# Gemba

- **Event:** [The Recursive Agents Hackathon](https://cerebralvalley.ai/e/crusoe-recursive-agents-hackathon)
- **When:** Sat, Oct 17 at 9:00 AM – 10:00 PM (PDT)
- **Where:** San Francisco, CA
- **Team:** [Enzo Junqueira](https://cerebralvalley.ai/u/enzopj), [Sudhersan KV](https://cerebralvalley.ai/u/sudhersankv)
- **GitHub:** https://github.com/enzojunq/gemba
- **Demo video:** https://drive.google.com/drive/folders/195liBWGiOSN7Pl7ZiFbqhTgOSw690oM3?usp=sharing
- **Gallery:** https://cerebralvalley.ai/e/crusoe-recursive-agents-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/crusoe-recursive-agents-hackathon/hackathon/gallery/42

Gemba is a decision twin for operations. Slotting software and digital twins tell a warehouse where things should go and how the floor behaves; they treat the human decisions as fixed inputs, so they never show what a decision triggers next, and when the engineer leaves, nobody knows why the plan was shaped the way it was.

Gemba simulates the warehouse and the people who run it. A deterministic hourly floor twin (28 aisles, a six-zone pick module, about 2,000 orders a day, synthetic data shaped on a real site) is run by an org of LLM agents defined in config: a program manager who chairs, ops, a safety lead with a veto, a quality lead who can lock a customer's SKUs, a shift lead who owns the labor truth, a director for escalations. Every simulated morning they hold a standup: options carry simulator what-ifs on forecast demand, stakeholders take positions citing that evidence, code scores the options against doing nothing and checks authority, the chair decides, and typed levers change the floor. A consequence engine turns outcomes into new events with causal links, so a night of aggressive moves can cause a picker learning dip, mis-picks, a key-customer escalation, Quality in the room, SKUs locked and the reslot slowed, and you can walk that chain back to the decision.

An engineer uses it to rehearse a change before living it, fork any day with a different call and compare the two futures, run the same month under different company priorities, inject disruptions in plain words or describe whole scenarios in a sentence, and keep the org's full decision record. The org also reviews its own worst days: a coach traces the regret to its decision, proposes a lesson, proves it by forking that day, and the playbook is promoted only if it beats the previous one on held-out scenarios. Done-checks are written by the org within code-set bounds and verified every simulated day; drift detectors catch churn, stalls, prediction error and overspend.

## More from The Recursive Agents Hackathon

- [Escape Labs](https://cerebralvalley.ai/e/crusoe-recursive-agents-hackathon/hackathon/gallery/39)
- [Team Sway](https://cerebralvalley.ai/e/crusoe-recursive-agents-hackathon/hackathon/gallery/40)
- [Crusoe Forge](https://cerebralvalley.ai/e/crusoe-recursive-agents-hackathon/hackathon/gallery/41)
- [BrainBob - Shared Memory](https://cerebralvalley.ai/e/crusoe-recursive-agents-hackathon/hackathon/gallery/43)
- [manubaba](https://cerebralvalley.ai/e/crusoe-recursive-agents-hackathon/hackathon/gallery/44)
- [Measured](https://cerebralvalley.ai/e/crusoe-recursive-agents-hackathon/hackathon/gallery/45)

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Markdown version of https://cerebralvalley.ai/e/crusoe-recursive-agents-hackathon/hackathon/gallery/42. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt