# Beyond Alerts: Designing a Memory-Driven Incident Response Agent

> Source: <https://dev.to/dhanusambangi/beyond-alerts-designing-a-memory-driven-incident-response-agent-55n9>
> Published: 2026-09-29 13:42:06+00:00

While building **RECALL**, my main contribution was understanding the incident-response problem, researching what an AI agent should actually do in a real engineering environment, designing the workflow, and testing how the system behaves under different scenarios.

The key question I focused on was simple:

**How can an AI agent help engineers respond to incidents using not only the current alert, but also what happened in the past?**

In a typical software system, an incident alert is only the starting point. An engineer may receive information such as error logs, timestamps, service names, stack traces, deployment details, and severity levels. However, this information can be noisy and incomplete. The same error may have occurred before, and the solution may already exist somewhere in the team's previous incident history.

This led us to the central idea behind RECALL: **an incident-response agent should not operate only on the information available at the moment of failure. It should also be able to use relevant historical knowledge.**

One of the first parts of the workflow I worked on was understanding how an incident should be represented before the agent starts reasoning about it.

An incoming alert can contain a large amount of information, but not everything is equally useful. Therefore, the alert needs to be normalized so that the important information can be identified clearly.

Some of the key information includes:

For example, an alert might say that a particular service has suddenly started returning a large number of timeout errors. Instead of simply passing the entire alert to an LLM and asking it to find a solution, the workflow first identifies the important characteristics of the incident.

This makes the problem more structured and gives the agent better information for retrieving relevant historical incidents.

From there, I helped design a memory-backed workflow using **Hindsight**.

The important idea was that RECALL should not perform one generic search and blindly use whatever result comes back. Different aspects of an incident can require different types of historical information.

For example, the system can retrieve memories related to:

This allows the agent to look at an incident from multiple perspectives.

Conceptually, the retrieval process can be represented as:

```
memories = recall(
    bank_id,
    query="similar symptoms and root causes for this service"
)
```

The retrieved memories then become supporting evidence for the diagnosis.

For example, suppose a service starts producing database timeout errors. The current alert alone may not explain why this happened. However, the memory system might retrieve a previous incident involving the same service where a connection-pool configuration caused similar timeouts.

That historical incident does not automatically mean the current problem has the same root cause. Instead, it gives the agent a useful hypothesis that can be investigated.

This distinction was important in our workflow: **memory should support reasoning, not replace reasoning.**

After relevant memories are retrieved, RECALL can combine the current incident information with historical evidence.

The agent can then consider questions such as:

This makes the agent more useful than a system that simply generates a generic troubleshooting response.

For me, this was one of the most important aspects of the project. The goal was not simply to make an LLM produce an answer. The goal was to design a workflow where the answer is supported by relevant information from both the **current incident** and the **past experience of the system**.

Another important part of my contribution was thinking about how RECALL could learn from the outcome of an incident.

An incident does not end when the agent suggests a solution. The actual result of the suggested action is also valuable information.

For example, if the agent recommends changing a configuration and the engineer applies the fix successfully, that outcome can become useful knowledge for future incidents.

On the other hand, if the suggested fix does not work, that information is also valuable.

Similarly, if an engineer investigates the incident and discovers that the actual root cause was different from the agent's initial diagnosis, that correction can become part of the system's future knowledge.

This creates a continuous learning loop:

**Incident → Diagnosis → Action → Feedback → Memory**

This was an important concept because the system should not only remember successful solutions. It should also learn from failures and corrections.

A failed fix can be just as useful as a successful fix because it tells the agent what **not** to recommend under similar circumstances.

While designing the workflow, I also considered an important problem with historical information: **not every old solution remains valid forever.**

Software systems constantly change. Infrastructure can be updated, services can be migrated, configurations can change, and new versions can be deployed.

For example, suppose a particular configuration change successfully solved an incident six months ago. After several infrastructure changes, applying exactly the same solution today might not be appropriate.

Therefore, historical information should not be treated as an automatic answer.

Instead, RECALL should treat memories as **evidence that needs to be verified against the current situation**.

This idea helped shape the workflow around contextual reasoning rather than simple retrieval.

The agent can use previous incidents to generate hypotheses, but it should still consider the current service state, recent changes, current logs, and other available evidence before recommending an action.

Another major part of my contribution was testing different scenarios.

It is easy to design an AI workflow that works perfectly when every component behaves as expected. However, real-world systems rarely behave that way.

During testing, we considered situations such as:

These scenarios helped us think beyond the ideal workflow.

For example, if there are no relevant memories for an incident, the system should not pretend that it found a historical solution. Instead, it should recognize that there is insufficient historical evidence and continue using the information available from the current incident.

Similarly, if the memory service is unavailable, the overall system should have a way to handle that failure instead of completely breaking the incident-response process.

This testing helped us understand that reliability is not only about producing a correct answer. It is also about handling uncertainty and failure gracefully.

My biggest takeaway from RECALL was that building an AI agent is not just about generating a good response.

The surrounding workflow is equally important.

We need to think about:

**What information should be retrieved?**

**How should that information be used?**

**How do we determine whether retrieved information is relevant?**

**What happens when the information is missing or outdated?**

**How should the system respond when a suggested solution fails?**

**How can the outcome become useful knowledge for the future?**

These questions changed the way I think about AI agents.

Before working on this project, it was easy to think of an AI agent mainly as a system that receives a prompt and generates an answer. Through RECALL, I understood that a useful engineering agent is more like a complete workflow.

It needs context, memory, reasoning, feedback, and failure handling.

RECALL was a team effort, with each member contributing to a different part of the system.

**Neeraj** worked on the project foundation, Hindsight research, backend, and overall architecture.

**Adarsh** contributed to Hindsight integration, backend development, and architecture.

**Nikhil** worked on integrating memory capabilities within the agent.

**Dhanu** — my contribution — focused on feature research, workflow design, testing, debugging, and understanding how the incident-response process could use historical knowledge effectively.

**Sekhar** worked on UI/UX and testing.

**Vamsi** contributed to UI/UX, testing, and refinement.

Although our responsibilities were different, the components were connected. The memory system, backend, agent workflow, interface, and testing all had to work together to turn the concept into a usable system.

Working on RECALL gave me a practical understanding of how an AI concept can be transformed into a structured, memory-driven engineering workflow.

The most important lesson for me was that **memory alone is not intelligence**. What matters is how the system retrieves relevant memories, evaluates them against the current situation, uses them as evidence, learns from the final outcome, and improves future responses.

RECALL therefore represents more than an AI system that responds to incidents. The broader idea is to create an agent that can build on previous experience instead of starting from zero every time an incident occurs.

The workflow can be summarized in four simple words:

**Respond. Learn. Remember. Improve.**