The problem
Autonomous agents don’t have judgment. They have instructions.
How it works
One action, one evaluation. Every time.
Hoop evaluates every action the agent takes, individually, in real time. The agent gets maximum autonomy for safe operations and zero autonomy for dangerous ones. The boundary is defined by your policies, not by time windows or static roles.
The agent can query, inspect, and analyze. Sensitive data is masked in responses. The agent gets the information it needs without ever seeing raw PII, credentials, or secrets.
Each write operation is evaluated against guardrail rules. Low-risk writes pass through. High-risk writes route for human approval via Slack or Teams. The human sees the exact command and approves or denies.
No exceptions. The agent cannot execute commands that match your guardrail patterns. DROP TABLE, delete namespace, rm -rf. None of these reach the target system.
For operations that need human oversight, Hoop sends a notification. The approver sees the exact command and approves or denies. One action, one decision. Not a 30-minute access window.
Agent observability
Every agent action. Logged, scored, reviewable.
Full session recording for every agent. The data reveals which workflows are safe and which need tighter controls.
ZERO-CODE INTEGRATION
No SDK. No code change. Just a connection string.
Point your agent's JDBC driver, your ETL pipeline, or any database client at the Hoop gateway. Same driver. Same ORM. Same code. The agent doesn't know Hoop exists. The data arrives masked, the session is recorded, and guardrails are active.
ORGANIZATIONAL IMPACT
From agent controls to organizational accountability.
Every AI agent action, every approval flow, every blocked command becomes an auditable compliance event. Your leadership sees agent governance at scale across every framework.
How many actions did your AI agents take last week?
If you don’t know, you need Hoop.