Incident Response Agent: What If Your AI Teammate Could Actually Remember?
Production incidents are an unavoidable part of running software systems.
A service can suddenly fail. A deployment can introduce an unexpected issue. A database can become unavailable. A configuration change can affect an application in a way nobody expected.
When something like this happens, engineers need to understand the problem quickly.
But there is an interesting problem that often gets overlooked:
What happens when the team has already faced the same problem before?
That question is what led us to build our Incident Response Agent powered by Hindsight.
The Problem We Wanted to Solve
In most DevOps teams, there is already a huge amount of information available.
There are Slack conversations, Jira tickets, Confluence pages, logs, Git commits, incident reports, and post-mortems.
The problem is usually not the lack of information.
The problem is finding the right information at the right time.
Imagine an engineer receiving a production alert at 3 AM.
A service is showing a high error rate. Users are being affected.
The engineer opens the monitoring dashboard and starts checking logs. They look at recent deployments and infrastructure changes. Then they search Slack and Jira.
Someone remembers that a similar problem happened a few months ago.
Now the team starts searching through old incident tickets.
Maybe the solution is already documented somewhere.
Maybe another engineer already solved exactly this problem.
Maybe the root cause and the successful fix are sitting inside an old post-mortem.
But during an active incident, nobody wants to spend twenty minutes searching through old conversations.
Sometimes, finding the solution takes longer than applying it.
That is the problem we wanted to work on.
What If an AI Teammate Could Remember?
AI tools are already becoming part of everyday software development.
We use them to understand code, debug errors, write scripts, and explain technical problems.
But while working on this project, we kept coming back to one question:
What if an AI teammate could actually remember?
Imagine having a teammate who is very good at debugging but forgets every incident after it is resolved.
Every time the same problem happens, you have to explain the entire situation again.
That would be frustrating.
In a similar way, a stateless AI agent may be able to reason about the current problem, but it does not automatically have access to the organization's previous troubleshooting experience.
For our project, we wanted to explore what happens when an AI agent can remember the experiences of a DevOps team.
That led us to our Incident Response Agent.
A Production Incident From an Engineer's Point of View
Let's consider a simple example.
It is late at night.
An alert suddenly appears.
One of the important services is showing a high error rate.
An engineer opens the monitoring dashboard.
Then the logs.
Then recent deployments.
They check infrastructure changes.
They search Slack.
They open Jira.
Someone remembers that there was a similar issue a few months ago, so the team starts searching old incident tickets.
This process is familiar to many DevOps teams.
The frustrating part is that the answer may already exist somewhere in the company's history.
Maybe another engineer already solved it.
Maybe the root cause was documented.
Maybe the exact fix was already tested.
But during an incident, the engineer needs useful context immediately.
This is where our Incident Response Agent comes in.
Instead of treating every incident as completely new, the agent can compare the current situation with incidents stored in its memory.
For example, suppose a service is experiencing database connection problems.
The agent can search its previous incident experience and potentially find older incidents involving the same service or a similar problem.
It can bring back information such as:
- What happened previously
- What caused the problem
- What engineers checked
- Which debugging steps were followed
- Which solution was attempted
- Whether the solution actually worked
This gives the engineer a starting point for the current investigation.
The engineer can then verify the information and decide what action to take.
Why Memory Matters
When we first started thinking about an AI agent for this project, it was tempting to focus mainly on the model.
Which LLM should we use?
How should the interface look?
What tools should the agent have?
But we realized that answering questions was not the most interesting part.
The more important question was:
Can the agent remember what the organization has already learned?
Incident response is a natural use case for persistent memory because history matters.
A production issue that happens today may have happened six months ago.
A deployment that causes problems today may be similar to a deployment that caused problems previously.
A particular error may already have a known solution inside the organization.
This is why we use Hindsight as the memory layer for our Incident Response Agent.
The LLM provides reasoning about the current problem.
Hindsight provides the persistent memory that connects the current incident with previous experience.
That combination is what makes our project different from a basic chatbot.
Memory Should Not Mean Storing Everything
There is an important distinction here.
We don't want memory to simply mean storing every piece of information.
The useful question is:
What information will actually help during the next incident?
For example, if an incident happened because of a configuration change, that information can be useful.
If a particular debugging step helped identify the root cause, that can be useful.
If a recommended solution failed, that is also important.
The outcome matters.
Suppose an engineer tried one solution and it didn't work. Later, another approach fixed the problem.
The agent should remember that difference.
When a similar incident happens again, the agent now has more context than it had before.
This creates a simple cycle:
Incident → Investigation → Resolution → Outcome → Memory → Future Incident
That cycle is the main idea behind our project.
