AI agent permission map
Seven quick questions that map which actions an AI agent may take in your business — what is permitted now and what should stay behind human approval. The result is directional, not a verdict — we use it as a starting point for a conversation about where you can safely let an agent act.
What the map looks at
- Which systems an agent may read versus write, and where it may act
- Logging and audit trail — where you see what the agent has actually done
- Approval — who gets to decide that an agent writes or changes something for real
- Safety net — kill switch, rollback, and how you catch an agent overstepping
- The team's ability to review and adjust permissions day to day
- Permissions need inventorying
- The basics need to land first: which systems the agent may read, where it may write, and who approves. Most teams start here, and a Mindset workshop draws the permission map before you build.
- Approval gaps to close
- You have a sense of the action surface but approvals and logging are not fully in place. A sandbox pilot sets the permissions in a scoped environment and lets you sign off on every write before anything touches real systems.
- Ready for sandbox
- You have a read and write map, logging, and approval chains. The next step is reviewing the permissions on their way into production — access, kill switches, and approval points before the agent goes live.
- Controls are there — but the team needs enablement
- Governance and approval chains are in place, but what decides whether the permissions hold is whether the team can review and tighten them themselves. Skillforging equips your people to own the agent permissions.
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