During an incident, ChatGPT should gather evidence, not become root

During an incident, the expensive part is rarely writing one SQL query.

It is preserving the same service, environment, tenant, deployment, and time window while responders move between errors, changes, health events, and runbooks.

A ChatGPT database connection can help maintain that context.

It should not become an unrestricted production console.

I would expose narrow read-only tools such as:

• get_incident_timeline
• compare_error_signatures
• list_recent_changes
• summarize_affected_tenants
• get_evidence_receipt

Each tool should require explicit service, environment, and time scope. It should enforce maximum windows, result limits, allowed sources, and tenant boundaries.

Every answer should carry evidence:

• source and freshness
• time window and filters
• row count and truncation
• schema or metric version
• query or trace ID

The model can summarize observations into a timeline or hypothesis, but it should label inference separately and show when sources disagree.

Remediation belongs on another authority path. Reading a feature-flag change must not silently grant permission to revert it. Write tools need preview, validation, idempotency, approval where required, and execution status.

The goal is not an AI that declares a root cause quickly.

It is a workflow that helps responders gather reviewable evidence without creating a second outage.

Full workflow: Connect ChatGPT to a SQL database for incident operations