Learning From Previous Incidents
One of the interesting parts of this approach is that every incident can become another experience.
Imagine that a service experiences a particular error today.
The team investigates it.
They discover the root cause.
They try a few possible fixes.
One solution works.
The incident is resolved.
Instead of that incident simply becoming an old ticket, its useful information becomes part of the agent's memory.
Later, the same or a similar problem occurs.
The agent can retrieve the previous experience and provide it as context.
If the previous solution worked, that is useful information.
If a previous solution failed, that is useful information too.
This means the agent is not simply remembering what happened.
It is also remembering what was tried and what happened afterward.
That makes the historical information much more useful during future incidents.
How We See the Agent Working
The workflow we have in mind is relatively simple.
1. An incident happens
The production system generates an alert or engineers provide information about the problem.
2. The agent investigates the current situation
The agent looks at the available information about the incident and reasons about what may be happening.
3. The agent searches its memory
Using Hindsight, it looks for relevant previous incidents and experiences.
4. Previous experience is returned
The agent can provide information about similar incidents, previous causes, debugging steps, attempted solutions, and outcomes.
5. The engineer evaluates the information
The engineer checks the evidence and decides what action makes sense for the current system.
6. The incident is resolved
The team applies the appropriate fix and resolves the problem.
7. The outcome becomes memory
The useful outcome of the incident can be stored so that it becomes available during future investigations.
This means the next incident does not have to start from exactly the same point.
The 45 Minutes to 45 Seconds Idea
One of the ideas behind our project is "45 minutes to 45 seconds."
This is not a promise that every production incident can be solved in 45 seconds.
Real production systems are complicated, and some incidents require significant investigation.
Instead, the idea represents a specific improvement we want to demonstrate:
Reduce the time spent searching for historical context.
Today, an engineer might spend a significant amount of time searching through Slack messages, Jira tickets, documentation, and old incident reports.
Our goal is for the agent to perform much of that historical search and context gathering quickly.
If an incident previously took 45 minutes to understand partly because engineers had to search through old information, and the agent can immediately surface relevant historical experience, then it has already done something useful.
The goal is not magic.
The goal is reducing repetitive work.
The Agent Is Not Replacing the Engineer
This is an important part of our project.
We don't see the Incident Response Agent as a replacement for DevOps engineers.
Production systems are too important to hand over blindly.
The agent can provide:
- Historical context
- Relevant previous incidents
- Possible causes
- Previous debugging steps
- Previous solutions
- Outcomes of previous attempts
But the engineer still needs to verify the evidence and decide what action to take.
The final decision belongs to the engineer.
We see AI fitting into DevOps as a teammate that helps people use the experience their organization has already gained.
What We Want to Change About Incident Response
The important question is not whether incidents will happen.
They will.
The more interesting question is:
How quickly can a team understand and respond to them?
A company might have hundreds of incident tickets.
It might have years of Slack conversations.
It might have detailed post-mortems and documentation.
All of this represents valuable engineering experience.
But if that experience remains buried inside different systems, engineers still have to manually search for it whenever something goes wrong.
We want to make that process more direct.
When a new incident occurs, the agent can look at the current situation and search its historical memory for similar cases.
If it finds something relevant, it can bring back the important details.
The engineer can then use that information while analyzing the current situation.
And after the incident is resolved, the outcome can become part of the agent's future memory.
That creates a feedback loop where every incident can potentially make future investigations more informed.
Why We Find This Interesting
For us, the most interesting part of this project isn't simply using AI for DevOps.
It is exploring what happens when an AI assistant starts accumulating organizational experience.
A normal chatbot can answer a question.
An AI agent with persistent memory can potentially connect today's problem with something the team learned months ago.
That is a different kind of interaction.
Instead of asking:
"What could be causing this problem?"
an engineer could ask:
"Have we seen something like this before?"
And the agent could provide relevant historical context.
That small change could make a meaningful difference during an incident.
The Bigger Idea
A production incident should not have to be a completely new problem every time.
Organizations already spend years building engineering knowledge.
That knowledge exists in incident reports, tickets, conversations, documentation, logs, and post-mortems.
The challenge is making that experience available when it matters.
Our Incident Response Agent is an attempt to do exactly that.
The LLM provides reasoning.
Hindsight provides memory.
The engineer provides judgment.
Together, they create a workflow where previous incidents can become useful context for future ones.
The idea behind our project can be summarized simply:
Don't make engineers solve the same incident from scratch when the organization has already solved it before.
Every resolved incident shouldn't just become an old ticket.
It should become an experience that can help the team the next time something goes wrong.
Top comments (0)