Enforces work-readiness gates by tracking project plots, plans, stakeholder views, slots, stops, and journal entries via REST and MCP, with LLM-based plan agreement and result verification.
A governance and control layer for MCP tools that manages tool requests as intents through policy-based approval, queuing, or blocking. It enables secure human oversight and audit trails for consequential agent actions across platforms like Claude Desktop and Cursor.
Enables AI agents to gate real-world side effects through a durable decision record, ensuring at-most-once initiation, deduplication, budget enforcement, and auditable outcomes across retries and failures.
Enables structured role-to-role handoffs and merge gating for multi-agent collaboration. It persists evidence and computes approval gates without invoking LLMs.
Provides plan state management and phase gate enforcement for AI development loops. It tracks task progress and coordinates the lifecycle of agents by requiring specific evidence before advancing through development phases.