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.
Enables AI agents to investigate and resolve operational exceptions across orders, payments, inventory, and fulfillment through a multi-system truth and guarded actions.
Enables AI assistants to investigate and safely resolve commerce order exceptions, such as expired inventory reservations, by providing a workflow across synthetic order, payment, inventory, and fulfillment systems.
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.
Provides an external, validated state database for LLM agents to manage long-horizon tasks, with tools for defining schemas, invariants, actions, procedures, and branching, preventing state drift and compounding errors.