Provides structured workflows (phases, gates, coordination) for AI agents, enabling complex task execution with quality enforcement and multi-agent coordination via Model Context Protocol.
A production-grade coordination hub that enables AI agents and human teams to work as a single organism by sharing tasks, context, decisions, and persistent memory across projects. It features two-tier agentic memory with per-agent hot caches, inter-agent messaging, and multi-agent authorship tracking for seamless collaboration.
Enables teams to share structured, git-synced context among AI coding agents working on the same repository, including living plans, task declarations, handoff briefs, file-provenance history, and conflict detection.
Provides a shared context layer for AI agent teams to improve token efficiency through context deduplication and incremental state sharing. It enables multiple agents to coordinate tasks, share real-time discoveries, and manage dependencies while significantly reducing redundant data transmission.