loop.foodFood rescue, with follow-through

A coordination agent for food rescue

Good food.
Plans change.
Follow through.

A collection is arranged. Then the driver cancels.
loop.food coordinates what happens next—and keeps the commitments, the limits and the evidence visible.

Challenge the plan Start with a difficult message

An interactive synthetic network. Every action uses the working application.

THE JUDGE CHANGES THE PLANSCENARIO 01
36portions.
One collection deadline.
×
Original transportDriver cancels
Unresolved
The next decisionIs a replacement still possible?
Recipient commitment Time remaining Cancellation acknowledged
Illustrative scenario · no real meals rescued

THE DEMONSTRATION

You choose the disruption.
The record shows what happened.

Cancel transport. Change recipient capacity. Add a delay. Inspect the feasible alternatives and follow the case to a participant-confirmed receipt—or a clear request for human help.

Take the judge’s seat

THREE THINGS TO CHALLENGE

A plan is a promise.
Make it inspectable.

01

Where did that fact come from?

Inspect the original words behind an extracted quantity or deadline. Missing information becomes a question for the kitchen.

Test the intake
02

Why is this plan still possible?

See time remaining, participant commitments and the reasons alternatives are excluded. A pending cancellation blocks replacement.

Inspect a changing plan
03

Who actually confirmed it?

Kitchen release, pickup and receipt belong to participants. A model’s statement cannot stand in for a committed action.

Inspect the action record

THE EVIDENCE STANDARD

Earn the claim.

We compare the agent with competent rules automation. Synthetic completion is not real-world impact. Live AI performance and any advantage over the baseline must come from measured runs.

Open the evidence workspace