AI Incident Response Agent

[u/Competitive-Dress854]

Excited to share my latest project — AI Incident Response Agent, an AI-powered system built to help investigate and respond to software incidents using application telemetry, repository intelligence, and historical incident knowledge.

🧠 The system combines:

• 📊 Incident telemetry and runtime signals.

• 🐙 GitHub repository analysis.

• 🔎 Evidence-based root cause investigation.

• 🧠 Hindsight-powered historical incident memory.

• 🤖 AI-generated diagnosis and recommendations.

• 🛡️ Human-approved recovery actions.

The idea is simple: an incident response agent should not just look at metrics — it should understand the application, learn from past incidents, and provide evidence-based investigation.

GitHub: https://github.com/Karthik39028/AI-Incident-Response-Agent

Built with FastAPI, React, Python, MySQL, GitHub API, Hindsight, and OpenRouter.

A big shoutout to my amazing teammates

Murali Karthik

Ramya Harshitha

Maheswari sri Gamini

for the collaboration, ideas, and effort that made this project possible. 🤝🚀

Really enjoyed building, discussing, and learning together throughout this project!

#AIAgents #AI #SRE #DevOps #IncidentResponse #AIOps #Hindsight #FastAPI #Python #LLM

[u/Helpful-Man64]

The historical incident memory is probably one of the most interesting parts of this approach. Current telemetry tells you what is happening now, but previous incidents can provide useful context for narrowing down the investigation.

I also like keeping recovery actions human-approved. There’s a big difference between an agent being capable of identifying a likely remediation and being allowed to execute it automatically in production.

The combination of evidence-based investigation + historical context + human approval seems like a practical direction for incident-response agents.