When a production incident fires at 2 AM, on-call engineers face an avalanche of signals — cascading alerts, gigabytes of logs, erratic metric graphs, and fragmented traces — with no clear starting point. The DevOps Root Cause Analysis Agent replaces hours of manual triage with an automated AI pipeline that ingests those signals, correlates them across sources, and delivers a ranked list of root cause hypotheses with supporting evidence, so your team can act rather than hunt.Documentation Index
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Who Is This For?
The RCA Agent is purpose-built for engineers who own production reliability:- DevOps Engineers managing CI/CD pipelines and deployment health
- Site Reliability Engineers (SREs) who respond to on-call alerts and conduct postmortems
- Platform Engineers responsible for observability infrastructure and incident tooling
The Problem: Manual Incident Investigation Is Expensive Toil
Traditional incident investigation is a deeply manual process. An engineer must simultaneously query a logging platform, pull metric dashboards, inspect distributed traces, cross-reference deployment history, and form mental hypotheses — all under pressure. Even experienced SREs commonly spend 30–90 minutes on root cause identification for non-trivial incidents. Postmortem data consistently shows that the majority of that time is spent correlating data across tools, not applying engineering judgment. The RCA Agent automates the correlation step entirely. It pulls raw signals from your connected data sources, normalizes them into a unified timeline, and hands them to an LLM reasoning chain trained to identify causal patterns. What you receive is not a raw dump of data — it is a prioritized set of hypotheses, each scored by evidence strength and accompanied by the specific log lines, metric anomalies, or trace spans that support it.Core Value Proposition
Multi-Signal Correlation
Simultaneously ingests logs, time-series metrics, and distributed traces for a given incident window, then identifies cross-source causal patterns that would take a human engineer many minutes to spot manually.
AI-Ranked Hypotheses
Each candidate root cause is scored by the strength and consistency of its supporting evidence. The highest-confidence hypothesis appears first, with verbatim excerpts from the underlying signals.
Async, Non-Blocking Pipeline
Long-running data fetches and LLM inference happen in Celery worker tasks backed by Redis, keeping the Streamlit UI responsive and allowing parallel ingestion from multiple data sources.
Interactive Investigation UI
A Streamlit dashboard lets engineers configure incident context, trigger analyses, drill into per-hypothesis evidence, and export findings — without writing a single query.
Key Components
The agent is composed of four tightly integrated layers that carry a signal from raw ingestion to actionable output.Agent Core
The agent core is the orchestration brain. It accepts an incident context (time window, affected service, alert description) and dispatches the analysis pipeline. It is responsible for assembling the final ranked output from the results produced by each pipeline stage.Data Source Connectors
Connectors are modular Python classes that know how to query a specific observability backend — a logging platform, a metrics store, or a tracing system. Each connector normalizes its output into a shared schema so that downstream pipeline stages can reason across sources without caring about the originating tool.Celery / Redis Async Pipeline
Each pipeline stage — signal ingestion, preprocessing, LLM inference — runs as a Celery task. Redis acts as the message broker, queuing tasks and passing results between stages. This architecture means that fetching logs from a slow API and querying a metrics endpoint happen concurrently, not sequentially, dramatically reducing end-to-end latency.Streamlit UI
The Streamlit front-end (app/ui.py) provides the human interface to the agent. Engineers fill in an incident form, submit it, and watch results populate in real time as Celery tasks complete. Each ranked hypothesis is displayed as an expandable card showing its confidence score and the evidence excerpts that support it.
Explore the Documentation
Quickstart
Install the agent locally, configure your environment, and trigger your first analysis in under 10 minutes.
How It Works
A technical walkthrough of the four-stage AI pipeline, the async architecture, and the Streamlit UI integration.
Environment Configuration
All supported environment variables for LLM providers, Redis, data source connectors, and agent tuning.
Running an Analysis
A step-by-step guide to structuring an incident context and interpreting the ranked hypothesis output.
The DevOps RCA Agent is in active development. APIs, configuration keys, and pipeline internals may change between releases. Pin to a specific Git tag in production environments and review the changelog before upgrading.