AI Incident Response Agent That Learns From Past Incidents Using Hindsight

poovammapunya971-coder — Sat, 07 Nov 2026 09:47:18 +0000

Technical Article · Hack With Bangalore 4.0 · Problem Statement 7

My Contribution as Member 4 to OnCall-Memory

1. Introduction

**
For Hack With Bangalore 4.0, our team worked on Problem Statement 7 – Incident Response Agent. We
developed OnCall-Memory, an AI-powered incident response project designed to help on-call engineers
investigate production issues using relevant information from previous incidents.
In a production environment, engineers may face recurring failures such as database connection timeouts or
unsuccessful customer orders. Information about earlier incidents can be valuable, but finding and understanding
old incident records during an outage can take time. OnCall-Memory aims to make this information easier to use
by combining historical incident recall with AI-powered analysis.
As Member 4, my main contribution focused on project documentation and demo preparation. I worked on
organising the README documentation, preparing the demonstration script, and helping communicate the
project's workflow clearly and accurately.
**

Understanding OnCall-Memory**

OnCall-Memory provides an incident investigation workflow through a Streamlit interface. The project uses
Python, Groq for AI-based analysis, and Hindsight as its memory system.
The application can take an incident description, retrieve related historical incident information, and use the
available context to produce an investigation briefing. The repository also includes fallback behaviour for cases
where memory retrieval is unavailable.
The goal is to help engineers consider previous incidents when investigating a new production problem, rather
than starting every investigation without historical context.

3. My Responsibilities as Member 4

3.1 README.md Documentation
One of my key responsibilities was preparing and organising the project's README documentation. The
README is often the first place a reviewer or developer looks to understand a repository, so it needs to explain
the project in a clear and structured way.
I worked on the README content and its organisation, including the project introduction, problem statement,
solution overview, features, technologies, setup guidance, and other information needed to understand the
project.
The README explains the real-world problem using an example of a food-delivery platform experiencing
database connection timeouts and describes how OnCall-Memory can help an engineer investigate by retrieving
relevant historical knowledge

3.2 Demo Script Preparation

I also worked on preparing and organising the project demonstration script. The purpose of the script is to help
the presenter explain the project in a logical sequence and keep the video within the required time.
The script includes the target video length and recording format, preparation steps, and important accuracy
checks. It describes the active application path: the Streamlit app submits an incident to the agent, the agent
attempts to retrieve similar incidents through the memory module, and the AI analysis uses the available historical
records as context.
An important part of the script is making sure that the presentation reflects the actual implementation. For
example, it warns the presenter not to claim that every incident submitted through the user interface is
automatically saved to Hindsight when the current UI flow does not call the save functions

3.3 Documentation and Demo Accuracy

I helped ensure that the README and demo script describe the project consistently. This involves checking that
setup instructions, commands, feature descriptions, and narration match the repository and the behaviour that
can actually be demonstrated.
The demo preparation notes also remind the presenter to use valid environment variables without displaying
secret API keys, to show only real application output, and not to invent terminal results or memory-retrieval
responses.

3.4 Technical Communication and Presentation Preparation

Another part of my contribution was organising the explanation of the project for reviewers and other audiences.
A technical demonstration should make clear:
• The real-world incident-response problem.
• Why historical incident knowledge is useful.
• How OnCall-Memory accepts an incident description.
• How the application retrieves relevant memories.
• How the AI uses the available context to prepare an investigation briefing.

4. Challenges and Learning

One challenge was understanding how the different parts of the project fit together so that the documentation and
presentation would be accurate. The README and demo script needed to explain the complete workflow without
overstating what the current implementation does.
This work helped me develop skills in technical writing, repository documentation, demo planning, reviewing
application workflows, and communicating technical ideas clearly. It also showed me that documentation is an
important part of a software project because it helps other people install, understand, review, and use the
application

5. Conclusion

As Member 4, I contributed to OnCall-Memory by focusing on the README documentation and the preparation of
the project demonstration script. I also helped organise the technical explanation and check that the presentation
accurately reflected the implemented workflow.
This experience taught me that a successful project requires more than code. Clear documentation and a
well-prepared demonstration help make the team's work understandable, reproducible, and easier to evaluate

DEV Community: poovammapunya971-coder

AI Incident Response Agent That Learns From Past Incidents Using Hindsight

1. Introduction