Building ProdSeer: AI-Powered Production Failure Prediction™
Modern systems rarely fail because of one bug.
They fail because of hidden operational complexity:
- cascading dependencies
- infrastructure bottlenecks
- observability blind spots
- external API failures
- scaling assumptions
So for the MeDo Hackathon, I built ProdSeer — an AI-powered Production Failure Prediction™ platform.
ProdSeer analyzes GitHub repositories, simulates cascading infrastructure failures, and forecasts production survivability before deployment using structured AI reasoning workflows.
What ProdSeer Does
ProdSeer goes beyond static code analysis.
It:
- analyzes repository architecture
- infers production topology
- identifies operational bottlenecks
- simulates cascading failure scenarios
- forecasts survivability under production pressure
- generates infrastructure redesign recommendations
Some Features
⚡ Repository intelligence
🕸️ Infrastructure topology visualization
🔥 Cascading failure simulation
📉 Survival probability forecasting
🛡️ Operational risk analysis
💬 Conversational infrastructure reasoning
📄 Executive production-readiness reports
The Most Interesting Part
One of the wildest moments during development was watching AI reason about:
- AI-agent architectures
- degraded operational modes
- LLM dependency bottlenecks
- secure command execution
- infrastructure survivability
The system began generating surprisingly believable operational redesigns involving:
- Kubernetes
- Redis
- observability pipelines
- secure execution sandboxes
- API resilience layers
Tech Stack
- MeDo AI Framework
- Gemini 2.5 Flash
Final Thoughts
AI is making software development dramatically faster.
But as AI-generated systems become more complex, operational uncertainty also increases.
ProdSeer was an experiment in exploring what AI-native operational intelligence could look like.
And honestly… watching AI simulate infrastructure collapse scenarios was mind-bending 😄