FIELD NOTES
Controlling agents in production
Clear guidance for teams building agent workflows and connecting them to real tools, data, and business systems. Learn how to control actions, supervise execution, inspect outputs, and recover when work goes off course.
- Agent controls
What is an agent control plane?
An agent control plane sits between AI agents and the systems they use. It checks identity, permissions, actions, execution, outputs, and recovery before an agent can create business risk.
- Agent controls
A practical permissions model for AI agents
Treat every agent like a service identity with an owner, role, scoped access, approval thresholds, and a tested recovery path.
- Agent controls
Agent action, execution, and output controls are different
A clear framework for controlling what an AI agent may do, how the work runs, and what result can be released.
- Regulatory
If AI model access becomes a policy risk, your rollout needs an access register
Reuters reports that Beijing is considering overseas access limits for advanced Chinese AI models. For mid-size companies, the practical control is not geopolitical prediction. It is knowing which teams, tools, and business actions depend on which models.
- Privacy
Copilot stores prompts in Exchange mailboxes — what mid-size compliance teams should verify
Microsoft 365 Copilot interactions are retained in user mailboxes and searchable via eDiscovery when configured. Here is what privacy and compliance leads at 50–1,000 employee organizations should check before scaling rollout.
Looking for implementation patterns? Browse the guides.