MongoHive turns what one agent learns into tested tools every teammate's agent can call by name. When your Claude or Codex agent finishes an investigation, a background worker distills it into a reusable, read-only tool persisted in MongoDB Atlas. A teammate's background worker checks it independently, and disagreements go to a human. Promoted tools then appear as named MCP tools in everyone's agent. Versions, evals and promotion live in Atlas, and change streams push updates into each agent's chat.
More from The Harness Engineering & Model Wrangling Hackathon
Ripple designs circuit boards from plain English, grades them against hidden checks, and evolves its own harness from the failures, going from 0/4 to 3/4 on…

Offload learns the work you repeat, so next time you can hand it over. Its engine, REM (Replay · Evolve · Merge), is an agent harness with a day/night cycle…
Recursive Computer Use is a local-first desktop automation harness that enables AI agents to execute tasks on your computer, verify outcomes locally, and…

Da Vinci is a recursive improvement CAD harness designed to support physical engineering tasks through specialized AI agents, programmatic CAD and automated…

CyclopsDiary gives factory workers wearing Meta AI glasses a shared memory of the floor. Every worker's glasses feed one MongoDB memory that keeps what was…

A harness that teaches itself what context to show Jev, TypeSafe's fast decision model, proves the result on a storm it never saw, and can't cheat because…