Building ResolveIQ: Giving AI Support Agents the Memory to Understand Recurring Issues

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║ BUILDING RESOLVEIQ: GIVING AI AGENTS LONG-TERM MEMORY ║
║ ║
║ By Srinidhi Guggilam ║
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║ ║
║ THE PROBLEM I WANTED TO SOLVE ║
║ ║
║ Production incidents are a regular part of software ║
║ engineering. When an application shows errors, database ║
║ timeouts, or unexpected failures, engineers need to ║
║ quickly understand what happened and decide what to do. ║
║ ║
║ The difficult part is that many incidents are not ║
║ completely new. A similar problem may have happened ║
║ earlier, but that previous experience can be difficult ║
║ to find when a new incident occurs. ║
║ ║
║ This led to the idea behind ResolveIQ: an AI-powered ║
║ incident response agent that can use both the current ║
║ incident and previous incident experience. ║
║ ║
║ WHAT IS RESOLVEIQ? ║
║ ║
║ ResolveIQ is designed to help engineers investigate ║
║ production incidents using AI reasoning together with ║
║ long-term memory. ║
║ ║
║ New Incident ║
║ ↓ ║
║ Hindsight Recall ║
║ ↓ ║
║ Historical Experience ║
║ ↓ ║
║ AI Reasoning ║
║ ↓ ║
║ Investigation & Recommendation ║
║ ↓ ║
║ Engineer Action ║
║ ↓ ║
║ Hindsight Retain ║
║ ║
║ WHY MEMORY MATTERS ║
║ ║
║ A normal AI model can analyze the information given to ║
║ it about the current incident, but it does not ║
║ automatically know an organization's previous incident ║
║ history. ║
║ ║
║ For example, a Payment API may previously have failed ║
║ because of database connection-pool exhaustion. If a ║
║ similar failure happens again, that historical ║
║ information can provide useful context. ║
║ ║
║ HOW HINDSIGHT IS USED ║
║ ║
║ Hindsight acts as the long-term memory layer in ║
║ ResolveIQ. Previous incidents can contain information ║
║ such as incident ID, service, problem, root cause, ║
║ resolution, and outcome. ║
║ ║
║ When a new incident occurs, ResolveIQ can recall relevant ║
║ previous incidents and provide that information to the ║
║ AI reasoning system. ║
║ ║
║ AI INVESTIGATION ║
║ ║
║ ResolveIQ combines the current incident information with ║
║ historical information retrieved from memory. The AI can ║
║ then reason about possible relationships between the two ║
║ incidents. ║
║ ║
║ SYSTEM ARCHITECTURE ║
║ ║
║ React Frontend ║
║ ↓ ║
║ FastAPI Backend ║
║ ↓ ║
║ ┌───────────┴───────────┐ ║
║ ↓ ↓ ║
║ PostgreSQL Hindsight ║
║ Structured Data Long-Term Memory ║
║ └───────────┬───────────┘ ║
║ ↓ ║
║ Groq LLM ║
║ ↓ ║
║ AI Investigation ║
║ ↓ ║
║ Root Cause + Recommendation ║
║ ║
║ THE LEARNING LOOP ║
║ ║
║ A resolved incident can become knowledge for future ║
║ incidents. This creates a continuous learning loop: ║
║ ║
║ New Incident → Recall → Historical Experience → ║
║ AI Reasoning → Resolution → Retain → Future Incident ║
║ ║
║ BEFORE MEMORY VS AFTER MEMORY ║
║ ║
║ BEFORE MEMORY: ║
║ New Incident → AI analyzes current information → ║
║ Recommendation ║
║ ║
║ AFTER MEMORY: ║
║ New Incident → Retrieve Previous Experience → ║
║ Combine Current + Historical Information → AI Reasoning ║
║ → Recommendation → Resolution → Store New Experience ║
║ ║
║ WHAT I LEARNED ║
║ ║
║ 1. AI needs more than reasoning. ║
║ 2. Memory should provide useful context. ║
║ 3. Resolved incidents can become knowledge. ║
║ 4. Human engineers still matter in the decision process. ║
║ ║
║ FUTURE IMPROVEMENTS ║
║ ║
║ ResolveIQ could be extended by connecting it with: ║
║ ║
║ • Application logs ║
║ • Monitoring systems ║
║ • Metrics ║
║ • Alerts ║
║ • Distributed tracing ║
║ • Incident-management systems ║
║ • Historical tickets ║
║ ║
║ CONCLUSION ║
║ ║
║ ResolveIQ explores how long-term memory can change the ║
║ way AI agents assist engineers. Instead of treating every ║
║ production incident as an isolated problem, the system ║
║ connects a new incident with relevant previous ║
║ experience. ║
║ ║
║ The central idea is simple: ║
║ ║
║ “Production incidents should not only be resolved. ║
║ They should become knowledge for future incidents.” ║
║ ║
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