AI agent governance insights.

OneTrust governs the enterprise AI estate from the GRC office — inventory, risk templates, monitoring, and policy guardrails including MCP enforcement. Execlave enforces in-process on every agent action with a hash-chained, offline-verifiable evidence trail, self-hosted on every tier. Different centers of gravity — where each is stronger, and how to choose.

Rubrik Agent Cloud pairs agent oversight (SAGE, Agent Inventory) with Agent Rewind — snapshot-based undo of unintended agent actions. Execlave prevents disallowed actions in-process before they execute and produces cryptographic compliance evidence. Undo vs prevent-and-prove — where each is stronger, and how to choose.

Gravitee extends API management to AI agents: an Agent Gateway (A2A and LLM proxies), Agent Catalog, and MCP Tool Server govern agent traffic at the network layer. Execlave enforces policy in-process on each agent action and produces a hash-chained compliance evidence trail. A gateway sees traffic; an in-process layer sees intent and context — where each is stronger, and how to choose.

AI agent governance enforces what agents may do; observability records what they did. How they differ, where they overlap, and why you need both.

The seven steps from idea to production agent: scope one task, pick a framework (LangChain, OpenAI Agents SDK, CrewAI — or none), design narrow tools, add guardrails in the request path, wire in governance and audit trails, test adversarially, deploy with monitoring and a kill switch. Working code included.

As teams move from one agent to dozens, the AI Agent Management Platform (AMP) has emerged as the control plane for agents in production: registry and lifecycle, tiered autonomy, runtime enforcement, real-time cost controls, permission-drift detection, and data-access lineage. What an AMP is, the six controls it includes, and how it differs from prompt security and governance-program tools.

Governance program management vs runtime enforcement. Credo AI inventories, assesses, and documents your AI estate. Execlave blocks disallowed agent actions in the request path. Source-cited deltas, and why large orgs often run both.

Monitoring vs enforcement. Invariant focuses on async detection and alerting. Execlave focuses on sync policy enforcement and compliance evidence. Here are the real deltas, and why production systems often need both.

Prompt security vs runtime governance. Lakera Guard excels at detecting adversarial inputs. Execlave enforces policies on agent actions. Here are the real deltas, and when to use both.

Step-by-step tutorial: Add policy enforcement, tracing, and compliance logging to any LangChain agent in under 10 minutes. Python and TypeScript examples with real copy-paste code.

You have four months. Article-by-article engineering checklist for the EU AI Act: the three deadlines that matter, the five questions auditors actually ask, and the eight controls to ship first.

Microsoft released the Agent Governance Toolkit as open-source, MIT-licensed software on 2 April 2026. Here are the real, source-cited deltas between it and Execlave, written honestly — including where Microsoft is stronger.

Your auditor has never reviewed an AI agent system. Here is what SOC 2 Trust Services Criteria actually require of agent workloads, the four evidence artifacts auditors ask for, and a 90-day readiness plan.

Prompt injection is the top security risk for autonomous AI agents — and a problem the model cannot solve on its own. Why detection alone is insufficient and how runtime policy enforcement makes injections irrelevant.

AI agent governance is the set of policies, runtime enforcement mechanisms, and audit controls that decide what an autonomous AI agent can do — and prove what it did. How it differs from GRC platforms and AMPs, the 5 pillars, a 7-capability evaluation checklist, and what the EU AI Act requires.