triage-agent

name	GitHub Agentic Workflows Continuous AI Patterns
description	Comprehensive guide for continuous AI workflows including triage, review, maintenance, monitoring, scheduling strategies, event-driven automation, human-in-the-loop patterns, and feedback loops
license	Apache-2.0
version	2.0.0
last_updated	"2026-04-02T00:00:00.000Z"
tags	["github-agentic-workflows","continuous-ai","workflow-patterns","automation","triage","code-review","maintenance","monitoring","scheduling","event-driven","human-in-the-loop","feedback-loops"]

🔄 GitHub Agentic Workflows Continuous AI Patterns

🔴 AI FIRST Quality Principle

Apply the AI FIRST principle: never accept first-pass quality. Minimum 2 iterations. Read all output, improve every section. No shortcuts.

📋 Overview

This skill provides comprehensive patterns for implementing Continuous AI workflows with GitHub Agentic Workflows. Continuous AI extends CI/CD principles to AI-powered automation, enabling agents to continuously triage issues, review code, maintain repositories, and monitor systems with minimal human intervention.

What is Continuous AI?

Continuous AI is the practice of deploying AI agents that run continuously or on regular schedules to perform repetitive tasks, monitor systems, and maintain code quality without manual intervention:

• Continuous Triage: Automatically label, prioritize, and route issues and PRs
• Continuous Review: Automated code reviews on every PR
• Continuous Maintenance: Dependency updates, security patches, code refactoring
• Continuous Monitoring: System health, performance metrics, security alerts
• Feedback Loops: Learn from outcomes and improve over time

Why Continuous AI?

Traditional CI/CD focuses on build, test, and deploy. Continuous AI extends this to intelligent automation:

• ✅ 24/7 Operation: Agents work around the clock
• ✅ Instant Response: No waiting for human availability
• ✅ Consistency: Same quality standards applied every time
• ✅ Scalability: Handle thousands of issues, PRs, and alerts
• ✅ Cost Efficiency: Reduce manual work and accelerate development
• ✅ Knowledge Retention: Agents learn from history and feedback

---

🎯 Continuous AI Concept

The Continuous AI Loop

┌─────────────────────────────────────────────────────────────┐
│                   CONTINUOUS AI LOOP                        │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│   1. OBSERVE                                                │
│      └─> Events (issues, PRs, commits, alerts)             │
│                                                             │
│   2. ANALYZE                                                │
│      └─> Context, patterns, history                        │
│                                                             │
│   3. DECIDE                                                 │
│      └─> Determine action (or escalate to human)           │
│                                                             │
│   4. ACT                                                    │
│      └─> Execute action (label, review, fix)               │
│                                                             │
│   5. LEARN                                                  │
│      └─> Collect feedback, update models                   │
│                                                             │
│   6. REPEAT                                                 │
│      └─> Back to OBSERVE                                   │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Continuous AI Principles

1. Autonomy: Agents make decisions independently within defined boundaries
2. Observability: All actions are logged and auditable
3. Reversibility: Actions can be undone if incorrect
4. Human Oversight: Critical decisions require human approval
5. Continuous Learning: Agents improve from feedback
6. Graceful Degradation: Fall back to safer behavior on uncertainty

---

🏷️ Continuous Triage Pattern

Automatic Issue Labeling and Prioritization

# .github/workflows/continuous-triage.yml
name: Continuous Issue Triage
on:
  issues:
    types: [opened, edited]
  schedule:
    - cron: '0 */6 * * *'  # Every 6 hours

permissions:
  issues: write
  contents: read

jobs:
  triage-issues:
    runs-on: ubuntu-latest
    steps:
      - name: Harden Runner
        uses: step-security/harden-runner@v2
        with:
          egress-policy: audit
      
      - name: Checkout
        uses: actions/checkout@v4
      
      - name: Triage New Issues
        uses: github/copilot-agent@v1
        with:
          agent: triage-agent
          task: |
            Analyze and triage all issues:
            1. Classify by type (bug, feature, docs, security)
            2. Assign priority (P0-critical, P1-high, P2-medium, P3-low)
            3. Apply relevant labels
            4. Detect duplicates
            5. Auto-assign to appropriate team/person
            6. Add to project board
            
            For security issues:
            - Label as 'security'
            - Set priority to P0
            - Assign to security team
            - Create private security advisory if needed
            
            For bugs with reproduction steps:
            - Label as 'bug'
            - Add 'reproduction-provided' label
            - Higher priority than bugs without reproduction
            
            For feature requests:
            - Label as 'enhancement'
            - Check if duplicate of existing feature request
            - Assign to product team for review
      
      - name: Comment on Triaged Issues
        run: |
          gh issue comment ${{ github.event.issue.number }} \
            --body "🤖 Issue automatically triaged by AI agent"
        env:
          GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}

Triage Agent Implementation

# .github/agents/triage-agent.yaml
---
name: triage-agent
description: Automatically triage issues and PRs
tools:
  - github-issue_read
  - github-issue_write
  - github-search_issues
  - github-projects_write
---

You are an expert issue triage agent. Your goal is to efficiently categorize, prioritize, and route issues to the appropriate teams.

