Technical leaders · Scientific & technical
Unified governance for models, tools and AI agents
As systems become multi-model and multi-agent, cost, access, tracing and runtime policy must cover the full interaction chain.
In a modern architecture, one agent may use several models, APIs, MCP services and internal datasets. Controlling model selection alone is insufficient. An organization needs to know who initiated a request, which tools the agent used, what data moved and what each run cost.
Runtime control
Access policy, spend limits, model routing, traces and tool restrictions should be enforced through a shared runtime layer. This makes a policy reusable across vendors and frameworks and avoids reconstructing incidents from disconnected logs.
Implementation starts with an inventory of models, agents, tools and their business owners. For every use case, define a budget, permitted data, prohibited actions and alert thresholds. Governance is most effective when product teams can apply these rules automatically during development and release. The objective is not a static policy document; it is observable, testable control over how AI systems behave in production.