Initial commit: LLM Automation Docs & Remediation Engine v2.0
Features: - Automated datacenter documentation generation - MCP integration for device connectivity - Auto-remediation engine with safety checks - Multi-factor reliability scoring (0-100%) - Human feedback learning loop - Pattern recognition and continuous improvement - Agentic chat support with AI - API for ticket resolution - Frontend React with Material-UI - CI/CD pipelines (GitLab + Gitea) - Docker & Kubernetes deployment - Complete documentation and guides v2.0 Highlights: - Auto-remediation with write operations (disabled by default) - Reliability calculator with 4-factor scoring - Human feedback system for continuous learning - Pattern-based progressive automation - Approval workflow for critical actions - Full audit trail and rollback capability
This commit is contained in:
LLM Automation System
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AUTO_REMEDIATION_GUIDE.md
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AUTO_REMEDIATION_GUIDE.md
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# 🤖 Auto-Remediation System - Complete Documentation
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## 📋 Table of Contents
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1. [Overview](#overview)
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2. [Safety First Design](#safety-first-design)
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3. [Reliability Scoring System](#reliability-scoring-system)
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4. [Human Feedback Loop](#human-feedback-loop)
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5. [Decision Engine](#decision-engine)
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6. [Auto-Remediation Execution](#auto-remediation-execution)
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7. [Pattern Learning](#pattern-learning)
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8. [API Usage](#api-usage)
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9. [Configuration](#configuration)
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10. [Monitoring & Analytics](#monitoring--analytics)
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---
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## Overview
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The **Auto-Remediation System** enables AI to autonomously resolve infrastructure issues by executing write operations on your systems. This is a **production-grade** implementation with extensive safety checks, human oversight, and continuous learning.
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### Key Features
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✅ **Safety-First**: Auto-remediation **disabled by default**
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✅ **Reliability Scoring**: Multi-factor confidence calculation (0-100%)
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✅ **Human Feedback**: Continuous learning from user feedback
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✅ **Pattern Recognition**: Learns from similar issues
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✅ **Approval Workflow**: Critical actions require human approval
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✅ **Full Audit Trail**: Every action logged with rollback capability
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✅ **Progressive Automation**: Decisions improve over time based on success rate
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---
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## Safety First Design
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### 🛡️ Default State: DISABLED
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```python
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# Example: Ticket submission
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{
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"ticket_id": "INC-001",
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"description": "Problem description",
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"enable_auto_remediation": false # ← DEFAULT: Disabled
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}
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```
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**Auto-remediation must be explicitly enabled for each ticket.**
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### Safety Layers
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1. **Explicit Enablement**: Must opt-in per ticket
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2. **Reliability Thresholds**: Minimum confidence required
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3. **Action Classification**: Safe vs. Critical operations
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4. **Pre-execution Checks**: System health, backups, rate limits
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5. **Human Approval**: Required for low-reliability or critical actions
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6. **Post-execution Validation**: Verify success
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7. **Rollback Capability**: Undo on failure
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### Action Classification
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```python
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class RemediationAction(str, enum.Enum):
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READ_ONLY = "read_only" # No changes (default)
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SAFE_WRITE = "safe_write" # Non-destructive (restart, clear cache)
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CRITICAL_WRITE = "critical_write" # Potentially destructive (delete, modify)
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```
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**Critical actions ALWAYS require human approval**, regardless of confidence.
