From Logs to Fixes: Inside the HealOps AI Engine

Beyond Regex

Most log analysis tools rely on Regular Expressions (Regex). You define a pattern, and if a log line matches, it triggers an alert. This is brittle and requires constant maintenance.

HealOps takes a different approach. We use specialized Large Language Models (LLMs) trained on millions of stack traces and infrastructure logs.

Understanding Context

When an error occurs, it's rarely an isolated event. It's usually a sequence of events. Our AI engine looks at the temporal context:

[INFO] User 123 initiated checkout
[WARN] Payment gateway latency > 500ms
[ERROR] Connection reset by peer

A regex might just catch the ERROR. HealOps understands that the latency warning was the precursor, and the user action was the trigger.

Generative Remediation

For code-level errors, HealOps integrates with your version control system. When it sees a ReferenceError or a NullPointerException, it doesn't just report it.

1. It locates the exact line of code in your git repository.
2. It analyzes the commit history to see who changed it last.
3. It generates a proposed patch (e.g., adding a null check).

This "Hot-Patch" capability allows for rapid mitigation of bugs that slip through testing, keeping your production environment stable while a permanent fix is deployed.