{"slug": "i-built-an-ai-team-that-investigates-production-incidents-better-than-most", "title": "I Built an AI Team That Investigates Production Incidents Better Than Most Engineers", "summary": "A developer built TattvaAI, an AI-powered incident investigation platform that uses six specialized agents to autonomously analyze production incidents. The system, built for the SigNoz 'Agents of SigNoz' Hackathon, correlates traces, logs, metrics, and dependencies to identify root causes faster than manual investigation.", "body_md": "*Built for the **SigNoz \"Agents of SigNoz\" Hackathon — July 2026***\n\nEvery engineer who has been on call knows the feeling.\n\nAn alert fires in the middle of the night. Dashboards turn red. Error rates spike. Customers begin reporting failures.\n\nThe incident itself may eventually be simple—a database timeout, an exhausted Redis connection pool, a failing downstream dependency—but discovering the actual root cause is often the hardest part.\n\nThe investigation usually looks something like this:\n\nBy the time the root cause is identified, hours may have passed.\n\nIn many organizations, the fix itself takes only a few minutes.\n\nThe investigation is the real bottleneck.\n\nThat observation became the motivation behind **TattvaAI**.\n\n**TattvaAI** is an AI-powered incident investigation platform that automatically performs the work experienced Site Reliability Engineers (SREs) do during production incidents.\n\nInstead of presenting engineers with dashboards full of raw telemetry, TattvaAI investigates the incident autonomously, correlates evidence from multiple observability signals, identifies the most probable root cause, and explains its reasoning.\n\nRather than replacing engineers, it removes the repetitive investigative work so engineers can focus on making decisions and restoring production faster.\n\nThe entire system was built for the **SigNoz \"Agents of SigNoz\" Hackathon** using SigNoz as the observability platform and LangGraph to orchestrate multiple specialized AI agents.\n\nToday's observability platforms have become extremely good at collecting telemetry.\n\nThey provide:\n\nThe data exists.\n\nThe problem is interpretation.\n\nWhen production fails, engineers still have to manually connect all these signals together.\n\nA single incident often requires answering questions like:\n\nAnswering these questions manually consumes most of the incident response timeline.\n\nThe challenge isn't a lack of observability.\n\nIt's a lack of automated reasoning.\n\nInstead of creating one large AI assistant, I designed **six independent AI agents**, each responsible for one domain of observability.\n\nEvery agent specializes in gathering evidence.\n\nLater, a reasoning engine combines that evidence into a complete investigation.\n\nResponsible for analyzing distributed traces.\n\nIt identifies:\n\nInstead of simply reporting slow requests, it determines where latency originates inside an entire request path.\n\nResponsible for log intelligence.\n\nIt searches for:\n\nRather than reading thousands of log entries manually, the agent extracts only the evidence relevant to the incident.\n\nResponsible for infrastructure and application health.\n\nIt continuously evaluates:\n\nThe goal is identifying abnormal behavior that explains observed failures.\n\nDistributed systems rarely fail in isolation.\n\nThis agent maps:\n\nInstead of asking *\"Which service is broken?\"*, it answers:\n\n*\"Which service caused every other service to fail?\"*\n\nModern monitoring systems often generate dozens of alerts during a single outage.\n\nMany are secondary effects.\n\nThis agent correlates alerts together and removes noise by identifying which alerts are symptoms versus actual causes.\n\nProduction systems repeat themselves.\n\nThis agent searches previous incidents and historical telemetry to answer questions such as:\n\nInstead of starting every investigation from scratch, TattvaAI learns from operational history.\n\nEach AI agent works independently.\n\nTheir findings become structured evidence rather than isolated observations.\n\nLangGraph orchestrates the entire workflow by:\n\nThis architecture makes investigations deterministic, explainable, and extensible.\n\nAdding another specialist agent later becomes straightforward.\n\nOne of the biggest design decisions was integrating directly with **SigNoz**.\n\nInstead of treating SigNoz as another dashboard, TattvaAI accesses telemetry through the **Model Context Protocol (MCP)**.\n\nThis allows agents to retrieve:\n\nwithout manually navigating dashboards.\n\nSigNoz becomes the source of truth, while TattvaAI becomes the reasoning layer built on top of it.\n\nThis separation keeps observability and intelligence cleanly decoupled.\n\nTo evaluate the platform, I created a realistic microservices environment with multiple containerized services generating live telemetry.