Scope and permission templates
One scope file per agent: allowed tools, data, limits, approvers, and a review date.
GenAI & Agents
AI Agent Controls
Scope, permissions, logging, and approvals for AI agents.
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AI agents your security team can review and approve.
Get the AI Agent Controls overview
The problem it solves, what is included, how it works, the technical components, and how we adapt it with you.
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The problem
Agents take real actions. Security needs to see the limits.
An agent that can send email, update records, or queue payments needs the same controls as a new employee with system access. Most agent projects add those controls late, if at all.
What this accelerator does
AI Agent Controls give each agent a written scope, check every action against it, and let you switch any agent off at once. They are the controls we apply to the agents on our own team.
What's included
A written scope, enforced on every action.
Four parts, each adapted to your data, platforms, and controls.
Action logging and audit trail
A hash-chained log of every action, block, and approval, copied to Azure Monitor or your SIEM.
Human approval
High-impact actions wait for a named approver who is not the agent's owner.
Switch-off and limits
Stop one agent or all agents at once, with action and spending limits per run and per day.
You keep the scope files, the guard, the audit trail, and the switch-off and escalation runbooks.
How it works
Every tool call passes five checks.
Switched off?
A stop file for this agent or all agents, an environment switch, or a paused scope.
Prohibited?
A baseline list no scope can remove, such as changing its own permissions or disabling logging.
In scope?
The tool must be listed and every data resource allowed. Denied always wins.
Within limits?
Actions and cost, per run and per day.
Approved?
High-risk tools wait for a listed approver who is not the owner. No answer counts as a denial.
Logged either way
Success, failure, cost, and duration are recorded. Blocks go back to the model to explain.
Technical detail
Under the hood.
Vendor-neutral Python and configuration, Azure first, with tests included from the start.
- Guard
- Wraps the tool functions an agent calls, so it works with any framework
- Scope files
- Reviewed like code and stored where the agent cannot edit them
- Approvals
- A file queue by default, or routed to Teams, ServiceNow, Jira, or email
- Audit log
- JSON lines with a hash chain that shows edits, removals, and reordering
- Switch-off
- A stop takes effect on the agent's next tool call
- Operator CLI
- Pending, approve, deny, stop, resume, verify, and report
Proof in the package
Worked example: an accounts-payable agent that triages invoices.
The agent checks invoices against purchase orders. The demo walks through every control.
What happens
- Mismatched invoices are flagged; matched ones wait for payment approval
- Reading supplier bank details is blocked
- Releasing a payment is prohibited
- Changing its own permissions is refused
- Approval from someone off the list does not count
- After switch-off, every call is blocked
Result
Every event is in the audit log, and the hash chain verifies it is intact.
Where it fits
Platforms, related accelerators, and limits.
Works with
- LangChain, Semantic Kernel, AutoGen, the OpenAI and Anthropic SDKs, or your own code
- Azure managed identities and Azure Monitor
- Teams, ServiceNow, Jira, or email for approvals
Pairs with
- The AI Governance Starter records each agent and checks its scope file
- Enforces governance controls AIG-06, 07, 08, and 10
- GenAI Evaluation Tests check behavior before release
Assumptions and limits
- It controls the tools you wrap, so we pair it with platform permissions
- Cost limits use the costs you connect from your model provider
- Agents on several machines need shared state storage
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