# RED QUEEN

- **Event:** [The Harness Engineering & Model Wrangling #SFTechWeek Hackathon](https://cerebralvalley.ai/e/mongodb-sftw-hackathon)
- **When:** Sun, Oct 11 at 9:00 AM – 10:00 PM (PDT)
- **Where:** San Francisco, CA
- **Team:** [Vincent Leguide](https://cerebralvalley.ai/u/Vincentleg)
- **GitHub:** https://github.com/vincentleg/red-queen
- **Demo video:** https://youtu.be/HBdpju9CPno
- **Gallery:** https://cerebralvalley.ai/e/mongodb-sftw-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/mongodb-sftw-hackathon/hackathon/gallery/3

# RED QUEEN 2.0 — The Evolution Chamber

**Every fix must survive its own attack.**

**What if an AI agent could test its own solutions, learn from failures, and improve until its fix actually works?**

That's the idea behind RED QUEEN 2.0.

RED QUEEN is a self-improving testing harness that automatically evaluates, challenges, and refines operational policies. Instead of trusting a proposed fix, it deliberately attacks it, identifies weaknesses, generates a better candidate, and verifies the results before allowing promotion.

### The Problem

Imagine a customer making an online payment. The payment succeeds, but the confirmation message gets lost.

The system thinks the payment failed and retries. **The customer gets charged twice.**

This is exactly the kind of failure RED QUEEN is designed to detect and prevent.

### How It Works

RED QUEEN runs each candidate policy through **54 different fault scenarios**, including lost confirmations, delayed responses, and stale ledger reads.

The system evolves through three generations:

- **Generation 1 — Blind Retry:** 20 scenarios fail. Rejected.
- **Generation 2 — Ledger Reconciliation:** 10 scenarios fail. Rejected.
- **Generation 3 — Stable Idempotency:** 54 out of 54 scenarios pass. Promoted.

Each failure produces a counterexample that guides the next deterministic policy improvement. An independent verifier checks every candidate before promotion.

### The Live Proof

RED QUEEN doesn't just display simulated success metrics. It executes the same payment fault against two different policies:

**Baseline policy: 2 charges. Failure.**

**Evolved policy: 1 charge. Success.**

Same payment. Same fault. Different outcome.

### Technologies

**MongoDB Atlas** serves as the system's persistent memory, storing execution events, counterexamples, verification results, and policy evolution history. Runs can be recovered after refreshing the application.

**Raindrop** provides live observability, capturing policy evaluation events, rejected candidates, and verification outcomes. The integration is operational in production.

The current candidate-generation mechanism is deterministic rather than LLM-generated. RED QUEEN demonstrates the underlying recursive harness architecture: adversarial testing, evidence-driven improvement, independent verification, and persistent memory.

### Why It Matters

As autonomous systems take on more responsibility, generating solutions is only half the challenge. **We also need systems capable of testing those solutions, detecting failures, and verifying improvements before deployment.**

RED QUEEN demonstrates how that process can work.

**Don't just generate a fix. Make it survive its own attack.**

**Live Demo:** https://red-queen-ten.vercel.app

**GitHub:** https://github.com/vincentleg/red-queen

## More from The Harness Engineering & Model Wrangling #SFTechWeek Hackathon

- [banana-fighter](https://cerebralvalley.ai/e/mongodb-sftw-hackathon/hackathon/gallery/1)
- [LILI](https://cerebralvalley.ai/e/mongodb-sftw-hackathon/hackathon/gallery/2)
- [DrawTalk Harness](https://cerebralvalley.ai/e/mongodb-sftw-hackathon/hackathon/gallery/4)
- [Bayflow](https://cerebralvalley.ai/e/mongodb-sftw-hackathon/hackathon/gallery/5)
- [Procurator](https://cerebralvalley.ai/e/mongodb-sftw-hackathon/hackathon/gallery/6)
- [OwnerOrbit](https://cerebralvalley.ai/e/mongodb-sftw-hackathon/hackathon/gallery/93)

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