Introduction
When an application experiences an outage or unexpected behavior, engineers often need to investigate the issue under time pressure. They may examine logs, review dashboards, read old incident reports, and ask teammates whether a similar problem has happened before.
One challenge is that useful troubleshooting knowledge can be scattered across different tools and documents. Even when a team has resolved a similar issue previously, finding that information at the right moment can be difficult.
This was one of the problems behind RecallOps, a memory-first AI incident recovery assistant developed to explore how persistent memory can support incident investigation.
As part of the project, my contributions focused on testing the application and its workflows, checking Hindsight memory retrieval and evidence, reviewing safety limitations, researching AI memory approaches, and preparing technical documentation.
This article shares the testing and reliability perspective of the project, including why evidence, relevance, and human oversight matter when building an AI-assisted engineering tool.
1. Understanding the Problem
A troubleshooting suggestion is not automatically a correct solution.
Two incidents may have similar error messages but completely different causes. For example, a timeout in a payment service and a timeout in an inventory service may appear similar, but their underlying dependencies and operational conditions can differ.
If an assistant retrieves an old solution and presents it as a verified fix for a different service, an engineer could waste time or apply an inappropriate change.
This made one question especially important during our work:
How can an incident assistant reuse previous knowledge without presenting every retrieved memory as a confirmed solution?
RecallOps approaches this through persistent incident memory, evidence-backed retrieval, and service-aware relevance classification. It is designed to assist engineers in investigating incidents, not to replace their judgment.
2. Exploring Persistent Memory with Hindsight
RecallOps uses Hindsight Cloud as its persistent memory layer.
Unlike a workflow that relies only on information supplied in a single request, persistent memory allows incident-related information to be retained and recalled across investigation sessions.
In RecallOps, the workflow includes:
- An engineer submits an incident with its service, severity, and description.
- The application extracts relevant symptoms and queries Hindsight for related memories.
- Retrieved evidence is used to produce investigation suggestions.
- The engineer reviews the evidence and investigates the issue.
- The engineer can record an outcome, including whether a troubleshooting attempt succeeded or failed.
During testing, checking memory retrieval was important because the usefulness of an investigation depends not only on whether information is returned, but also on whether the retrieved information is relevant and represented accurately.
A retrieved memory should remain traceable to its source. The assistant should not turn an observation into a confirmed resolution or imply that a previous result proves what is happening in the current incident.
3. Testing Incident Workflows
Testing RecallOps involved examining the application as an engineer would use it.
The dashboard and incident workflows were checked to understand how an incident moves from intake to analysis and how retrieved evidence is displayed.
The main workflow areas included:
- Submitting an incident with a service name, severity, and description.
- Opening an incident and requesting an analysis.
- Reviewing recalled memories and the investigation suggestions associated with them.
- Checking whether the evidence source is visible.
- Recording an engineer-confirmed outcome through the application.
These checks helped examine the complete user journey rather than treating individual API responses as the only measure of functionality.
The project also includes backend automated tests. The backend test suite reported 79 passing tests during the team's verification. This is evidence that the tested cases passed, not a guarantee that every possible incident scenario or production environment has been covered.
The application was tested using synthetic incident data. The results should therefore be understood as project testing results, not as proof of performance during a real production outage.
4. Why Cross-Service Relevance Matters
One important area of testing and review was the distinction between same-service evidence and cross-service references.
Consider a synthetic payment-gateway incident involving high latency and an exhausted outbound connection pool.
Now consider a new incident in the inventory API. Both incidents might contain timeout-related symptoms, but that similarity does not establish that the inventory API has the same problem.
RecallOps distinguishes between different kinds of retrieved information:
Direct historical evidence: A memory associated with the same service and relevant incident context.
Cross-service reference: A potentially useful memory from a different service. It can provide investigation ideas, but it is not automatically a verified resolution for the current service.
General reference: Broader technical information that may help an engineer investigate but does not establish the current incident's cause.
This distinction is reflected in the application's analysis and interface, including cross-service reference labels and warnings.
From a testing perspective, the important question is not simply whether a memory was retrieved. It is whether the application communicates what that memory actually establishes.
A similar symptom is a starting point for investigation, not proof of a shared root cause.
5. Documentation and Safety Boundaries
Technical documentation is important for making a project understandable and reproducible.
My documentation contributions included work on the README and setup instructions, helping explain the technology stack, project structure, configuration, and steps needed to run the application locally.
The project uses React, Vite, and Tailwind CSS for the frontend, FastAPI and Python for the backend, SQLite for local incident and outcome records, and Hindsight Cloud for persistent memory.
Documentation also needs to communicate limitations clearly.
RecallOps is an investigation assistant. It does not automatically execute production commands, make infrastructure changes, or independently establish a guaranteed root cause.
The engineer remains responsible for reviewing evidence, validating suggestions, and deciding what actions to take.
Outcome recording is also intended to be engineer-controlled. A suggestion should not be treated as successful simply because it was displayed or retrieved from memory. Both successful and unsuccessful troubleshooting attempts can provide useful learning material when their outcomes are recorded appropriately.
These boundaries are important because a tool that helps engineers investigate incidents should make uncertainty visible instead of hiding it behind confident language.
6. Lessons Learned
Working on testing, documentation, and research for RecallOps highlighted several lessons.
First, successful retrieval is not the same as useful retrieval. Returning a memory is only one part of the process. Relevance, evidence attribution, and context are equally important.
Second, similar symptoms do not guarantee similar causes. Service context matters, and a cross-service reference should be presented as an investigation lead rather than a proven fix.
Third, safety is part of the user experience. Labels, warnings, evidence references, and clear explanations help engineers understand the limits of an assistant's suggestions.
Fourth, testing should cover the complete workflow. An incident assistant must be examined from incident intake through memory retrieval, evidence presentation, and outcome recording.
Finally, documentation is part of engineering quality. A project is easier to evaluate, reproduce, and improve when its setup instructions, architecture, limitations, and testing approach are clearly explained.
7. What's Next?
RecallOps is a working prototype and a foundation for further experimentation.
Future improvements could include broader incident datasets, more extensive integration testing, evaluation of retrieval relevance, and comparisons across different memory-retrieval strategies.
Additional work could also explore how an LLM-based investigation layer might use retrieved evidence while preserving source attribution, uncertainty, and engineer oversight.
Any such improvements would need to be evaluated rather than assumed to improve incident response automatically.
Conclusion
RecallOps explores how persistent memory can help engineering teams make past troubleshooting knowledge more accessible during incident investigation.
From the testing and documentation perspective, the project reinforced an important principle: an AI-assisted engineering tool should not only retrieve information, but also communicate where that information came from, how relevant it is, and what it does not prove.
By combining persistent memory with evidence-backed suggestions and engineer-controlled outcomes, RecallOps provides a foundation for exploring more reliable and transparent incident investigation workflows.
The project also demonstrates why testing, documentation, and safety review are important parts of building AI-assisted developer tools.
Project: RecallOps — A Memory-First AI Incident Recovery Assistant
Source Code: https://github.com/Karunya0612/RecallOps
Hindsight: https://github.com/vectorize-io/hindsight
Hindsight Documentation: https://hindsight.vectorize.io/
Agent Memory Resource: https://vectorize.io/
Top comments (0)