AGENTIC AIRTRAFFIC SIMULATORPYTHON & NEXT.JS

SkyRipple:
Agentic Airtraffic Simulator

A full-stack discrete-event delay propagation engine and agentic recovery simulator for airline operations, predicting network-wide cascades from localized disruptions.

Launch Simulator

01. Abstract & The Performance Bottleneck

In airline operations, computing the cost of an isolated flight delay is trivial; modeling the multi-day network cascade it triggers is an expensive computational bottleneck. SkyRipple is a full-stack discrete-event delay propagation engine and agentic recovery simulator.

Operating on 582,304 real BTS flight records, 190,711 crew pairings, and 2.41 million synthetic passenger itineraries, the core engine proves that a localized disruption (e.g., a 2-hour capacity drop at ORD) carrying a direct cost of $181,376 triggers a network-wide cascade of $942,080 - a 5.19x propagation multiplier.

The Optimization

By isolating state mutation from graph traversal and aggressively monkey-patching functools.lru_cache onto the passenger loaders in Python, itinerary generation dropped from 110 seconds to 8 seconds. The live FastAPI endpoint achieved a steady-state resolution time of 16-20 seconds for a full national cascade.

167MB SQLite DB

Immutable Dec 2025 Schedule

16-20s Resolution

Full national cascade

5.19x Multiplier

Delay propagation cost

02. Agentic Arbitration & The LLM Boundary

To autonomously recover from cascading disruptions, SkyRipple deploys a 5-role Operations Control Center (Aircraft, Crew, Passenger, Gate, Duty Manager). Traditional airline recovery systems rely on greedy rule engines; SkyRipple replaces this with arbitrated financial simulation.

Parsing

Gemini Proposes

gemini-flash-lite operates purely as a routing layer in /api/qa_router.py. It maps complex inputs ('delay United 1234 by 90 mins and ground N24993 for 4 hours') to a strictly validated Pydantic schema.

Execution

Code Disposes

The LLM never returns a final calculation. The structured intent is passed to deterministic code which runs pure functions against the pre-computed engine state.

Parallel Evaluation

Duty Manager Agent

Intercepts proposals, runs isolated financial simulations on each branch, blocks destructive swaps, and executes the mathematically optimal reassignment. Outperformed greedy logic by exactly $172,854 on a single disruption.

OCC Multi-Agent Arbitration

INPUT
"Delay UA1234 by 90m & Ground N24993"
GEMINI FLASH LITE
NL Router
PYDANTIC
Validated Schema
Broadcast Disruption
Aircraft Agent
Proposes: SWAP_TAILS
Crew Agent
Proposes: REASSIGN
Gate Agent
Idle
Pax Agent
Idle
Duty Manager Arbitration
Branch A: Greedy Swap
Creates secondary cascade
Cost: -$172,854
Vetoed
Branch B: Delay Downstream
Absorbs localized impact
Cost: -$65,436
Optimal Action

03. The Multi-Day Midnight Seam & Guardrails

Airline schedules do not reset at midnight; delays carry over in the form of displaced aircraft, illegal crews, and missed connections. Phase 8 of SkyRipple implements a continuous operational month engine.

Continuous Physics

The engine models tight-gap overflows. For example, tail N464UA carried a 335-minute unrecovered delay through a 395-minute overnight maintenance buffer.

Spillover-Aware Costing

Standard bounded calculations arbitrarily cut off reactionary costs at midnight. Spillover-aware calculation captured the true value by accurately crediting downstream delay reductions that calendar-based boundaries discard.

Honesty Guardrails

If the engine finds no net-beneficial actions, the system suppresses all celebratory UI states and plainly declares doing nothing was optimal. Hallucinated 'phantom savings' are structurally impossible.

04. Deployment Architecture: SkyRipple Lite

Serverless Precomputation

Instead of running the Python engine live on the web, massive multi-day scenarios are precomputed locally and exported to a zero-latency Next.js static client.

Browser-Side Analytics

Multi-gigabyte JSON outputs were partitioned by domain. The frontend fetches these dynamically in parallel. All dynamic scaling and exports execute natively in the client browser in 0ms, delivering full analytical depth with zero infrastructure overhead.