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---
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## Reliability Scoring System
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### Multi-Factor Calculation
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The reliability score (0-100%) is calculated from **4 components**:
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```python
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Reliability Score = (
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AI Confidence × 25% + # Model's own confidence
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Human Feedback × 30% + # Historical feedback quality
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Success History × 25% + # Past resolution success rate
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Pattern Match × 20% # Similarity to known patterns
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)
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```
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### Component Details
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#### 1. AI Confidence (25%)
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- Direct from Claude Sonnet 4.5
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- Based on documentation quality and analysis certainty
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- Range: 0-1 converted to 0-100%
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#### 2. Human Feedback (30%)
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- Weighted by recency (recent feedback = more weight)
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- Considers:
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- Positive/Negative/Neutral feedback type
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- Star ratings (1-5)
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- Resolution accuracy
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- Action effectiveness
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```python
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feedback_score = (
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positive_feedback_rate × 100 +
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average_rating / 5 × 100
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) / 2
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```
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#### 3. Historical Success (25%)
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- Success rate in same category (last 6 months)
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- Formula: `resolved_tickets / total_tickets × 100`
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#### 4. Pattern Match (20%)
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- Similarity to known, resolved patterns
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- Requires ≥3 similar tickets for pattern
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- Boosts score if pattern has positive feedback
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### Confidence Levels
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| Score Range | Level | Description |
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|-------------|-----------|-------------|
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| 90-100% | Very High | Excellent track record, safe to auto-execute |
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| 75-89% | High | Good reliability, may require approval |
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| 60-74% | Medium | Moderate confidence, approval recommended |
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| 0-59% | Low | Low confidence, manual review required |
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### Example Breakdown
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```json
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{
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"overall_score": 87.5,
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"confidence_level": "high",
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"breakdown": {
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"ai_confidence": "92%",
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"human_validation": "85%",
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"success_history": "90%",
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"pattern_recognition": "82%"
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}
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}
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```
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---
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## Human Feedback Loop
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### Feedback Collection
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After each ticket resolution, collect structured feedback:
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```python
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{
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"ticket_id": "INC-001",
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"feedback_type": "positive|negative|neutral",
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"rating": 5, # 1-5 stars
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"was_helpful": true,
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"resolution_accurate": true,
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"actions_worked": true,
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# Optional detailed feedback
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"comment": "Great resolution!",
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"what_worked": "The restart fixed it",
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"what_didnt_work": null,
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"suggestions": "Could add more details",
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# If AI failed, what actually worked?
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"actual_resolution": "Had to increase memory instead",
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"actual_actions_taken": [...],
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"time_to_resolve": 30.0 # minutes
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}
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```
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### Feedback Impact
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1. **Immediate**: Updates ticket reliability score
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2. **Pattern Learning**: Strengthens/weakens pattern eligibility
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3. **Future Decisions**: Influences similar ticket handling
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4. **Auto-remediation Eligibility**: Pattern becomes eligible after:
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- ≥5 occurrences
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- ≥85% positive feedback rate
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- ≥85% average reliability score
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### Feedback Analytics
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Track feedback trends:
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- Positive/Negative/Neutral distribution
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- Average ratings by category
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- Resolution accuracy trends
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- Action success rates
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---
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## Decision Engine
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### Decision Flow
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```
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1. Check: Auto-remediation enabled for ticket?
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├─ NO → Skip auto-remediation
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└─ YES → Continue
