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Product

RCA Assistant

AI-assisted root cause analysis for Linux infrastructure.

RCA Assistant is a multi-agent diagnostic system for IT operations teams. It gathers the evidence around an incident, correlates signals across sources, and proposes a structured root cause hypothesis, with the reasoning laid out so engineers can verify it.

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How it works

From incident to explained root cause

  1. Capture

    Collects system state around the incident: logs, monitoring metrics, topology and the content of the ticket.

  2. Correlate

    Specialised agents analyse the signals in parallel and combine their findings across data sources.

  3. Explain

    Returns a structured root cause hypothesis with supporting evidence and a transparent reasoning path in a chat interface.

Key capabilities

Built for teams under incident pressure

  • Works with the data you already have

    Uses existing logs, monitoring metrics, knowledge base and ticket content. No additional services to install.

  • Transparent reasoning

    Every hypothesis comes with the steps and evidence behind it, so the diagnosis can be checked rather than taken on trust.

  • Minutes, not hours

    Designed to deliver an automated first-pass diagnosis within minutes of incident detection.

  • Post-mortem ready

    Agent output can be used directly in post-mortem documentation.

Who it's for

  • System administrators
  • DevOps and SRE teams
  • IT operations teams
  • IT infrastructure managed service providers

Research behind it

RCA Assistant grows out of our R&D project on adaptive context-aware chunking of heterogeneous technical data and hierarchical multi-agent architectures for Linux diagnostics, co-financed by the European Funds.

About the EU-funded project

Experience

Case study

Autonomous RCA Agent for Linux Infrastructure

Client: Qlos

Challenge
Root cause analysis for infrastructure incidents required manual data gathering from multiple monitoring systems, logs, and ticketing tools. Under incident pressure, that was slow and error-prone.
What we built
A multi-agent pipeline that automatically captures system state (logs, metrics, topology), correlates signals across sources, and generates a structured root cause hypothesis with supporting evidence.
Result
Automated first-pass diagnosis available as soon as an incident is detected; agent output used directly in post-mortem documentation.

“The agent handles the part of an incident nobody enjoys: pulling logs, metrics and history together. By the time an engineer opens the ticket, there is already a first hypothesis with evidence behind it.”

Incident Manager, Qlos

Contact

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