Governing Multi-Agent AI Systems: Policies, Approvals, and Audit Trails

Multi-agent systems are becoming common. CrewAI crews, LangChain agent teams, and MCP-connected assistants work together. But who controls what each agent can do? The governance problem Without governance, any agent can do anything. A compromised or misbehaving agent can access data it should not touch. Policy-based access control from asqav import Asqav client = Asqav ( api_key = " sk_... " ) client . create_policy ( name = " no-delete " , action_pattern = " data:delete:* " , action = " block_and_alert " , severity = " critical " ) Multi-party authorization For critical actions, require multiple approvals: group = client . create_signing_group ( name = " payment-approvers " , required_approvals = 2 ) client . add_entity ( group_id = group . id , user_id = " cfo@company.com " ) client . add_entity ( group_id = group . id , user_id = " cto@company.com " ) Complete audit trail Every action across all agents is recorded with quantum-safe signatures. Links SDK Docs MCP Server