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2. Get applicable policy for category
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├─ No policy → Require manual approval
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└─ Policy exists → Continue
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3. Classify action risk level
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├─ READ_ONLY → Low risk
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├─ SAFE_WRITE → Medium risk
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└─ CRITICAL_WRITE → High risk
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4. Check confidence & reliability thresholds
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├─ Below minimum → Reject
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└─ Above minimum → Continue
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5. Perform safety checks
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├─ Pre-checks failed → Reject
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└─ All passed → Continue
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6. Check pattern eligibility
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├─ Unknown pattern → Require approval
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└─ Known good pattern → Continue
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7. Determine approval requirement
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├─ Reliability ≥ auto_approve_threshold → Auto-approve
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├─ Critical action → Require approval
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└─ Otherwise → Follow policy
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8. Execute or await approval
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```
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### Decision Example
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```json
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{
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"allowed": true,
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"action_type": "safe_write",
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"requires_approval": false,
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"reasoning": [
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"All checks passed",
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"Auto-approved: reliability 92% >= 90%"
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],
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"safety_checks": {
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"time_window_ok": true,
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"rate_limit_ok": true,
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"backup_available": true,
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"system_healthy": true,
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"all_passed": true
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},
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"risk_level": "medium"
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}
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```
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---
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## Auto-Remediation Execution
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### Execution Flow
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```python
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async def execute_remediation(ticket, actions, decision):
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# 1. Verify decision allows execution
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if not decision['allowed']:
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return error
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# 2. Check approval if required
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if decision['requires_approval']:
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if not has_approval(ticket):
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return "awaiting_approval"
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# 3. Execute each action with safety
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for action in actions:
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# Pre-execution check
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pre_check = await check_system_health()
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if not pre_check.passed:
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rollback()
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return error
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# Execute action via MCP
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result = await execute_via_mcp(action)
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# Post-execution verification
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post_check = await verify_success()
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if not post_check.passed:
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rollback()
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return error
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# Log action
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log_remediation(action, result)
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return success
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```
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### Supported Operations
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#### VMware
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- `restart_vm` - Graceful VM restart
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- `snapshot_vm` - Create snapshot
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- `increase_memory` - Increase VM memory
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- `increase_cpu` - Add vCPUs
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#### Kubernetes
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- `restart_pod` - Delete pod (recreate)
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- `scale_deployment` - Change replica count
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- `rollback_deployment` - Rollback to previous version
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#### Network
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- `clear_interface_errors` - Clear interface counters
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- `enable_port` - Enable disabled port
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- `restart_interface` - Bounce interface
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#### Storage
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- `expand_volume` - Increase volume size
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- `clear_snapshots` - Remove old snapshots
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#### OpenStack
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- `reboot_instance` - Soft reboot instance
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- `resize_instance` - Change instance flavor
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### Safety Checks
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**Pre-execution:**
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- System health check (CPU, memory, disk)
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- Backup availability verification
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- Rate limit check (max 10/hour)
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- Time window check (maintenance hours)
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**Post-execution:**
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- Resource health verification
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- Service availability check
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- Performance metrics validation
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### Rollback
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If any action fails:
