See what your agents did, which data and tools they used, and where a person approved or changed the outcome. We build that control layer into the system from the start.
Why Governance Is Different for Agents
Software governance already covers design, code, deployment, and operations. Agent governance adds controls for model outputs, tool use, runtime decisions, approvals, and changes in behavior as data and models change.
Your team needs to know both what was deployed and what the system is doing in the workflow now.
Governance Framework
Policy-as-Code
Express permissions and operating rules in machine-readable files. Version and review them with the application, then apply them where the agent reads data, uses a tool, or requests an action.
Decision Audit Trails
Keep reviewable records for important decisions, tool calls, outputs, approvals, and overrides. Logging is designed to preserve useful evidence without exposing more sensitive data than your team needs.
Compliance Evidence Generation
Collect and organize evidence for the SOC 2, HIPAA, FedRAMP, or industry controls your system must support. Your control owners can review coverage, operation, retention, and gaps from one clear workflow.
Identity & Authorization
Give each workload a least-privilege identity and use managed secret storage for sensitive credentials. Authorization rules define which data and tools each agent may access.
Human-in-the-Loop Escalation
Define when the workflow stops and asks a person to decide, using signals such as confidence thresholds, financial limits, sensitive data, and unusual conditions.
AWS-Native Governance Stack
- AWS IAM for agent identity
- AWS CloudTrail for action logging
- Bedrock AgentCore for session isolation and credential management
- Bedrock Guardrails for content and safety policies
Deploy the governance layer around your AWS identity, networking, encryption, and data controls. Before launch, we map each service and integration so your team knows where data is processed and who can access it.
Make Approval and Operation Easier
Before launch, security, risk, compliance, and business owners need a clear view of permissions, approvals, monitoring, stop and recovery behavior, and retained records. A shared control plan gives each team something concrete to review.
Start with a bounded workflow. Expand its permissions or reach only after testing and operating evidence support the change.
Frequently Asked Questions
AI agent governance is the set of policies, controls, owners, and operating records used to limit what an agent can do, review important decisions, and understand how the system behaved.
Software governance already covers design, code, deployment, and operations. Agent governance adds controls for model outputs, tool use, runtime decisions, approvals, and changes in behavior as data and models change.
Policy-as-code expresses permissions and operating rules in machine-readable, version-controlled files. Teams can review those policies with the application code and apply them at the points where the agent reads data, uses a tool, or requests an action.
Tactical Edge maps agent controls and evidence to the SOC 2, HIPAA, FedRAMP, or industry requirements that apply to your system. Your security and compliance teams can review how each control is configured, who owns it, and what evidence it produces as part of their normal approval process.
Build Governed Agents