that action at the time it occurs. Closing this gap requires a control that operates at the point of agent action, not only at the point of review.
Questions to Evaluate Before Choosing a Governance Approach
- Does current governance documentation reflect actual system behavior at runtime, or only intended behavior approved at time of review?
- Which regulatory obligations require operational evidence, such as logs, versus static documentation artifacts?
- Can the governance platform demonstrate that an agent's live tool calls and data access matched its documented policy?
- What is the process for reconciling a documentation-based risk assessment with an incident where an agent acted outside its intended scope?
- Is a runtime control layer needed in addition to documentation tooling to meet both regulatory expectations and internal risk tolerance?
In This Comparison
- What documentation-first governance covers
- What runtime enforcement covers
- Where insurance regulation currently sits
- Documentation governance vs runtime enforcement
Frequently Asked Questions
Does Credo AI provide runtime enforcement for AI agents?
Published Credo AI platform materials describe the product in terms of policy mapping, risk assessment, and documentation generation. Available evidence does not describe runtime technical enforcement mechanisms, such as tool-call blocking or live permission checks, as part of that platform.
Does NAIC regulation require runtime enforcement technology?
No. The NAIC Model Bulletin directs insurers to maintain AI governance programs covering risk management, documentation, and vendor oversight. It does not prescribe specific runtime enforcement mechanisms, leaving implementation of real-time controls to the carrier.
Can documentation-based governance and runtime enforcement be used together?
Yes, in principle. Documentation platforms and runtime enforcement layers address different points in the AI system lifecycle, one covering design-time and audit records, the other covering live agent behavior. Carriers should verify how any runtime layer integrates with existing documentation workflows before adoption.
Evaluate Runtime Governance for Insurance AI Agents
See how runtime enforcement complements documentation-based governance for underwriting, claims, and policy servicing agents.
Explore Runtime Governance