AI Incident Response Agent That Learns From Past Incidents Using Hindsight

Building an AI-Powered Incident Response Agent:
My Contribution to Hack With Bangalore 4.0

Event: Hack With Bangalore 4.0

Team: Data World

Project: Incident Response Agent

Domain: Artificial Intelligence

My Role: Member 3 – Incident Data, LLM Prompts, Briefing Format & Error Handling

## Introduction

In modern software development, system failures, unexpected errors, and service disruptions are common challenges faced by developers and IT operations teams. When an incident occurs, engineers must quickly understand the problem, investigate possible causes, identify relevant historical incidents, and determine the appropriate solution.

However, incident investigation can become time-consuming when information is scattered across logs, documentation, and previous troubleshooting records.

To address this challenge, our team, Data World, developed an AI-based project called Incident Response Agent as part of Hack With Bangalore 4.0.

The project explores how Artificial Intelligence and Large Language Models (LLMs) can assist engineers in understanding incidents, retrieving useful historical information, and generating structured incident briefings.

Our main objective was to explore how AI could make incident investigation more organized, efficient, and accessible.

## Understanding the Incident Response Agent

The Incident Response Agent is designed to assist developers and technical teams during incident investigation.

Rather than treating every incident as a completely new problem, the system uses the concept of memory to make previously recorded incident information available when investigating new issues.

The general workflow involves:

1. Providing incident details to the system.
2. Processing the submitted information.
3. Retrieving potentially relevant historical incident records.
4. Using an LLM to analyze the available information.
5. Generating a structured incident briefing.
6. Helping the engineer understand possible causes and next steps.

One important aspect of this approach is that historical incident information can provide useful context for understanding new problems.

The system is intended to support engineers rather than replace human investigation and decision-making.

## My Individual Contribution – Member 3

As Member 3 of Team Data World, my responsibilities focused on four important areas:

• Incident data.
• LLM prompt engineering.
• Incident briefing format.
• Error handling.

My contribution was mainly related to the information-processing and AI reasoning aspects of the project.

### 1. Incident Data Management

Incident data is an important component of an incident response system.

For an AI assistant to generate meaningful analysis, it needs properly organized information about the incident.

Incident information may include:

• Incident description.
• Affected service or component.
• Error messages and logs.
• Observed symptoms.
• Possible root causes.
• Troubleshooting steps.
• Resolution details.

Organizing incident information into a consistent structure makes it easier for the system to process and retrieve relevant records.

I focused on understanding how incident information should be represented so that it could provide useful context during AI-based analysis.

### 2. LLM Prompt Engineering

Another major responsibility was understanding and working on the prompts used by the Large Language Model.

Prompt engineering plays an important role in AI applications because the quality of the instructions directly influences the structure and usefulness of the generated response.

For an incident response assistant, prompts should guide the model to:

• Understand the incident description.
• Identify important technical symptoms.
• Consider relevant historical incidents.
• Suggest possible causes.
• Recommend appropriate troubleshooting steps.
• Clearly communicate uncertainty.

An important consideration is preventing the model from generating unsupported technical information.

For example, if the available incident description does not contain sufficient information to identify the root cause, the assistant should communicate that limitation rather than presenting an assumption as a confirmed fact.

This helped me understand the importance of designing prompts that produce structured, relevant, and responsible AI-generated responses.
**

3. Incident Briefing Format**

A technical response is most useful when it is presented clearly.

During incident investigation, engineers may not have time to read lengthy and unstructured explanations.

Therefore, the incident briefing should organize information into meaningful sections.

A useful briefing structure includes:

• Incident Summary
• Affected Component
• Observed Symptoms
• Relevant Historical Incidents
• Possible Root Cause
• Recommended Investigation Steps
• Suggested Resolution
• Additional Information Required

This structure helps engineers understand the incident without having to search through a large amount of unorganized information.

I focused on the importance of making AI-generated briefings readable, informative, and practical.

### 4. Error Handling

Error handling is another important aspect of developing a reliable AI application.

An incident response workflow may encounter different problems, such as:

• Missing incident information.
• Empty user inputs.
• Failed memory retrieval.
• Unexpected LLM responses.
• API request failures.
• Network timeouts.
• Incorrect response formats.

The application should handle these situations gracefully instead of crashing or displaying misleading information.

For example, when historical incident retrieval is unavailable, the system should still be able to explain that its analysis was generated without historical context.

Similarly, when an AI response is incomplete, the application should communicate the issue clearly.

This responsibility helped me understand why error handling is essential when developing applications that depend on external AI services.

## Understanding the Role of Memory in AI

One of the interesting concepts behind this project is the use of memory in AI applications.

Traditional AI interactions often depend heavily on the information provided in the current interaction.

However, incident response requires the ability to reuse previously recorded knowledge.

The memory-based approach allows the system to:

Remember: Store useful information from previous incidents.

Recall: Retrieve relevant information when a new incident occurs.

Reflect: Identify useful patterns and relationships across previously recorded incidents.

This approach can help engineers avoid repeating investigation steps and make better use of previously documented experience.

However, retrieved incidents should always be treated as supporting references. Similar symptoms do not automatically mean that two incidents have the same root cause.

## Challenges and Learnings

Working on this project helped me understand that developing an AI-based application involves much more than simply connecting an LLM to a user interface.

Some of the important areas that require attention include data quality, prompt design, output formatting, memory retrieval, and error handling.

One of the key challenges is ensuring that the information provided to the AI system is clear and sufficiently detailed.

Another important consideration is the reliability of generated responses. AI-generated recommendations should be reviewed by engineers, particularly when they involve changes to production systems.

Through this project, I gained a better understanding of:

• Practical applications of Artificial Intelligence.
• Large Language Model prompt engineering.
• Organizing and structuring technical information.
• Memory-based AI workflows.
• Generating structured AI responses.
• Handling errors in AI-powered applications.
• Collaboration and responsibility in team-based development.

## Project Repository

The project source code and implementation can be explored through our GitHub repository:

GitHub: https://github.com/chiranth-art/OnCall-Memory
**

Conclusion**

Participating in Hack With Bangalore 4.0 as a member of Team Data World was a valuable learning experience.

As Member 3, I focused on incident data, LLM prompts, incident briefing structure, and error handling. These responsibilities helped me understand the importance of information quality and structured AI reasoning in developing practical applications.

The Incident Response Agent demonstrates an interesting application of Artificial Intelligence by combining language-model capabilities with the concept of persistent memory to support incident investigation.

This experience strengthened my interest in Artificial Intelligence, prompt engineering, and developing AI-powered solutions for real-world technical challenges.

I look forward to exploring more practical applications of AI and improving my skills through future projects.

Project: Incident Response Agent

Team: Data World

Event: Hack With Bangalore 4.0

Role: Member 3