Enables human-coordinated multi-agent workflows for Earth-observation research, Cube Chess 512 self-play benchmarking, and reversible visual QA through WebMCP tools with verification and approval gates.
Enables MCP hosts to run auditable multi-agent work through a SQLite task registry, DAG orchestration, three collaboration modes (pipeline, iterative refinement, and proposal voting), human-in-the-loop approvals, an enforced sandbox, compliance scanning, and long-term memory. Exposes these capabilities as 36 tools so every consequential step stays gated behind a human checkpoint.
Enables AI agents to collaborate via shared blackboard state, multi-agent deliberation, human-in-the-loop proposal approval, operating-mode control, and audit logging through MCP.
Enables stable, traceable hybrid AI workflows through MCP, with deterministic task contracts, bounded specialists, independent visual judging, persistent evidence, and local run tracing for image and fact tasks.