Building RecallOps: An AI Incident Response Agent That Learns from Past Outages

Ansh-Bnsal — Sun, 07 Jun 2026 13:55:43 +0000

Operational incidents are inevitable in modern software systems. APIs fail, login systems break after key rotation, releases go wrong, and infrastructure dependencies become bottlenecks at the worst possible moment. In open-source communities and technical teams, the real challenge is not just resolving incidents quickly — it is making sure each incident makes the next response better. That is exactly the problem we tackled in our hackathon project, RecallOps: an AI incident response agent that remembers historical incidents, understands patterns from past failures, and uses that memory to recommend better actions when a similar issue appears again.

Problem Statement

Traditional incident response is often too dependent on human memory. Even if teams write postmortems, that knowledge usually stays buried in documents, chats, or tickets. So when a new incident happens, responders often start from scratch: checking recent deploys, scanning dashboards, and trying to guess the root cause under pressure. This creates slower triage, inconsistent decisions, and repeated mistakes. Our goal was to build an agent that can retain incident knowledge — root causes, signals, mitigations, resolutions, and preventive actions — and then reuse that knowledge when a similar operational or security incident happens in the future.

The Core Idea

We designed RecallOps around one simple belief: memory should change the first response. Instead of giving only a generic troubleshooting checklist, the system compares two paths side by side:

a stateless generic triage plan, and
a memory-backed recommendation informed by similar historical incidents.

This makes the value of memory visible. In the UI, the “Without Memory” panel stays broad and procedural, while the “With Hindsight Memory” panel recalls concrete patterns such as Redis cache stampedes, JWKS rotation failures, CoreDNS saturation, database migration locks, token leaks, and broken package releases. The result is faster hypothesis generation, more targeted mitigation steps, and a clearer explanation of why the agent is recommending those actions.

Solution Approach

RecallOps uses a memory system inspired by how real incident responders think. First, we seed the system with historical incidents. Each incident contains structured fields such as service name, severity, symptoms, telemetry signals, root cause, mitigation, resolution, prevention, and timeline. When a new incident is submitted, the agent converts it into a searchable query, retrieves similar memories, and synthesizes a response based on matching evidence. After the incident is resolved, the postmortem can be retained back into memory so the system improves over time. This creates a closed learning loop: remember → retrieve → recommend → retain.

Why We Used Hindsight

Our project integrates with Hindsight, a memory system designed specifically for AI agents. Hindsight provides three capabilities that were central to our solution: retain() to store memories, recall() to retrieve relevant past knowledge, and reflect() to reason over that memory and generate a grounded response. Hindsight also supports multiple memory types and multi-strategy retrieval, which makes it more suitable for incident knowledge than plain semantic search alone.

In Hindsight, retrieval is not limited to vector similarity. Its documented multi-strategy search includes semantic, keyword, graph, and temporal retrieval. That is especially useful for incident response, where responders often care about exact technical terms, time relationships, or correlated entities — not just semantically similar text. This fits our use case well because outage investigation depends heavily on signals like service names, metrics, token identifiers, DNS failures, and timeline clues.

Architecture and Design

At a high level, RecallOps has three layers: a frontend incident console, a backend orchestration layer, and a memory layer. The frontend is built in React and lets users select or enter an active incident, run analysis, inspect evidence, and retain a new postmortem. The backend is built with Express and acts as the control plane: it validates incident payloads, seeds the memory bank, performs analysis, and stores newly learned incidents. The memory layer is powered by Hindsight, which acts as the long-term knowledge system for incident history.

One of the best parts of the design is that the system explicitly compares generic reasoning against memory-backed reasoning. In the backend, a generic recommendation function produces a broad triage playbook focused on standard actions like freezing deploys, checking dependencies, and communicating status. The memory-based path, on the other hand, retrieves relevant incidents and turns them into more specific root-cause hypotheses, mitigation plans, verification steps, and prevention guidance. This side-by-side comparison clearly demonstrates the problem statement: historical knowledge measurably improves future incident management.

We also added an evidence panel to improve explainability. Rather than only outputting a recommendation, the system shows which remembered incidents influenced the answer. This is important in incident response because responders need to trust the recommendation quickly. Showing the related incidents, signals, and source text makes the AI less of a black box and more like an experienced teammate referencing old postmortems.

How the Workflow Works

The workflow starts when a user enters an incident such as “Checkout API timing out after flash coupon launch” or “Login failures after emergency key rotation.” The system sends that input to the backend, where it is validated using schema rules. The backend then builds a query from the incident’s title, service, severity, symptoms, signals, and any actions already attempted. If Hindsight is available, the app recalls relevant incidents from the memory bank and then uses reflection to generate a recommendation grounded in those memories. If Hindsight is unavailable, the app still works in offline demo mode using locally seeded incidents and a scoring system for similarity matching.

