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AI for Automation Difficulty: Advanced ClaudeChatGPT

Confidence-Gated Auto-Remediation Prompt

Design an auto-remediation system that acts only when diagnostic confidence clears a tier-specific threshold — auto-fixing high-confidence low-risk issues, proposing fixes for medium confidence, and paging a human for everything else, with full dry-run and rollback.

Target user
SREs building safe automated remediation pipelines
Difficulty
Advanced
Tools
Claude, ChatGPT

The prompt

You are a reliability engineer designing auto-remediation where the central question is not "can we fix this?" but "are we sure enough, and is it safe enough, to fix it without a human?" Build a system that gates every action on both confidence and blast radius.

I will provide:
- The failure types we want to remediate and their current manual fixes
- Diagnostic signals available per failure type
- Blast radius / impact of each remediation
- Our environment and existing automation tooling
- Risk tolerance and audit requirements

Your tasks:

1. **Confidence scoring** — define how diagnostic confidence is computed per failure (corroborating signals, recent-change correlation, historical match rate). Be explicit about what lowers confidence.

2. **The decision matrix** — cross confidence (low/med/high) with risk (low/med/high). Specify the action for each cell: auto-fix, propose-and-confirm, or page-human. High-risk is human-only regardless of confidence.

3. **Mandatory dry-run** — every mutating remediation runs a dry-run/plan first and validates the expected change before executing. Block if the plan looks wrong.

4. **Verification and rollback** — after acting, confirm the fix worked; if not, auto-rollback and escalate. Define the success check and the rollback step per remediation.

5. **Rate limiting and circuit breaker** — cap auto-fixes per window; trip the breaker after consecutive failures and fall back to human-only.

6. **Audit** — log signal → confidence → decision → action → result for every event, including the no-action decisions.

Output as: (a) the confidence-scoring method, (b) the confidence × risk decision matrix, (c) one remediation fully specified end-to-end (detect → score → dry-run → act → verify → rollback), (d) rate-limit/circuit-breaker config, (e) the audit schema and rollout starting in propose-only mode.

Anti-patterns to reject: auto-acting on high-confidence high-risk fixes, skipping dry-run, no rollback path, hiding the confidence number, and a breaker that never trips.
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