Analyze logs and correlate incidents with OpenAI and Slack

Overview

This workflow implements an AI-powered incident investigation and root cause analysis system that automatically analyzes operational signals when a system incident occurs.

When an incident is triggered via webhook, the workflow gathers operational context including application logs, system metrics, recent deployments, and feature flag changes. These signals are processed to detect error patterns, cluster similar failures, and correlate them with recent system changes.

The workflow uses vector embeddings to group similar log messages, allowing it to detect dominant failure patterns across services. It then aligns these failures with contextual events such as deployments, configuration changes, or traffic spikes to identify potential causal relationships.

An AI agent analyzes all available evidence and generates structured root cause hypotheses, including confidence scores, supporting evidence, and recommended remediation actions.

Finally, the workflow posts a detailed incident report directly to Slack, enabling engineering teams to quickly understand the issue and respond faster.

This architecture helps teams reduce mean time to resolution (MTTR) by automating the early stages of incident investigation.

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