{"slug": "article-resolveiq-building-an-ai-incident-response-agent-that-learns-from", "title": "Article: ResolveIQ — Building an AI Incident Response Agent That Learns From Production Failures", "summary": "A developer built ResolveIQ, an AI incident response agent that pairs an LLM reasoning layer with long-term memory so that resolutions from past production incidents inform future investigations. The system stores structured incident data in PostgreSQL, recalls historical experience from Hindsight, and routes the combined context through a Groq-powered reasoning layer, with resolved incidents retained as memory for later use.", "body_md": "Introduction\n\nProduction incidents are an unavoidable part of software engineering. When an application starts returning HTTP 500 errors, database timeouts, authentication failures, or unexpected latency spikes, engineers need to quickly understand what happened, identify the root cause, and restore the service. The challenge is that many incidents are not completely new. Similar problems may have happened weeks or months earlier, but the knowledge about how those incidents were diagnosed and resolved is often difficult to retrieve.\n\nThis led us to build ResolveIQ, an AI-powered incident response agent that combines AI reasoning with long-term memory. The goal is not simply to ask an LLM to analyze an error, but to give the agent access to the organization's previous incident experience so that it can use what was learned from earlier failures.\n\nThe project was built around the idea of AI agents that learn using Hindsight.\n\nThe Problem\n\nTraditional incident investigation often involves multiple steps. An engineer receives an alert, checks application logs, searches monitoring dashboards, investigates the database or infrastructure, looks through previous tickets, and then decides how to resolve the problem.\n\nThe difficult part is that previous solutions may already contain valuable information.\n\nFor example, suppose a Payment API starts returning HTTP 500 errors because the database connection pool is exhausted. If the same problem occurred previously and the engineering team solved it by increasing the connection pool from 50 to 100, that historical experience could be extremely useful.\n\nHowever, a normal LLM does not automatically know this organization's previous incident history.\n\nResolveIQ addresses this gap by giving the AI agent long-term incident memory.\n\nWhat Is ResolveIQ?\n\nResolveIQ is an AI-powered incident response system that helps engineers investigate production incidents using both current incident information and historical experience.\n\nThe system has five major stages:\n\nIncident\n\n   ↓\n\nHindsight Recall\n\n   ↓\n\nGroq AI Reasoning\n\n   ↓\n\nEngineer Action\n\n   ↓\n\nHindsight Retain\n\n   ↓\n\nFuture Incident\n\nThe important idea is that the system does not stop after generating a recommendation.\n\nAfter the incident is resolved, the resolution becomes part of the agent's memory.\n\nTherefore:\n\nEvery resolved incident becomes experience for future investigations.\n\nResolveIQ Dashboard\n\nThe first component is the ResolveIQ Dashboard.\n\nThe dashboard provides engineers with a centralized view of active production incidents. Each incident contains information such as the incident ID, service name, severity, error rate, affected users, current status, and incident signals.\n\nFor our demonstration, one of the main incidents is:\n\nINC-245 — Payment Service HTTP 500 Spike\n\nThe incident contains information such as a 35% error rate, approximately 12,430 affected users, and signals indicating database connection acquisition timeouts and connection pool saturation.\n\nInstead of manually searching through different systems, the engineer can select the incident directly from the ResolveIQ dashboard.\n\nAI Investigation\n\nAfter selecting an incident, the engineer can start an AI Investigation.\n\nThe React frontend sends the incident information to the FastAPI backend. The backend coordinates the investigation by retrieving structured incident information from PostgreSQL and requesting relevant historical experience from Hindsight.\n\nThe current incident is then combined with the historical information before being sent to the Groq-powered reasoning layer.\n\nThis gives the AI more context than simply looking at the current error.\n\nPostgreSQL — Structured Incident Data\n\nPostgreSQL is responsible for storing structured operational information.\n\nFor example:\n\nIncident ID\n\nService\n\nSeverity\n\nError Rate\n\nAffected Users\n\nLogs\n\nStatus\n\nResolution\n\nFor INC-245, PostgreSQL contains the structured details of the Payment Service incident.\n\nPostgreSQL answers the question:\n\n“What is happening in the current incident?”