Fragility Discovery Engine — Overview

What it is: An open-source Python stress-search harness — wire a resettable discrete-time model, search shock schedules for worst-case failure, export reproducible replay JSON and counterfactual attribution bundles.

Not: a calibrated institution model, a causal inference tool, or a drop-in production risk product.

Live demo: http://34.61.255.147/ · Source: https://github.com/AgenticOp-io/fragility-discovery-engine · Release: v0.6.0

The core idea

You describe a system as a simulation world — something that resets to known starting conditions and advances one step at a time. The engine searches across possible stress sequences to find ones that maximize damage under your metric, then saves a step-by-step record of the run.

That record is JSON you can replay in the browser, compare with counterfactual re-runs, and reproduce with the same seed.

Attribution means counterfactual and path deltas on re-rollouts (FEL Δ⁻ / Δ⁺ conventions) — not Pearl-grade causal identification.

Bring your own world (primary path)

Real value is a ~100-line adapter you write (reset, step, state_vector, instability_score, is_collapsed). Tutorial: BRING_YOUR_OWN_WORLD.md.

```powershell

pip install -e ".[dev]"

fragility search --example capacity-pool

fragility check-world --example capacity-pool

```

Six reference domains (toy regression oracles)

The in-tree worlds are deliberately simplified — structural analogies for frozen CI, not models of real institutions:

Use them to validate the harness; use BYOW for your own physics.

What a run produces

1. Replay file — timestep trajectory, collapse bit, shock lane.

2. Pareto archive (optional) — severity vs attack-cost trade-offs.

3. Attribution bundles — counterfactual re-runs and mutation chains.

4. Certificate / manifest — reproducibility metadata for papers.

All outputs are schema-versioned JSON with deterministic seeds.

What it is not

• Not a live trading system or market data feed
• Not regulatory compliance or calibrated institutional risk
• Not proof that your production system is safe

Quick start

```bash

git clone https://github.com/AgenticOp-io/fragility-discovery-engine

cd fragility-discovery-engine

python3 -m venv .venv && source .venv/bin/activate

pip install -e ".[dev]"

fragility search --example capacity-pool --export-replay out.json

fragility falsify search --example ranked-store # invariant predicate demo

python scripts/run_ga_demo.py --mode aggregate --generations 4 --export-replay toy.json

```

Full guide: How to Use · Cite: Zenodo 10.5281/zenodo.20455689