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1. Stop execution immediately
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2. Log failure details
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3. Execute rollback procedures
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4. Notify administrators
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5. Update ticket status to `partially_remediated`
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---
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## Pattern Learning
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### Pattern Identification
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```python
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# Generate pattern signature
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pattern = {
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'category': 'network',
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'key_terms': ['vlan', 'connectivity', 'timeout'],
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'hash': sha256(signature)
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}
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```
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### Pattern Statistics
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Tracked for each pattern:
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- **Occurrence count**: How many times seen
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- **Success/failure counts**: Resolution outcomes
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- **Feedback distribution**: Positive/negative/neutral
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- **Average confidence**: Mean AI confidence
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- **Average reliability**: Mean reliability score
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- **Auto-remediation success rate**: % of successful auto-fixes
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### Pattern Eligibility
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Pattern becomes eligible for auto-remediation when:
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```python
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if (
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pattern.occurrence_count >= 5 and
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pattern.positive_feedback_rate >= 0.85 and
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pattern.avg_reliability_score >= 85.0 and
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pattern.auto_remediation_success_rate >= 0.85
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):
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pattern.eligible_for_auto_remediation = True
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```
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### Pattern Evolution
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```
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Initial State:
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├─ occurrence_count: 1
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├─ eligible_for_auto_remediation: false
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└─ Manual resolution only
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After 5+ occurrences with good feedback:
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├─ occurrence_count: 7
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├─ positive_feedback_rate: 0.85
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├─ avg_reliability_score: 87.0
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├─ eligible_for_auto_remediation: true
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└─ Can trigger auto-remediation
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After 20+ occurrences:
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├─ occurrence_count: 24
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├─ auto_remediation_success_rate: 0.92
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├─ Very high confidence
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└─ Auto-remediation without approval
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```
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---
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## API Usage
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### Create Ticket with Auto-Remediation
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```bash
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curl -X POST http://localhost:8000/api/v1/tickets \
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-H "Content-Type: application/json" \
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-d '{
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"ticket_id": "INC-12345",
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"title": "Service down",
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"description": "Web service not responding on port 8080",
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"category": "server",
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"enable_auto_remediation": true
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}'
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```
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**Response:**
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```json
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{
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"ticket_id": "INC-12345",
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"status": "processing",
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"auto_remediation_enabled": true,
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"confidence_score": 0.0,
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"reliability_score": null
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}
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```
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### Check Ticket Status
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```bash
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curl http://localhost:8000/api/v1/tickets/INC-12345
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```
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**Response:**
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```json
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{
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"ticket_id": "INC-12345",
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"status": "resolved",
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"resolution": "Service was restarted successfully...",
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"suggested_actions": [
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{"action": "Restart web service", "system": "prod-web-01"}
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],
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"confidence_score": 0.92,
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"reliability_score": 87.5,
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"reliability_breakdown": {
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"overall_score": 87.5,
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"confidence_level": "high",
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"breakdown": {...}
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},
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"auto_remediation_enabled": true,
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"auto_remediation_executed": true,
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"remediation_decision": {
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"allowed": true,
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"requires_approval": false,
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"action_type": "safe_write"
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},
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"remediation_results": {
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"success": true,
