I Built an AI Incident Response Agent That Remembers What Happened
Security incidents rarely happen in isolation.
A new alert may look like a completely new problem, but in many environments it is connected to something that happened hours, days, or even weeks earlier. The difficult part is not only detecting an incident. It is understanding what happened, finding the relevant evidence, deciding what matters, and responding without losing the context gathered during previous investigations.
That is the problem I wanted to explore with an AI Incident Response Agent.
The idea is straightforward: instead of treating every security alert as an independent event, the agent can investigate an incident, retain useful information about what it discovered, and recall relevant information when a similar situation appears again.
The key piece that makes this possible is persistent agent memory.
The problem with stateless incident response
A typical incident-response workflow starts with an alert.
An unusual login, suspicious network activity, unexpected privilege escalation, or abnormal system behavior can trigger an investigation. An analyst then collects evidence, examines related events, determines whether the alert is actually dangerous, and decides what action should be taken.
The problem is that each investigation can produce valuable context.
An analyst might discover that a particular IP address has appeared in previous incidents. They might learn that a certain sequence of events usually indicates compromised credentials. They might also discover that an alert that initially looks serious is actually normal behavior for a particular system.
If that information is not retained in a usable form, the next investigation starts almost from zero.
This creates a gap between detecting an incident and learning from it.
I wanted the agent to close that gap.
From alert to response
The basic workflow of the AI Incident Response Agent can be viewed as five stages:
Alert → Investigation → Evidence → Decision → Response
The alert is the starting point.
The agent receives information about a potentially suspicious event and begins investigating it. Instead of immediately treating the alert as either safe or dangerous, it gathers and evaluates the available context.
During investigation, the agent can examine details such as the type of event, affected systems, timestamps, users, suspicious indicators, and related activity.
The next stage is evidence.
The agent needs to connect individual observations rather than looking at each event independently. A suspicious login by itself may not mean much. A suspicious login followed by unusual privilege activity and access to sensitive resources tells a very different story.
Once enough evidence has been collected, the agent can reason about the incident and determine an appropriate response.
The final stage is response.
Depending on the system and its available tools, this could mean escalating the incident, recommending an action, requesting additional investigation, or initiating an automated response.
But there is one important question:
What happens when a similar incident occurs again?
That is where memory becomes important.
Giving the agent a memory
A language model can reason about the information placed in its current context, but an incident-response system needs something more persistent.
This is where I use Hindsight as the memory layer.
Instead of treating memory as simply a large collection of previous conversations, the goal is to retain useful information from investigations and recall it when it becomes relevant.
For example, imagine that an earlier investigation identified a recurring pattern:
- An unusual login occurred.
- The login originated from an unfamiliar location.
- Privileges were elevated shortly afterward.
- Similar activity had previously been associated with compromised credentials.
That investigation can become useful context for future incidents.
When another alert contains some of the same characteristics, the agent can recall the relevant historical information and use it as part of its reasoning.
The agent is no longer only asking:
"What does this alert mean?"
It can also ask:
"Have I seen something like this before, and what did I learn from it?"
That difference is the central idea behind the system.
For more information about the technology behind this approach, see the Hindsight GitHub repository and the Hindsight documentation.
A simple example
Consider a hypothetical incident involving a suspicious account login.
On the first occurrence, the agent receives the alert and begins investigating.
It finds an unusual login location and additional activity involving the same account. The investigation establishes that the combination of events deserves attention.
The important part is not just the final decision.
The investigation itself contains knowledge that may be useful later.
The agent can retain information about the incident and the reasoning surrounding it.
Now imagine that a similar alert appears several days later.
Without persistent memory, the agent sees another suspicious login and starts the investigation from the information currently available.
With memory, it can retrieve relevant information from the previous investigation.
The previous incident does not automatically determine the answer. Instead, it provides additional context.
The agent can compare the current evidence with what it has learned previously and then continue investigating.
This creates a simple before-and-after difference:
Without memory:
Alert → investigate from current context → decision
With memory:
Alert → recall relevant history → investigate with additional context → decision
Memory does not replace investigation.
It improves the context available during investigation.
Why memory needs to be selective
One of the interesting challenges is that an agent should not simply remember everything.
Incident-response environments can produce enormous amounts of information. Keeping every event and passing all of it into every investigation would create another problem: too much irrelevant context.
Useful memory should be connected to future decisions.
For example, previous investigation findings, recurring incident patterns, important indicators, and conclusions from earlier cases can be more valuable than retaining every individual event forever.
This is one reason I see agent memory as more than a storage problem.
The useful question is not:
"How much information can the agent remember?"
It is:
"What information will help the agent make better decisions later?"
That changes how the memory layer should be designed.
Where Hindsight fits into the architecture
At a high level, the architecture can be thought of as four major parts.
The first is the incident input layer, which receives alerts and relevant event information.
The second is the AI investigation layer, where the agent reasons about the incident, determines what information is important, and decides what should be investigated next.
The third is the Hindsight memory layer.
This layer allows useful information from previous investigations to be retained and relevant information to be recalled later.
The fourth is the response layer, which represents the outcome of the investigation.
The important relationship is between investigation and memory.
During an investigation, the agent can learn something worth retaining.
During a later investigation, it can recall that information.
This creates a feedback loop:
Investigate → Learn → Remember → Recall → Investigate with more context
That loop is what makes the system different from a stateless AI assistant.
You can learn more about the broader concept of agent memory from Vectorize.
What I learned
Building the concept of an AI Incident Response Agent changed how I think about AI agents.
1. Reasoning is only part of the problem
A capable model can reason about an incident, but reasoning is limited by the information available to it. Persistent context can become just as important as the model itself.
2. Memory should support decisions
An agent does not need to remember everything. It needs to remember information that can become useful during future reasoning.
3. Previous incidents are valuable context
Security investigations often contain patterns that are difficult to recognize from a single event. Historical context can help connect today's investigation with yesterday's findings.
4. Memory should not replace evidence
A previous incident can guide an investigation, but it should not automatically determine the conclusion. Current evidence still matters.
5. Incident response is a good example of why agents need continuity
A one-time chatbot interaction can be useful without memory. An agent that repeatedly investigates related problems is different.
It needs to learn from what happened before.
The bigger idea
The most interesting part of this project is not simply using an LLM to analyze a security alert.
It is the idea of creating an incident-response agent that can build context over time.
A useful security agent should not behave as though every investigation is its first one.
When an incident happens, it should investigate.
When the investigation produces useful knowledge, it should remember.
When a related incident happens later, it should be able to recall that knowledge and use it as additional context.
That creates a more continuous investigation process:
Detect. Investigate. Learn. Remember. Recall. Respond.
For me, that is the most important lesson from building an AI Incident Response Agent: an agent becomes much more useful when its knowledge can persist beyond a single interaction.
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