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 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.
Enables MCP-capable AI agents to work on bounded project folders through read, write, run, and verify tools, with durable approval/resume workflows and completion proven by observed verification evidence rather than self-reported results.