The Governance Problem in Multi-Agent Systems
Multi-agent systems distribute responsibility across a network. Governed execution keeps authority, evidence, approvals, and accountability intact as work moves.
Tag
Risk
6 articles on this topic.
Are AI agents safe for business? What governance actually changes
The honest answer to whether AI agents are safe is: only when they are governed. Scope, approvals, and audit trails are what turn an autonomous agent into one you can trust.
Keeping agent autonomy inside the lines: a technical look at governed execution
Open protocols are solving how agents reach tools. The harder problem is deciding what an agent may do, proving what it did, and stopping it mid-run. Here is how we think about that.
Approvals are a feature, not a friction
Approval is often treated as the thing slowing automation down. In business AI execution, it is the mechanism that lets more work move without pretending every action has the same risk.
Audit trails for AI work should be built before the work runs
An audit trail is not a transcript dump. For autonomous AI work, it should show the goal, plan, tools, evidence, decisions, approvals, and final outcome in a way humans can reconstruct.
A practical readiness checklist for business AI agents
A readiness checklist for teams evaluating AI agents: start with ownership, system access, approval thresholds, audit requirements, and the operational workflows where autonomy is actually useful.