AI agent governance

Govern AI agents from policy through execution

BlueBear connects identity, permissions, approvals, evidence, cost controls, and incident ownership so enterprise teams can govern what agents do—not merely document what they should do.

Discuss your governance model Understand the control plane

Governance must survive contact with a live agent

A policy document does not stop an agent from using the wrong credential, calling a restricted tool, or writing an unverified result. Operational governance places enforceable decisions in the path between a request and an action, while preserving enough evidence to explain what happened later.

Core capabilities

Identity and authority

Tie each session and tool request to a user, tenant, workspace, agent, and credential source. Separate the identity requesting work from the workload identity executing it.

Risk-tiered policy

Classify actions by impact. Low-risk reads can proceed automatically, while financial, destructive, regulated, or externally visible actions can require narrower permissions or review.

Human approval

Present reviewers with the proposed action, target system, relevant evidence, expected change, and expiration—not a context-free approve button.

Execution evidence

Record the policy decision, model and tool path, inputs and outputs, approval event, resulting system response, cost, and correlation identifiers needed for investigation.

Cost and capacity controls

Attribute model, compute, storage, and integration usage to the correct tenant and workflow. Apply budgets and limits before a runaway workflow becomes a billing incident.

Incident ownership

Define who can pause an agent, revoke credentials, preserve evidence, communicate impact, and authorize recovery when an automated workflow behaves unexpectedly.

A practical governance path for every agent action

BlueBear treats governance as an execution sequence. The exact controls vary by deployment and integration, but the accountability chain should remain intact.

  1. Step 1

    Establish context

    Resolve tenant, workspace, requesting user, agent, destination system, and permitted credential source.

  2. Step 2

    Evaluate policy

    Check action scope, risk tier, data boundary, budget, required approval, and deployment-specific restrictions.

  3. Step 3

    Execute within boundaries

    Dispatch only the allowed tool operation with request-scoped context and the minimum necessary credentials.

  4. Step 4

    Verify and preserve evidence

    Capture the destination response, confirm the intended state change, meter usage, and retain an investigation-ready record.

What this page does—and does not—claim

BlueBear provides control-plane, workspace, tenant, MCP, session, billing, and deployment mechanisms that can support an enterprise governance program. Governance outcomes still depend on the policies, integrations, cloud controls, retention settings, and operating procedures selected for a deployment.

This page does not claim that every deployment is automatically compliant with a named framework. Certifications, contractual controls, data residency, and audit scope must be evaluated for the specific customer environment.

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Enterprise AI agent security

Implemented isolation, credential, MCP, runtime, and disclosure boundaries.

AI agent audit trails

What an investigation-ready evidence record needs to contain.

Human approval design

Build approval steps around risk and reviewer context.

Incident response runbook

Contain, investigate, recover, and learn from agent incidents.