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AgentGov
AI Agent Governance Layer
Controls autonomous AI agents through permissions, risk scoring, approvals, auditability and kill switches.
Problem
Autonomous agents can gain tool access, take actions and interact with systems without sufficient governance — most teams find out an agent overstepped after the fact, not before.
Who it's for
- Companies running AI agents against real tools and data
- Teams that need an approval and audit layer before widening agent permissions
Current status
System Prototype — control-plane logic and an operational demo dashboard (agents, risk scores, audit events, kill-switch controls) built and demonstrated internally. Not yet running in a production agent deployment.
Capabilities
Agent identityTool permissionsRisk scoringPolicy enforcementHuman approvalQuarantineTamper-evident audit logsKill agentKill jobKill tenant
What it coordinates
- Agent identity and permissions
- Risk scoring per action
- Approval routing
- Quarantine and kill switches
Human control points
- Actions above a risk threshold pause for human approval
- Any agent, job, or tenant can be killed immediately
- Quarantine isolates a single agent without shutting down the whole system
Integrations
- LLM APIs
- Internal tool and action APIs
- Audit log storage
Operational safeguards
- Tamper-evident audit trail
- Policy enforcement at the tool-call level
- No standing broad permissions — access is scoped per task
Have a process that still depends on spreadsheets, inboxes and manual follow-ups?
Show us the workflow. We'll map where orchestration could remove friction without removing control.