Enables MCP-compatible AI agents to safely act on business backends by enforcing per-agent permissions, autonomy thresholds, human approval with review-and-edit, and full audit trails.
Enables AI agents to safely mutate business state by demonstrating phase-gating, validation-before-mutation, and structured audit logging in a toy inventory and purchase order system.
Enables AI agents to execute multi-step Standard Operating Procedures step by step, with enforcement of completion at each step, making LLM behavior predictable and auditable.
Enables AI coding agents to evaluate actions against team-defined policies, record decisions, and obtain human approvals for potentially risky operations.
Behavioral governance layer for AI assistants that monitors for hallucination, inconsistency, and unsafe reasoning patterns while managing stateful AI sessions.
A public-safe research prototype for controlling AI-agent tool actions with deterministic policy, risk-based human approval, time-bound authorization and a tamper-evident audit chain.