After analysis, the user sees two outputs: a generic plan and a memory-backed plan. The memory-backed recommendation includes likely root causes, immediate actions, mitigation steps, communication guidance, verification steps, and prevention ideas, all influenced by prior incidents. Finally, once the current incident is resolved, the user can submit the root cause, mitigation, resolution, and prevention notes through the “Retain Resolution” flow, allowing the system to store that incident for future reuse.

Incident Corpus We Seeded

To make the demo realistic, we prepared a diverse set of historical incidents. These include:

checkout latency caused by a Redis cache stampede,
cluster-wide DNS failures from CoreDNS saturation,
login failures after JWKS key rotation,
dashboard outages caused by database migration locks,
public token exposure in open-source infrastructure, and
broken package releases caused by stale build caches.

These examples were deliberately chosen to cover both operational incidents and security/release incidents, which makes the agent more representative of real technical communities and open-source maintenance workflows.

Technologies Used

RecallOps is built using a modern TypeScript-first stack. The frontend uses React for the interface and Vite for development and bundling. The backend uses Express running in TypeScript. We used Zod for request validation, which helps ensure incident inputs and retained postmortems are well-structured. For memory operations, we integrated the official @vectorize-io/hindsight-client package. The development workflow uses tsx and concurrently to run the frontend and backend together.

We also designed the project to support both Hindsight Cloud and local/open-source Hindsight. Hindsight Cloud exposes a hosted API endpoint and API key flow, while the local setup allows developers to run Hindsight through Docker for experimentation. This made our project flexible for both demo reliability and future extensibility.

Challenges We Encountered

One of the biggest challenges was turning postmortems into reusable memory. Incident reports are often messy, verbose, and inconsistent. To make the AI useful, we had to define a clear structure for incidents: trigger, symptoms, signals, root cause, mitigation, resolution, prevention, and timeline. That structure became the foundation for both retrieval quality and explainable recommendations.

Another challenge was demonstrating value clearly during a hackathon demo. It is easy to say “this AI has memory,” but much harder to show why that matters. That is why the side-by-side comparison became such an important design decision. Instead of only showing one answer, we show how generic triage differs from memory-backed triage. This made the improvement visible, intuitive, and judge-friendly.

We also had to handle resilience and demo safety. External AI or memory services may not always be reachable during a live presentation, so we implemented an offline fallback mode. In this mode, the UI still works using the same seeded incident corpus and a local similarity matcher. That ensured the product could still demonstrate the core concept even if the external memory service failed.

A final challenge was balancing specificity with trust. In incident response, overconfident AI is dangerous. The system needs to suggest likely patterns without pretending it is certain. Our design addresses that by surfacing evidence, confidence, matching incidents, and verification steps, so the responder can validate the recommendation against live telemetry instead of blindly following it.

What Makes This Project Different

Many AI assistants can summarize alerts or generate troubleshooting checklists, but RecallOps focuses on something more practical: organizational memory for incidents. It does not just answer “what should I do now?” — it answers “what happened before that looks like this, and how did we solve it?” That shift is important because incident response is rarely about raw intelligence alone; it is about recognizing patterns under pressure and acting on prior learning.

By combining memory retention, similarity-based retrieval, reflection, and explainable evidence, RecallOps behaves less like a chatbot and more like an incident responder who has actually seen similar failures before.

Future Scope

There are several ways this project can evolve beyond the hackathon. First, RecallOps can be integrated with real incident sources such as PagerDuty, Slack, GitHub Issues, Statuspage, or postmortem databases so memories are retained automatically instead of manually entered. Second, the agent can become more observability-aware by directly consuming metrics, logs, deployment events, and traces to enrich the incident query. Third, recommendation quality can improve with feedback loops where responders rate whether a recalled incident was actually useful.

We also see strong potential for team-specific memory banks. Different organizations respond to incidents differently, and memory should adapt to that culture. Hindsight supports configurable bank behavior through mission and reasoning settings, which opens the door for incident agents tailored to platform teams, open-source maintainers, DevOps teams, or security responders.

In the long run, this concept can grow into a full incident command copilot: suggesting likely root causes, drafting stakeholder updates, recommending rollback or containment actions, and automatically preserving lessons learned after resolution.

Conclusion

RecallOps was our attempt to solve a very real engineering problem: teams keep learning from incidents, but their tools rarely do. By building an AI incident response agent with memory, we showed how historical knowledge can improve future outage handling. The project demonstrates that better incident response is not only about faster AI generation — it is about structured memory, relevant recall, grounded recommendations, and continuous learning after every incident. That is the idea we wanted to bring to this hackathon, and it is the direction we believe practical AI tooling should move toward.

DEV Community: Ansh-Bnsal