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3. Automated Error Analysis via LLM

Date: 2026-01-22

Status

Accepted

Context

When an incident occurs (e.g., 500 Internal Server Error), the "Mean Time To Resolution" (MTTR) is dominated by the initial investigation: 1. Finding the log. 2. Reading the stack trace. 3. Googling the error code.

We want to reduce this time by automating the "Level 1 Support" analysis.

Decision

We will integrate a Large Language Model (LLM) Agent to analyze error logs on-demand.

  • Trigger: Authenticated admin hits /debug/summarize-errors.
  • Input: The raw app.json error logs (Sanitized of PII where possible).
  • Output: A structured JSON report containing root_cause, severity, and remediation_steps.
  • Providers: We support Groq (for speed/cost) and OpenAI (for quality), selectable via config.

Consequences

Positive

  • Speed: Instant root cause analysis without manual grep.
  • Accessibility: Allows non-expert engineers to understand complex failures (e.g., "Connection Refused at port 4317").
  • Standardization: The output is structured, allowing downstream automation (e.g., auto-creating Jira tickets).

Negative

  • Cost: API calls cost money (though Groq/Llama is very cheap).
  • Privacy: Logs are sent to a third-party API. Strict data scrubbing is required to ensure no sensitive customer PII (credit cards, passwords) is leaked to the LLM.