Building an Incident Response Agent That Remembers with Hindsight

Khushi A Palankar — Sat, 07 Nov 2026 13:02:48 +0000

How I Built RecallOps: Exploring Hindsight for Smarter Incident Response

Introduction

In software development, technical issues are unavoidable. Applications may stop responding, database connections may fail, APIs may become unavailable, and services may experience unexpected errors.

When an incident occurs, developers investigate the issue, identify its root cause, and apply a solution. However, when a similar incident happens again, the team may need to repeat much of the same investigation.

This creates unnecessary work and increases the time required to restore a service.

To address this problem, I worked on RecallOps, an incident response assistant designed to help development teams organize incident information, record resolutions, manage runbooks, and find relevant information from previous incidents.

An important part of the project's design is exploring how Hindsight can provide memory capabilities to an AI-powered incident response workflow.

The Problem: Repeated Incident Investigation

Consider a payment application that suddenly stops processing transactions because it cannot connect to its database.

A developer investigates the problem, identifies the database connection issue, fixes it, and documents the resolution.

A few weeks later, the same problem happens again.

If the previous incident information is not easily accessible, another developer may have to investigate the issue from the beginning.

Some common challenges include:

Previous incident information is scattered across different places.
Root causes and resolutions are not consistently documented.
Developers spend time investigating recurring issues.
Runbooks may exist but are difficult to find when needed.
Teams may lose valuable troubleshooting knowledge.

The main idea behind RecallOps is simple: make previous incident knowledge easier to organize and retrieve.

Introducing RecallOps

RecallOps is a web-based incident response assistant that provides a centralized place to manage technical incidents and their resolutions.

The application focuses on four main features.

Incident Management

Users can create incidents by entering details such as:

Incident title
Service name
Severity
Description
Symptoms
Error message

These details help organize incidents and make them easier to investigate later.

Resolution Tracking

After investigating an incident, users can record its root cause and resolution steps.

RecallOps updates the incident status and stores the resolution information in the database. It also records the resolution time.

This creates a structured history of how previous incidents were handled.

Runbook Management

Runbooks contain predefined troubleshooting instructions for technical issues.

RecallOps allows users to create and view runbooks with service names, descriptions, and step-by-step instructions.

This helps developers access documented procedures instead of searching through scattered notes.

Previous Incident Search

RecallOps includes a search feature that checks stored incident and runbook information for relevant matches.

For example, when a user searches for a database connection timeout, the application can identify matching previous incidents and display their recorded root causes and resolutions.

The current working implementation uses keyword-based matching against MySQL records. It is not yet a fully semantic AI memory system.

Technology Stack

RecallOps was developed using the following technologies:

The frontend communicates with the backend through REST API endpoints. The backend handles incident operations and interacts with the MySQL database.

How Hindsight Fits Into the Project

One of the interesting aspects of RecallOps is its exploration of Hindsight.

Traditional database storage is useful for saving structured records. However, an agent memory system can provide a different approach to retaining and retrieving useful information from previous interactions.

Hindsight is an agent memory system developed by Vectorize.

Official resources:

Hindsight GitHub: https://github.com/vectorize-io/hindsight
Hindsight Documentation: https://hindsight.vectorize.io/
What is Agent Memory?: https://vectorize.io/what-is-agent-memory

The intended RecallOps workflow is:

An incident occurs.
The developer records the incident details.
The root cause and resolution are documented.
The incident knowledge can be retained as memory.
When a similar issue occurs, the system can retrieve relevant previous knowledge.
The developer can use that information as a starting point for troubleshooting.

This design aims to reduce repeated investigation and make incident knowledge more useful over time.

Implementation note: We configured the Hindsight client in the backend and attempted to use its retain and recall operations. However, the live Hindsight Cloud operations could not be completed because of an account credits/access limitation. Therefore, the current working search feature uses local MySQL keyword matching. We do not present this as a successful live Hindsight integration.

Recording Incident Resolution in MySQL

An important part of RecallOps is storing the resolution of an incident.

The following SQL query updates an incident after its resolution:

UPDATE incidents
SET
root_cause = ?,
resolution = ?,
status = 'RESOLVED',
resolved_at = NOW(),
resolution_minutes =
TIMESTAMPDIFF(
MINUTE,
created_at,
NOW()
)
WHERE id = ?;

This query stores the root cause and resolution, updates the status, and calculates the time taken to resolve the incident.

Using structured records makes it easier to review incident history and retrieve previous solutions.

Example Workflow

Imagine that the Payment Service experiences a database connection timeout.

The developer creates an incident with the following details:

Incident: Payment application cannot connect to the database.

Service: Payment Service.

Symptom: Payment requests are failing.

Error: Database connection timeout.

After investigation, the developer records the root cause and resolution steps.

Later, another developer searches for a similar database connection issue.

RecallOps can use the stored incident information to display matching records and their documented resolutions.

This demonstrates how maintaining incident history can help teams avoid starting every investigation from zero.

What I Learned

Building RecallOps provided practical experience with full-stack application development and incident management workflows.

Some important lessons were:

Backend development: Creating APIs using Node.js and Express.
Database management: Designing MySQL tables and storing incident records.
Frontend development: Building interactive interfaces using React.
API integration: Connecting frontend components with backend services.
Error handling: Identifying configuration and integration problems.
Agent memory concepts: Understanding how systems such as Hindsight can support the retention and retrieval of useful knowledge.

One important learning was that integrating an external service requires more than installing its SDK. Authentication, permissions, configuration, and service access must also be verified.

Future Improvements

RecallOps has several opportunities for improvement:

Complete and validate the live Hindsight retain and recall integration.
Improve retrieval using semantic memory rather than relying only on keyword matching.
Recommend relevant runbooks based on incident context.
Add incident similarity scoring.
Introduce role-based access for development teams.
Improve incident analytics and reporting.

These improvements could make RecallOps more useful for teams managing recurring technical issues.

Conclusion

RecallOps explores a practical problem in software engineering: repeated incident investigation and the loss of useful troubleshooting knowledge.

By combining incident management, resolution tracking, runbook organization, and a design for agent memory, the project aims to make previous technical knowledge easier to access.

The exploration of Hindsight also helped us understand the potential of memory-enabled systems beyond ordinary database storage.

The most important takeaway is that resolving an incident is only one part of the process. Preserving what was learned can help the entire team handle similar problems more effectively in the future.

Useful resources

Hindsight: https://github.com/vectorize-io/hindsight
Documentation: https://hindsight.vectorize.io/
Agent Memory: https://vectorize.io/what-is-agent-memory

DEV Community: Khushi A Palankar

Building an Incident Response Agent That Remembers with Hindsight