Building an Incident Response Agent That Remembers Past Incidents

Response Agent That Remembers Past Incidents
By Bavya Arlagadda

Incident response is rarely about solving a problem that has never happened before.

A service becomes slow. Requests start timing out. A database connection pool is exhausted. Engineers investigate the symptoms, identify the root cause, apply a fix, and eventually resolve the incident.

Then, weeks later, something similar happens again.

The difficult part is not necessarily finding the solution. The difficult part is making the experience from the previous incident available when the next investigation begins.

That is the idea behind our Incident Response Agent: combine AI-assisted incident investigation with persistent incident memory so that previous incident experiences can become useful evidence for future investigations.

As the documentation and content contributor for the project, I focused on documenting the architecture, workflow, demonstration process, and the central memory loop that connects one incident to another.

The problem with treating every incident as new

A typical incident investigation starts with the current evidence:

• What service is affected?
• What symptoms are being observed?
• What do the logs show?
• What is the likely root cause?
• What action should be taken?

That information is important, but there is another potentially valuable source of evidence: previous incidents.

Imagine that a payment service previously experienced high latency and request timeouts because its database connection pool was exhausted.

The incident was investigated and resolved.

Later, the same service experiences high latency and timeouts again.

If the previous incident is available as useful memory, the second investigation does not have to start completely from scratch.

That creates the central idea of our project:

Retain what was learned from an incident, then Recall it when a similar incident occurs.

The Incident Response Agent workflow

The project can be understood as a continuous memory loop:

Incident 1
↓
Investigate
↓
Resolve
↓
Hindsight Retain
↓
Persistent Memory
↓
Incident 2
↓
Hindsight Recall
↓
Historical Evidence
↓
AI Agent
↓
Recommendation
The important part is that the first incident does not simply disappear after it is resolved.
Its useful experience can be retained and later recalled.
This gives the system a form of persistent incident memory.

Where Hindsight fits-
The memory layer is provided by Hindsight, which is used to retain and recall relevant incident experiences.
The Retain step happens after an incident has been investigated and resolved.
Information that can be useful to retain includes:

• Incident symptoms
• Relevant logs
• Root cause
• Resolution
• Outcome When a new incident arrives, the system can use Recall to search for relevant historical experiences. The result is historical evidence that can be considered alongside the current incident. This distinction is important. The system is not simply displaying an old incident because it exists. The goal is to retrieve historical experience that is relevant to the current investigation. A concrete example would be- Consider the first incident: Incident: INC-001 Service: payment-api Severity: HIGH Symptoms:
• High latency
• Request timeouts Evidence:
• ConnectionPoolTimeout
• Database connection limit reached Root Cause: Database connection pool exhaustion Resolution: Increase connection pool capacity Outcome: Resolved

After the incident is resolved, its experience is retained.

Now consider a second incident:
Incident: INC-002
Service: payment-api
Severity: HIGH
Symptoms:

• High latency
• Request timeouts Evidence:
• ConnectionPoolTimeout
• Database connection limit reached The second incident looks similar to the first one. Instead of treating it as completely isolated, the system can recall the previous incident.The historical experience then becomes supporting evidence for the AI investigation. The resulting investigation can contain information such as:

json
{
"summary": "...",
"root_cause": "...",
"evidence": [],
"historical_matches": [],
"recommended_action": "...",
"confidence": 0.0
}
The exact investigation result depends on the current incident and the historical information that is recalled.
_Why Retain and Recall matter

_There is an important difference between storing data and using memory.
Simply storing every incident creates a large collection of historical records.

The more useful behavior is:

Previous experience
↓
Retain
↓
Persistent memory
↓
Recall
↓
Relevant evidence
↓
Current investigation

This creates a feedback loop between resolved incidents and future investigations.A previous resolution can become context for a later incident.That is the behavior we wanted the project documentation and demonstration to make clear.

## The role of the AI agent
The AI agent receives the current incident together with relevant historical evidence.
Conceptually:

Current Incident
+
Historical Evidence
↓
AI Agent
↓
Investigation
↓
Recommendation

The historical incident is not a replacement for current investigation.
It is supporting evidence .The current incident still needs to be evaluated using its own symptoms, logs, and available evidence.
This is particularly important for incident response because two incidents can look similar while having different underlying causes.

## The project architecture

The system can be viewed as four major parts.

Frontend

The frontend provides the interface through which users can interact with the incident investigation workflow and view the resulting information.

Backend

The backend acts as the orchestration layer between the application, memory system, and AI investigation workflow.

AI Agent

The AI agent analyzes the current incident and produces a structured investigation and recommendation.

Hindsight Memory

Hindsight provides persistent memory through the Retain and Recall workflow.

Together, these components create the overall flow:

Frontend
↓
Backend
↓
AI Investigation
↕
Hindsight Memory

**## What I contributed to the project
**
As **Member 5**, my contribution focused on documentation and project content.

I documented the architecture and overall workflow so that the relationship between the application components could be understood clearly.

I also documented the demonstration flow, including the important transition from the first resolved incident to the later incident that recalls historical experience.

Another important part of my contribution was organizing the project content around the central concept of persistent incident memory.

The goal was to make the project's key idea easy to communicate:

An incident response agent becomes more useful when previous incident experience can be recalled during a new investigation.

**## Demonstrating the memory loop
**
The most important part of the project demonstration is showing the memory loop rather than only showing an isolated AI response.

The demonstration follows this sequence:

1. Start with an initial incident.
2. Investigate the incident.
3. Resolve it.
4. Retain the resolved experience.
5. Create or investigate a later similar incident.
6. Recall relevant historical experience.
7. Provide the historical evidence to the AI agent.
8. Display the resulting investigation and recommendation.

This makes the Retain → Recall behavior visible instead of treating memory as an invisible implementation detail.

## Limitations

Persistent memory does not automatically guarantee a correct diagnosis.

Historical incidents are supporting evidence, not proof of the current root cause.

The usefulness of recalled information also depends on the quality and relevance of the incidents that have previously been retained.

A similar-looking incident can have a different root cause, so engineers still need to verify the current evidence.

This is why the project's memory workflow should be viewed as an aid to investigation rather than a replacement for investigation.

## The bigger idea

The interesting part of this project is not simply that an AI agent can analyze an incident.

The more interesting idea is that the agent can use **experience from previous incidents** when investigating future ones.

That changes the workflow from:


Incident → Investigation → Resolution


to:

Incident
   ↓
Investigation
   ↓
Resolution
   ↓
Retain Experience
   ↓
Future Incident
   ↓
Recall Experience
   ↓
AI-Assisted Investigation

In other words, the system creates a connection between what was learned yesterday and what needs to be investigated today.

## Conclusion

Our Incident Response Agent demonstrates how persistent memory can be integrated into an AI-assisted incident response workflow.

The core concept is simple:

**Retain previous incident experience → Recall relevant experience → Use it during a new investigation.**

For incident response, that means a previously resolved problem can become useful context when a related problem appears again.

As part of the documentation and content work, my focus was making this workflow clear: from the architecture and memory flow to the demonstration sequence and project explanation.

The result is a system concept where incident history is not just an archive.

It can become reusable experience.

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
Project Repository:
https://github.com/DivyaSree0912/incident-response-agent