\n\nHindsight answers a different question:\n\n“Have we experienced something like this before?”\n\nHindsight — Long-Term Memory\n\nHindsight is the memory layer of ResolveIQ.\n\nHindsight is designed as an agent memory system focused on helping agents learn rather than simply remember. Its documented core operations include Retain, Recall, and Reflect.\n\nIn ResolveIQ, we use Hindsight to store production incidents, root causes, resolutions, and outcomes.\n\nFor example, a previous incident might be stored as:\n\nINC-127 — Payment API Failure\n\nProblem:\n\nDatabase connection pool exhaustion.\n\nRoot Cause:\n\nConnection pool reached its maximum capacity.\n\nResolution:\n\nIncrease connection pool from 50 to 100.\n\nOutcome:\n\nSuccessful.\n\nThis information becomes part of the ResolveIQ incident memory.\n\nHindsight Recall\n\nWhen INC-245 occurs, ResolveIQ doesn't only analyze the current error.\n\nIt asks Hindsight to recall relevant previous experiences.\n\nThe query can be conceptually expressed as:\n\nFind previous incidents where:\n\nPayment API\n\n+\n\nHTTP 500\n\n+\n\nDatabase connection timeout\n\n+\n\nConnection pool exhaustion\n\nHindsight retrieves relevant memories using its memory and retrieval mechanisms. Its architecture combines different retrieval signals including semantic, keyword, graph, and temporal information.\n\nFor our example, Hindsight can retrieve INC-127 because it contains a similar Payment API failure caused by database connection pool exhaustion.\n\nThis is where the memory layer becomes valuable.\n\nGroq AI Reasoning\n\nThe next stage is Groq AI reasoning.\n\nGroq receives two important pieces of information:\n\nCurrent Incident\n\n       +\n\nHistorical Experience\n\nThe current incident tells the model what is happening now.\n\nHindsight tells the model what happened previously.\n\nGroq then reasons over both pieces of information to generate an investigation result.\n\nCurrent:\n\nPayment Service → HTTP 500\n\nConnectionPoolTimeoutException\n\nConnection pool saturated\n\nPrevious:\n\nPayment API → HTTP 500\n\nConnection pool exhausted\n\nPool increased 50 → 100\n\nSuccessful\n\nThe agent can then identify the likely relationship between the two incidents.\n\nRoot Cause Analysis\n\nFor our demonstration, the probable root cause is database connection pool exhaustion.\n\nThe sequence is:\n\nHigh production load\n\n       ↓\n\nDatabase connections become saturated\n\n       ↓\n\nNew requests cannot obtain connections\n\n       ↓\n\nConnection acquisition timeout\n\n       ↓\n\nPayment request fails\n\n       ↓\n\nHTTP 500\n\nResolveIQ presents this reasoning to the engineer instead of requiring them to manually reconstruct the entire chain.\n\nRecommended Resolution\n\nBased on the current incident and the historical incident retrieved from Hindsight, the agent can recommend:\n\nIncrease database connection pool\n\nfrom 50 → 100\n\nRestart affected service\n\nMonitor HTTP 500 rate\n\nand database connections\n\nThe recommendation is presented to the engineer as decision support.\n\nThe system does not need to automatically execute potentially destructive production operations.\n\nThe engineer remains responsible for reviewing and applying the appropriate action.\n\nLearning From the Resolution\n\nThis is the most important part of ResolveIQ.\n\nAfter the engineer resolves INC-245, the system records the outcome.\n\nINC-245\n\nRoot Cause:\n\nDatabase connection pool exhaustion\n\nResolution:\n\nPool increased from 50 → 100\n\nOutcome:\n\nSuccessful\n\nResolveIQ then retains this information in Hindsight.\n\nThis means INC-245 becomes another piece of historical experience.\n\nThe next time a similar incident occurs, Hindsight can potentially retrieve both INC-127 and INC-245.\n\nThe Continuous Learning Loop\n\nThis creates the core learning loop:\n\n```\n    New Incident\n         ↓\n  Hindsight Recall\n         ↓\n  Historical Experience\n         ↓\n   Groq AI Reasoning\n         ↓\n Root Cause + Recommendation\n         ↓\n   Engineer Applies Fix\n         ↓\n    Hindsight Retain\n         ↓\n   New Learned Experience\n         ↓\n    Future Incident\n```\n\nThis is the central concept behind ResolveIQ.\n\nThe system becomes more useful as more resolved incidents are retained in its memory.\n\nHindsight Memory Graph\n\nOne of the interesting aspects of Hindsight is its ability to organize memories and relationships.\n\nIn our Hindsight interface, memories can be visualized as a graph containing nodes and connections. Hindsight's documented architecture separates different types of information and relationships, allowing agents to work with structured long-term memory rather than only flat text.