Overview
We harden agent systems with deterministic tool authorization, human-in-the-loop for irreversible actions, auditability, and recovery — so confidence is never mistaken for permission.
Who it is for
Security, platform, and AI engineering teams shipping agents into production backends.
The problem
Unsupervised tool calls create operational and security risk. LLM “judgment” alone is not an authorization system.
What you get
- Tool inventory classified by effect (read / write / irreversible)
- Authorization policy design (default deny for protected tools)
- HITL flows for sensitive actions
- Audit / timeline recommendations
- Failure and recovery patterns for durable runs
Technical scope
Tool permissions, Gatekeeper-style authorization, audit trails, and recovery paths for failed or partial runs — so agents can act usefully without becoming an ungoverned automation risk.
How we work
- 01
Inventory tools and side effects
- 02
Classify reversible vs irreversible actions
- 03
Design policy and approval UX
- 04
Implement with Gatekeeper-style patterns where applicable
- 05
Review with security and platform stakeholders
FAQ
- Is Gatekeeper the same as an LLM judge?
- No. Authorization should be deterministic policy. Models may inform decisions; they should not grant permission alone.
- Can you retrofit governance onto an existing agent?
- Often yes. We start with tool classification and the highest-risk write paths.