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"executed_actions": [...]
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}
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||||
}
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```
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### Submit Feedback
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```bash
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curl -X POST http://localhost:8000/api/v1/feedback \
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-H "Content-Type: application/json" \
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-d '{
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||||
"ticket_id": "INC-12345",
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"feedback_type": "positive",
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||||
"rating": 5,
|
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"was_helpful": true,
|
||||
"resolution_accurate": true,
|
||||
"actions_worked": true,
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"comment": "Perfect resolution, service is back up!"
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}'
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```
|
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|
||||
### Approve Remediation
|
||||
|
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For tickets requiring approval:
|
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|
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```bash
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curl -X POST http://localhost:8000/api/v1/tickets/INC-12345/approve-remediation \
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-H "Content-Type: application/json" \
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-d '{
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"ticket_id": "INC-12345",
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"approve": true,
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"approver": "john.doe@company.com",
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"comment": "Approved for execution"
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}'
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```
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### Get Analytics
|
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|
||||
```bash
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# Reliability statistics
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curl http://localhost:8000/api/v1/stats/reliability?days=30
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# Auto-remediation statistics
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curl http://localhost:8000/api/v1/stats/auto-remediation?days=30
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# Learned patterns
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curl http://localhost:8000/api/v1/patterns?category=network&min_occurrences=5
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```
|
||||
|
||||
---
|
||||
|
||||
## Configuration
|
||||
|
||||
### Auto-Remediation Policy
|
||||
|
||||
```python
|
||||
policy = AutoRemediationPolicy(
|
||||
name="network-auto-remediation",
|
||||
category="network",
|
||||
|
||||
# Thresholds
|
||||
min_confidence_score=0.85, # 85% AI confidence required
|
||||
min_reliability_score=80.0, # 80% reliability required
|
||||
min_similar_tickets=5, # Need 5+ similar resolved tickets
|
||||
min_positive_feedback_rate=0.8, # 80% positive feedback required
|
||||
|
||||
# Allowed actions
|
||||
allowed_action_types=["safe_write"],
|
||||
allowed_systems=["network"],
|
||||
forbidden_commands=["delete", "format", "shutdown"],
|
||||
|
||||
# Time restrictions
|
||||
allowed_hours_start=22, # 10 PM
|
||||
allowed_hours_end=6, # 6 AM
|
||||
allowed_days=["monday", "tuesday", "wednesday", "thursday", "friday"],
|
||||
|
||||
# Approval
|
||||
requires_approval=True,
|
||||
auto_approve_threshold=90.0, # Auto-approve if reliability ≥ 90%
|
||||
approvers=["admin@company.com"],
|
||||
|
||||
# Safety
|
||||
max_actions_per_hour=10,
|
||||
requires_rollback_plan=True,
|
||||
requires_backup=True,
|
||||
|
||||
# Status
|
||||
enabled=True
|
||||
)
|
||||
```
|
||||
|
||||
### Environment Variables
|
||||
|
||||
```bash
|
||||
# Enable/disable auto-remediation globally
|
||||
AUTO_REMEDIATION_ENABLED=true
|
||||
|
||||
# Global safety settings
|
||||
AUTO_REMEDIATION_MAX_ACTIONS_PER_HOUR=10
|
||||
AUTO_REMEDIATION_REQUIRE_APPROVAL=true
|
||||
AUTO_REMEDIATION_MIN_RELIABILITY=85.0
|
||||
|
||||
# Pattern learning
|
||||
PATTERN_MIN_OCCURRENCES=5
|
||||
PATTERN_MIN_POSITIVE_RATE=0.85
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Monitoring & Analytics
|
||||
|
||||
### Key Metrics
|
||||
|
||||
```python
|
||||
# Reliability metrics
|
||||
- avg_reliability_score: Average across all tickets
|
||||
- avg_confidence_score: Average AI confidence
|
||||
- resolution_rate: % of tickets resolved
|
||||
|
||||
# Auto-remediation metrics
|
||||
- execution_rate: % of enabled tickets that were auto-remediated
|
||||
- success_rate: % of auto-remediation actions that succeeded
|
||||
- approval_rate: % requiring human approval
|
||||
|
||||
# Feedback metrics
|
||||
- positive_feedback_rate: % positive feedback
|
||||
- negative_feedback_rate: % negative feedback
|
||||
- avg_rating: Average star rating (1-5)
|
||||
|
||||
# Pattern metrics
|
||||
- eligible_patterns: # of patterns eligible for auto-remediation
|
||||
- pattern_success_rate: Success rate across all patterns
|
||||
```
|
||||
|
||||
### Grafana Dashboards
|
||||
|
||||
Example metrics:
|
||||
|
||||
```promql
|
||||
# Reliability score trend
|
||||
avg(datacenter_docs_reliability_score) by (category)
|
||||
|
||||
# Auto-remediation success rate
|
||||
rate(datacenter_docs_auto_remediation_success_total[1h]) /
|
||||
rate(datacenter_docs_auto_remediation_attempts_total[1h])
|
||||
|
||||
# Feedback sentiment
|
||||
sum(datacenter_docs_feedback_total) by (type)
|
||||
```
|
||||
|
||||
### Alerts
|
||||
|
||||
```yaml
|
||||
# Low reliability alert
|
||||
- alert: LowReliabilityScore
|
||||
expr: avg(datacenter_docs_reliability_score) < 70
|
||||
for: 1h
|
||||
annotations:
|
||||
summary: "Reliability score below threshold"
|
||||
|
||||
# High failure rate
|
||||
- alert: HighAutoRemediationFailureRate
|
||||
expr: rate(datacenter_docs_auto_remediation_failures_total[1h]) > 0.2
|
||||
for: 15m
|
||||
annotations:
|
||||
summary: "Auto-remediation failure rate > 20%"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Best Practices
|
||||
|
||||
### 1. Start Conservative
|
||||
|
||||
- Enable auto-remediation for **low-risk categories** first (e.g., cache clearing)
|
||||
- Set high thresholds initially (reliability ≥ 90%)
|
||||
- Require approvals for first 20-30 occurrences
|
||||
- Monitor closely and adjust based on results
|
||||
|
||||
### 2. Gradual Rollout
|
||||
|
||||
```
|
||||
Week 1-2: Enable for 5% of tickets
|
||||
Week 3-4: Increase to 20% if success rate > 90%
|
||||
Week 5-6: Increase to 50% if success rate > 85%
|
||||
Week 7+: Full rollout with dynamic thresholds
|
||||
```
|
||||
|
||||
### 3. Category-Specific Policies
|
||||
|
||||
Different categories need different thresholds:
|
||||
|
||||
| Category | Min Reliability | Auto-Approve | Reason |
|
||||
|----------|----------------|--------------|--------|
|
||||
| Cache | 75% | 85% | Low risk, frequent |
|
||||
| Network | 85% | 90% | Medium risk |
|
||||
| Storage | 90% | 95% | High risk |
|
||||
| Security | 95% | Never | Critical, always approve |
|
||||
|
||||
### 4. Human in the Loop
|
||||
|
||||
- Always collect feedback, even for successful auto-remediations
|
||||
- Review logs weekly
|
||||
- Adjust thresholds based on feedback trends
|
||||
- Disable patterns with declining success rates
|
||||
|
||||
### 5. Continuous Learning
|
||||
|
||||
- System improves over time through feedback
|
||||
- Patterns with 20+ occurrences and 90%+ success → Very high confidence
|
||||
- Allow system to become more autonomous as reliability proves out
|
||||
- But maintain human oversight for critical operations
|
||||
|
||||
---
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Auto-remediation not executing
