AI Root Cause Analysis for Production Systems

What is root cause analysis?

Root cause analysis is the process of tracing a production symptom — an error spike, a slow endpoint, a failed request — back to the underlying change or condition that caused it. In distributed systems, the symptom and the cause are often several hops apart: a bad deploy in one service shows up as timeouts in another, three services downstream.

Traditional root cause analysis

Traditionally, an engineer does this correlation by hand: checking dashboards, grepping logs, pulling up traces, cross-referencing recent deploys, and asking around in Slack — often across three or four separate tools that don't share context with each other. It works, but it's slow, and it depends on the on-call engineer already knowing where to look.

Why production incidents are difficult to investigate

Modern systems are distributed, deployed frequently, and instrumented unevenly. A single incident can touch logs in one tool, traces in another, and a deploy history in a third — with no single place that shows how they relate. The investigation time isn't spent understanding the problem; it's spent finding and assembling the evidence.

How AI-assisted root cause analysis works

Instead of an engineer manually assembling evidence across tools, an AI-assisted workflow starts from telemetry that's already correlated in one place, analyzes it against recent Git and deployment history, and proposes a root cause hypothesis along with a suggested fix. The engineer's job shifts from "find the evidence" to "review the hypothesis."

Signals used in an investigation

• Logs — application and system-level error and event data
• Metrics — performance and resource trends around the incident window
• Traces — the request path across services
• Errors — exception details and stack traces
• Session context — what the affected user was doing when it happened
• Git / deployment context — what changed recently, and by whom

Limitations of AI

AI-assisted root cause analysis helps engineers understand what happened and accelerate resolution — it does not guarantee a correct diagnosis every time, and it shouldn't be treated as infallible. Novel failure modes, incidents caused by factors outside available telemetry (a third-party outage, a misconfigured firewall rule never logged), and ambiguous evidence can all lead to an incorrect or incomplete hypothesis. That's why human review stays in the loop.

Security and data control

Root cause analysis necessarily touches sensitive telemetry and, for code-fix suggestions, source code context. Where that analysis runs matters as much as how good it is — data sent to a third-party model you don't control is a genuine security consideration, not a footnote.

BYOK

BYOK (Bring Your Own Key) means AI-assisted resolution runs on your own LLM API key — for example, your own Azure OpenAI or Bedrock deployment — inside your own cloud boundary. Your source code and telemetry are never sent to Modulis or to a third-party model Modulis controls.

Human validation

AI-generated root causes and fix suggestions are a starting point for the reviewing engineer, not an automatic deploy. Treating human review as a required step — not an optional one — is what makes AI-assisted resolution safe to use on production systems.

How Modulis approaches AI-assisted resolution

Modulis correlates logs, traces, metrics, errors, session replay, and Git metadata into one investigation, then uses AI — running on your own model key (BYOK) — to propose a root cause summary and a PR-ready code fix. The engineer reviews the suggestion before it ships. Modulis doesn't claim to always find the root cause; it claims to get you to a well-informed hypothesis faster than manually correlating four separate tools.

FAQ