AI agent governance: control what agents can access, decide and change

AI agent governance is the framework of technical controls, identity boundaries, runtime policies, and human approval gates that define what autonomous AI agents can access, decide, and change across enterprise systems.

Model governance evaluates model training data, weights, bias, and prompt-completion safety. AI agent governance addresses execution: the tools an agent invokes, the enterprise data it retrieves, the financial or system changes it proposes, and the verifiable human approvals required before execution.

When an agent encounters an action requiring escalated authority (such as issuing funds, modifying production infrastructure, or communicating with customers), the control plane pauses execution, prepares an evidence packet with reasoning and proposed payloads, and routes the action to a designated reviewer.

SR 26-2, issued on 17 April 2026 by the Federal Reserve, the OCC and the FDIC, supersedes SR 11-7 and SR 21-8 and explicitly places generative and agentic AI outside its scope. That leaves the governance question open for agents. Moring establishes tamper-evident decision records, model input/output lineage, deterministic boundary policies, and scoped tool access, so a bank can evidence what an agent ran, which policy allowed it and who approved it.

A governed AI agent is an autonomous agent that runs inside an enforced boundary: a registered identity, a scoped permission set, an action-level policy it cannot override, a human approval gate for irreversible steps, and a decision record for everything it did. Governed AI agents differ from ungoverned ones in that the boundary is architectural, not a prompt instruction the model may ignore.

An AI agent registry is the record of every agent in production: the workflow it serves, the systems it can reach, the accountable owner, its risk tier and its current authority level. Without a registry there is no complete inventory to review, no owner to call, and no way to revoke access when the workflow or the risk changes. In Moring, registry entries are the source of truth that identity, policy and approvals resolve against.

An AI agent audit trail records, for every run: the agent and owner, the trigger, every prompt and model call, each retrieved document, every tool or MCP invocation with its parameters, the policy decisions taken, any human approval with the approver identity, and the resulting action. Records are written as tamper-evident decision records so an internal auditor or a regulator can reconstruct the run without asking the model what it did.

Cost observability means attributing model, token and tool spend to a named agent, workflow, cost centre and owner rather than to a single shared API key. Moring applies budgets and velocity limits per agent, stops runaway loops at a hard step or spend ceiling, and reports spend by workflow so an agent that no longer earns its place can be paused.

Yes. The NAIC AI Model Bulletin, adopted in a majority of states, expects an insurer to document an AI system and the controls around it at the level of the decision it supports. Moring governs claims, underwriting and servicing agents on that basis: each agent is registered to a use case and owner, decisions carry policy checks and evidence, and human review is recorded where the workflow requires it.

Yes. The Moring control plane acts as an enforceable gate between agents and models (via AWS Bedrock, Azure, or private deployments) as well as Model Context Protocol (MCP) servers, inspecting every call and parameter before execution.

Deterministic stop conditions, step budgets, token velocity limits, and automated circuit breakers halt rogue loops instantly. Operators can immediately pause or kill any session with one click, revoking short-lived access tokens.