|
||||
|
||||
**Check:**
|
||||
1. Is `enable_auto_remediation: true` in ticket?
|
||||
2. Is there an active policy for the category?
|
||||
3. Does confidence/reliability meet thresholds?
|
||||
4. Are safety checks passing?
|
||||
5. Does pattern meet eligibility requirements?
|
||||
|
||||
**Debug:**
|
||||
```bash
|
||||
# Check decision
|
||||
curl http://localhost:8000/api/v1/tickets/TICKET-ID | jq '.remediation_decision'
|
||||
|
||||
# Check logs
|
||||
curl http://localhost:8000/api/v1/tickets/TICKET-ID/remediation-logs
|
||||
```
|
||||
|
||||
### Low reliability scores
|
||||
|
||||
**Causes:**
|
||||
- Insufficient historical data
|
||||
- Negative feedback on category
|
||||
- Low pattern match confidence
|
||||
- Recent failures in category
|
||||
|
||||
**Solutions:**
|
||||
- Collect more feedback
|
||||
- Review and improve resolutions
|
||||
- Wait for more data points
|
||||
- Manually resolve similar tickets successfully
|
||||
|
||||
### Pattern not becoming eligible
|
||||
|
||||
**Requirements not met:**
|
||||
- Need ≥5 occurrences
|
||||
- Need ≥85% positive feedback
|
||||
- Need ≥85% average reliability
|
||||
|
||||
**Action:**
|
||||
- Continue resolving similar tickets
|
||||
- Ensure feedback is being collected
|
||||
- Check pattern stats: `GET /api/v1/patterns`
|
||||
|
||||
---
|
||||
|
||||
## Future Enhancements
|
||||
|
||||
- **Multi-step reasoning**: Complex workflows spanning multiple systems
|
||||
- **Predictive remediation**: Fix issues before they cause incidents
|
||||
- **A/B testing**: Compare different resolution strategies
|
||||
- **Reinforcement learning**: Optimize actions based on outcomes
|
||||
- **Natural language explanations**: Better transparency in decisions
|
||||
- **Cross-system orchestration**: Coordinated actions across infrastructure
|
||||
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
The **Auto-Remediation System** is designed for **safe, gradual automation** of infrastructure issue resolution:
|
||||
|
||||
1. ✅ **Disabled by default** - explicit opt-in per ticket
|
||||
2. ✅ **Multi-factor reliability** - comprehensive confidence calculation
|
||||
3. ✅ **Human feedback loop** - continuous learning and improvement
|
||||
4. ✅ **Pattern recognition** - learns from similar issues
|
||||
5. ✅ **Safety first** - extensive checks, approval workflows, rollback
|
||||
6. ✅ **Progressive automation** - system becomes more autonomous over time
|
||||
7. ✅ **Full observability** - complete audit trail and analytics
|
||||
|
||||
**Start small, monitor closely, scale gradually, and let the system learn.**
|
||||
|
||||
---
|
||||
|
||||
For support: automation-team@company.local
|
||||
Reference in New Issue
Block a user