\n\nOne investigation looked like this.\n\nAn e-commerce checkout system suddenly experienced an **18% error rate**.\n\nAn engineer would typically:\n\nTotal investigation time:\n\n**2–3 hours**\n\nWithin seconds the AI agents produced the following evidence.\n\n**Trace Agent**\n\nPayment service latency increased to 4800 ms, over four times the normal baseline.\n\n**Logs Agent**\n\nDetected 143 Redis timeout exceptions originating from the payment service.\n\n**Metrics Agent**\n\nRedis CPU reached 98%, and connection pool utilization hit maximum capacity.\n\n**Dependency Agent**\n\nFailure propagation path identified:\n\nGateway → Order Service → Payment Service → Redis\n\n**Alert Agent**\n\nSeven alerts detected.\n\nFive classified as downstream symptoms.\n\n**Historical Agent**\n\n94% similarity to a previous Redis connection pool exhaustion incident.\n\nAfter correlating all evidence, the reasoning engine concluded:\n\n**Root Cause**\n\nRedis connection pool exhaustion.\n\n**Confidence**\n\n96%\n\n**Recommended Actions**\n\nTotal investigation time:\n\n**Approximately 30 seconds.**\n\nTattvaAI combines modern AI orchestration with production-ready backend engineering.\n\nTo validate the platform end-to-end, I built an entire observable microservices environment.\n\nIt includes:\n\nThis allows TattvaAI to investigate realistic production incidents instead of synthetic examples.\n\nAfter running repeated investigations inside the demo environment, the results were encouraging.\n\nOne principle guided the design of TattvaAI from the beginning.\n\n**Every conclusion should be explainable.**\n\nInstead of producing a single answer, every investigation includes:\n\nIf TattvaAI concludes that Redis caused the outage, it also shows:\n\nThe goal is transparency.\n\nEngineers should understand *why* the AI reached its conclusion.\n\nAlthough developed during a hackathon, TattvaAI was designed with production deployment in mind.\n\nIt includes:\n\nThe architecture is intentionally modular so organizations can extend it with additional agents and custom workflows.\n\nThe observability ecosystem has matured significantly over the past decade.\n\nWe no longer struggle to collect telemetry.\n\nWe struggle to interpret it quickly enough when production is failing.\n\nTattvaAI shifts observability from passive monitoring to active investigation.\n\nInstead of showing engineers hundreds of charts, it answers the question they actually care about:\n\nWhat is broken, why did it break, and what should I do next?\n\nThat shift has implications beyond reducing Mean Time to Resolution (MTTR).\n\nIt can also help teams:\n\nThis project represents the first version of a much larger vision.\n\nFuture directions include:\n\nBuilding TattvaAI fundamentally changed the way I think about observability.\n\nThe future of incident response is not simply collecting more telemetry or building more dashboards.\n\nIt is creating intelligent systems capable of reasoning across telemetry the same way experienced engineers do.\n\nSigNoz provides an excellent foundation for collecting and exposing observability data.\n\nTattvaAI builds on that foundation by transforming telemetry into explanations, hypotheses, and actionable decisions.\n\nIf AI can reduce a two-hour investigation to thirty seconds while remaining transparent and explainable, then incident response becomes less about searching for information and more about solving problems.\n\nAnd that is exactly the future I wanted to build.\n\n**TattvaAI** was built for the **SigNoz \"Agents of SigNoz\" Hackathon (July 2026)**.\n\nThe project is open source, and I would love to hear feedback from the observability community.\n\nIf you're interested in AI-powered incident investigation, explore the project, experiment with it, and let me know what improvements you'd like to see.\n\nHappy building—and may your next production incident take seconds instead of hours to understand.", "url": "https://wpnews.pro/news/i-built-an-ai-team-that-investigates-production-incidents-better-than-most", "canonical_source": "https://dev.to/animbargi5art/i-built-an-ai-team-that-investigates-production-incidents-better-than-most-engineers-5hmi", "published_at": "2026-07-27 01:49:54+00:00", "updated_at": "2026-07-27 02:29:15.743222+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-agents", "ai-tools", "developer-tools", "machine-learning"], "entities": ["SigNoz", "TattvaAI", "LangGraph", "Model Context Protocol"], "alternates": {"html": "https://wpnews.pro/news/i-built-an-ai-team-that-investigates-production-incidents-better-than-most", "markdown": "https://wpnews.pro/news/i-built-an-ai-team-that-investigates-production-incidents-better-than-most.md", "text": 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