AgentMD and AI Governance

Why governance matters for agentic AI

Agentic AI systems can autonomously chain tasks, make decisions, and execute actions. That autonomy creates value—but also risk. As [IDC research](https://www.ibm.com/think/topics/agentops) notes, agentic workflows that change customer files, approve loans, or rate candidates can make mistakes with real consequences. Regulations like the EU AI Act are evolving to address these risks.

AgentMD is built for governed execution. We don't just watch what agents do—we run what they're *supposed* to do, with guardrails and permissions enforced at runtime.

How AgentMD supports governance

Guardrails — Declare constraints in YAML frontmatter. Examples: "Never modify production config", "Never merge without review", "Never access customer PII". AgentMD validates and enforces these at execution time.

Permissions — Explicit allowlists for shell commands, pull requests, and other resources. Default-deny with opt-in for specific commands. No more unbounded agent access.

Policies — In the Ops dashboard, define policy rules that block, warn, or require approval for agent actions. Scope by agent, repository, or workflow.

Audit — Full execution history and audit logs. Traceability for compliance and debugging.

Risk management in practice

• Sandboxed execution — Commands run in isolated environments with permission boundaries
• Output contracts — Define expected schema and quality gates; validate before completion
• Human-in-the-loop — Require approval for sensitive operations

Roadmap

We're planning features to support evolving regulations: EU AI Act risk classification, enhanced traceability, automated risk assessment, and integrations with governance platforms. See the [Governance Roadmap](/docs) in our docs.

Learn more

• [AgentOps (IBM)](https://www.ibm.com/think/topics/agentops) — Lifecycle management for AI agents; includes the IDC whitepaper on AI governance and agentic AI