Engineering with Hindsight

Building an Incident Response Agent with Long-Term Memory

A developer encounters an error, gives it to an AI agent, finds a solution, and moves on. A week later, the same error appears again. The developer returns to the agent, but the process starts from scratch. The solution existed, but the agent had no useful way to remember it.

That raised a question for us: What changes when an AI agent can retain and retrieve context from previous interactions?

This became the foundation of our Incident Response Agent.

The Project

We’re building an AI-powered system that combines incident handling, long-term memory, backend services, workflows, and an interactive interface. The goal is to make incident handling more contextual by allowing information from previous interactions to become useful when similar situations occur again.

At the centre of this approach is Hindsight.

Why Hindsight?

Hindsight is a long-term memory system for AI agents. Instead of treating every interaction as isolated, it allows information to be retained and retrieved when it becomes relevant.

It’s more than simply storing previous conversations. The important part is being able to retrieve the right context when the agent needs it.

In our project, Hindsight forms the memory component of the Incident Response Agent, working alongside the backend and workflows.

The concept can be simplified as:

Without memory: Input → Process → Response

With memory: Input → Retrieve relevant context → Process → Response → Retain useful information

This gives the agent the potential to use previous context when handling related incidents.

The Architecture

Our system brings together several components:

• Incident Response Agent: Works with incident-related information and the workflow.
• Hindsight: Provides long-term memory and contextual retrieval.
• Backend: Connects the components and handles application logic and integrations.
• Workflow: Defines how information moves through the incident-response process.
• Interface: Provides the user-facing layer for interacting with the system.

The interesting part is not any single component, but how these components work together.

Our Contributions

I worked primarily on project setup, Hindsight research and integration, backend integration, and system architecture, alongside Nikhil and Neeraj.

Dhanu contributed to feature and problem-statement research, workflow design, testing, and debugging.

Sekhar and Vamsi worked on UI/UX along with testing and debugging.

Each member focused on different parts of the system, while collaborating to bring them together into one working project.

What We’re Exploring

The bigger idea behind the project is simple: an AI agent shouldn't always have to start from zero.

By combining an agent with long-term memory, we’re exploring how previous context can become useful in future incident-response scenarios.

Our Incident Response Agent is our practical exploration of that idea, bringing together AI, memory, backend engineering, workflows, and UI/UX in one system.

We’re still building, but this is the foundation we’ve created.