AI Incident Response Agent with Hindsight Memory
Introduction
Production incidents can happen repeatedly, and engineers often need to search through previous incident records to understand what happened and how it was resolved.
To address this challenge, we built an AI Incident Response Agent with Hindsight Memory. The system uses AI and long-term memory to recall similar past incidents and provide useful troubleshooting context for new incidents.
The Problem
When a production service experiences an incident, the engineering team needs to identify the possible cause and find an appropriate solution.
If a similar incident happened before, its root cause and resolution can be valuable. However, finding and reusing that information manually can take time.
Our project aims to make this process more efficient by giving the AI agent access to memories of previous incidents.
Our Solution
The AI Incident Response Agent follows this workflow:
Incident → AI Analysis → Hindsight Recall → Resolution → Hindsight Storage
- The user enters a production incident.
- The AI agent analyzes the incident.
- Hindsight recalls similar incidents from the past.
- Previous root causes and resolutions provide troubleshooting context.
- The user confirms the actual root cause and resolution.
- The new incident and its outcome are stored in Hindsight.
- Future incidents can use this information.
Example
Consider a payment API that starts returning 500 errors.
Hindsight can recall a previous incident where the database connection pool was exhausted.
The previous resolution was to increase the database connection pool size and restart the service.
The current incident can then be investigated using this previous experience as useful context.
After the incident is resolved, the new incident, root cause, resolution, and outcome are stored in Hindsight for future recall.
Technology Stack
- Python
- FastAPI
- Hindsight
- Ollama
- Llama 3.2 1B
- HTML
- CSS
- JavaScript
- Docker
- GitHub
Architecture
User
|
v
Web Frontend
|
v
FastAPI Backend
|
+------> Local LLM (Ollama)
|
+------> Hindsight Memory
|
+--> Recall past incidents
|
+--> Store new resolutions
Why Hindsight Memory?
The key part of this project is the memory layer.
Instead of treating every incident as a completely new problem, the system can use information from previous incidents.
Hindsight provides the memory layer for:
- Retaining incident information
- Recalling relevant past incidents
- Using previous outcomes as context
This allows the incident response workflow to become more useful as more incidents are resolved and stored.
Demo
Our demonstration shows a complete incident-response cycle:
New Incident → Similar Past Incident → AI Analysis → Resolution → Memory Storage
The demo uses a payment API incident to show how a previous database connection pool issue can be recalled and used as troubleshooting context.
Future Improvements
Some possible future improvements include:
- Integration with monitoring and alerting systems
- Automated incident log ingestion
- Multi-service incident correlation
- More advanced root-cause analysis
- Authentication and role-based access
- Production deployment
Conclusion
The AI Incident Response Agent demonstrates how AI agents and long-term memory can be combined to support incident analysis.
By recalling previous incidents and storing newly confirmed resolutions, the system creates a feedback loop where past operational experience can be reused for future incidents.
Project Repository
GitHub: https://github.com/varshinireddy1028/AI-Incident-Response-Agent
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