Agent approval controls: setting limits for long-running AI agents
governance
How to decide what AI agents can do alone, what needs sign-off and how limits keep long-running work safe and reviewable.
Agents that work for weeks need clear boundaries. Approval controls let you give them room to run while keeping people responsible for the decisions that matter.
Why limits matter for long-running agents
An agent that runs for days can take many actions without anyone watching. Limits make sure those actions stay inside what you intended, and approvals make sure a person sees the important ones first.
The three kinds of control
- Scope: which data and tools an agent can reach
- Limits: what it may do alone, such as drafting, researching or tagging
- Approvals: actions that wait for a person, such as sending messages or changing records
How to choose where approvals go
Start strict and loosen as trust builds. Require sign-off for anything external, costly or hard to undo. Let agents act freely on internal research and preparation.
What reviewers see
Each step is traceable and each answer is cited, so a reviewer can check the reasoning rather than take it on faith. The video on agent governance shows the setup in practice.
Self-correction inside the limits
Constella Agents check their work against what they learn and adjust course, all within the boundaries you set. What they learn stays inside your boundary.
Fit for larger organizations
Teams with stricter requirements can pair these controls with Constella for Enterprise. Full details are on the Trust and Controls page.
Start free at Constella and set your first limits.