\n\nFor ResolveIQ, the memory graph can conceptually connect:\n\nPayment Service\n\n       |\n\n       ↓\n\nHTTP 500\n\n       |\n\n       ↓\n\nConnection Timeout\n\n       |\n\n       ↓\n\nDatabase Connection Pool\n\n       |\n\n       ↓\n\nPool Exhaustion\n\n       |\n\n       ↓\n\nResolution\n\n       |\n\n       ↓\n\n50 → 100\n\n       |\n\n       ↓\n\nSuccessful Outcome\n\nThis gives us a visual representation of how different pieces of incident knowledge are connected.\n\nSystem Architecture\n\nThe overall ResolveIQ architecture is:\n\n```\n             React Frontend\n                   |\n                   ↓\n            FastAPI Backend\n                   |\n         ┌─────────┴─────────┐\n         ↓                   ↓\n    PostgreSQL            Hindsight\n Structured Data       Long-Term Memory\n         |                   |\n         └─────────┬─────────┘\n                   ↓\n              Groq LLM\n                   |\n                   ↓\n           AI Investigation\n                   |\n                   ↓\n      Root Cause + Recommendation\n                   |\n                   ↓\n            Engineer Action\n                   |\n                   ↓\n            Hindsight Retain\n```\n\nEach component has a specific responsibility.\n\nReact provides the engineer interface.\n\nFastAPI coordinates the backend workflow.\n\nPostgreSQL stores structured incident information.\n\nHindsight provides long-term memory.\n\nGroq performs AI reasoning.\n\nTogether, these components form the ResolveIQ incident investigation and learning system.\n\nWhy This Approach?\n\nThe main idea is that an AI agent should not have to start from zero every time an incident occurs.\n\nA conventional approach might look like:\n\nNew Incident\n\n     ↓\n\nLLM analyzes current information\n\n     ↓\n\nRecommendation\n\nResolveIQ adds organizational experience:\n\nNew Incident\n\n     ↓\n\nRetrieve Previous Experience\n\n     ↓\n\nLLM analyzes Current + Historical Information\n\n     ↓\n\nRecommendation\n\n     ↓\n\nResolution\n\n     ↓\n\nStore New Experience\n\nThis makes long-term memory a central part of the incident-response workflow.\n\nTechnology Stack\n\nResolveIQ was built using:\n\nReact.js — Frontend dashboard\n\nFastAPI — Backend API\n\nPython — Agent and backend services\n\nPostgreSQL — Structured incident storage\n\nHindsight — Long-term agent memory\n\nGroq — LLM reasoning\n\nHindsight is open source under the MIT license, and its official repository documents its memory concepts and integration options.\n\nFuture Improvements\n\nThe current project demonstrates the core memory-driven incident response workflow. A production version could connect ResolveIQ directly to observability and monitoring systems.\n\nApplication\n\n     ↓\n\nLogs / Metrics / Alerts\n\n     ↓\n\nIncident Detection\n\n     ↓\n\nResolveIQ\n\n     ↓\n\nHindsight + Groq\n\n     ↓\n\nEngineer\n\nFuture versions could integrate with systems such as application logging, metrics, alerting, tracing, and incident-management platforms.\n\nAnother potential improvement would be adding more sophisticated incident correlation, evaluation of recommended resolutions, and automated post-incident summaries.\n\nConclusion\n\nResolveIQ explores how long-term memory can change the way AI agents assist software engineers.\n\nInstead of treating every production incident as an isolated event, ResolveIQ connects the current incident with previous engineering experience. Hindsight provides the memory layer, Groq provides reasoning, PostgreSQL stores structured incident data, FastAPI coordinates the backend workflow, and React provides the engineer-facing interface.\n\nThe central idea is simple:\n\nProduction incidents should not just be resolved. They should become knowledge for the next incident.\n\nThat is the idea behind ResolveIQ — an AI incident response agent that learns from every production failure.", "url": "https://wpnews.pro/news/article-resolveiq-building-an-ai-incident-response-agent-that-learns-from", "canonical_source": "https://dev.to/daggupati_leelanaren_ec7/article-resolveiq-building-an-ai-incident-response-agent-that-learns-from-production-failures-2eni", "published_at": "2026-09-28 11:37:33+00:00", "updated_at": "2026-09-28 11:49:57.033440+00:00", "lang": "en", "topics": ["ai-agents", "ai-tools", "large-language-models", "mlops"], "entities": ["ResolveIQ", "Hindsight", "Groq", "PostgreSQL", "FastAPI", "React"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/article-resolveiq-building-an-ai-incident-response-agent-that-learns-from", "markdown": "https://wpnews.pro/news/article-resolveiq-building-an-ai-incident-response-agent-that-learns-from.md", "text": "https://wpnews.pro/news/article-resolveiq-building-an-ai-incident-response-agent-that-learns-from.txt", "jsonld": "https://wpnews.pro/news/article-resolveiq-building-an-ai-incident-response-agent-that-learns-from.jsonld"}}