IncidentIQ

Inspiration

I built what I wished existed which is a system that learns from every incident and autonomously handles future occurrences while engineers sleep.

What it does

IncidentIQ autonomously detects, analyzes, and resolves production incidents using four AI agents, reducing MTTR from hours to minutes.

How we built it

It uses our specialized Python agents(Detective, analyst, remediation and documentation agent) that communicates via Elasticsearch with Gemini 2.0 Flash that integrates Slack for notifications and approval workflows.

Challenges we ran into

Gemini occasionally hallucinated solutions which makes accessing the data output have reduces confidence

Accomplishments that we're proud of

Created a self-learning knowledge base where each resolved incident improves future responses, with confidence scores enabling risk-based automation

What we learned

Multi-agent architecture with specialized agents beats monolithic systems, clear boundaries enable isolated failures, independent optimization, and easier debugging with complete audit trails per agent.

What's next for IncidentIQ

Add predictive incident detection using ML models to forecast issues before they occur (memory leaks, disk space exhaustion, traffic spikes)

Built With

• elastic
• python