## Classification Rules

### Issue Types
- **bug**: Runtime errors, crashes, incorrect behavior
- **enhancement**: New features or improvements
- **documentation**: Docs updates, typos, missing examples
- **security**: Vulnerabilities, CVEs, security concerns
- **performance**: Slow code, memory leaks, optimization
- **refactor**: Code cleanup, technical debt
- **ci**: CI/CD issues, workflow problems
- **dependencies**: Dependency updates, conflicts

### Priority Levels
- **P0-critical**: System down, data loss, security breach
- **P1-high**: Major functionality broken, security issue
- **P2-medium**: Feature broken, workaround exists
- **P3-low**: Minor bug, cosmetic issue, nice-to-have

### Priority Decision Matrix

┌──────────────────┬──────────┬──────────┬──────────┬──────────┐ │ Type / Impact │ Critical │ High │ Medium │ Low │ ├──────────────────┼──────────┼──────────┼──────────┼──────────┤ │ Security │ P0 │ P0 │ P1 │ P2 │ │ Bug (prod) │ P0 │ P1 │ P2 │ P3 │ │ Bug (dev) │ P1 │ P2 │ P3 │ P3 │ │ Enhancement │ P1 │ P2 │ P3 │ P3 │ │ Documentation │ P2 │ P3 │ P3 │ P3 │ │ Refactor │ P2 │ P3 │ P3 │ P3 │ └──────────────────┴──────────┴──────────┴──────────┴──────────┘

## Triage Steps

1. **Read issue content**: Title, description, labels, comments
2. **Classify**: Determine issue type based on content
3. **Assess impact**: Production vs. dev, users affected
4. **Assign priority**: Use decision matrix
5. **Apply labels**: Type + priority + additional context labels
6. **Detect duplicates**: Search for similar issues
7. **Assign owner**: Route to appropriate team or person
8. **Add to project**: Place in correct project board column

## Special Handling

### Security Issues
- Immediately label as 'security'
- Set priority to P0 or P1
- Assign to security team
- If vulnerability disclosure, create security advisory
- Do NOT comment sensitive details publicly

### Duplicates
- Search for similar issues using keywords
- If found, comment with link and close as duplicate
- Transfer conversation to original issue

### Invalid Issues
- Missing reproduction steps for bugs → Request more info
- Spam or off-topic → Close with explanation
- Question (not issue) → Convert to discussion

## Examples

### Example 1: Security Issue

Issue: "SQL Injection in user login" Classification: security Priority: P0-critical Labels: security, bug, P0-critical Assigned: security-team Action: Create security advisory

### Example 2: Feature Request

Issue: "Add dark mode support" Classification: enhancement Priority: P2-medium Labels: enhancement, ui, P2-medium Assigned: frontend-team Action: Add to backlog project

### Example 3: Bug with Reproduction

Issue: "App crashes when clicking save button" Classification: bug Priority: P1-high (production impact) Labels: bug, P1-high, reproduction-provided Assigned: backend-team Action: Add to current sprint

Intelligent Duplicate Detection

// duplicate-detector.js
class DuplicateDetector {
  constructor(github) {
    this.github = github;
  }
  
  async findDuplicates(issue) {
    // Extract keywords from title and body
    const keywords = this.extractKeywords(issue.title + ' ' + issue.body);
    
    // Search for similar issues
    const searchQuery = `repo:${issue.repository} is:issue ${keywords.join(' OR ')}`;
    
    const { data: searchResults } = await this.github.search.issuesAndPullRequests({
      q: searchQuery,
      per_page: 10,
    });
    
    const candidates = searchResults.items.filter(
      item => item.number !== issue.number
    );
    
    // Calculate similarity scores
    const scored = candidates.map(candidate => ({
      issue: candidate,
      score: this.calculateSimilarity(issue, candidate),
    }));
    
    // Filter by similarity threshold
    const duplicates = scored.filter(s => s.score > 0.7);
    
    return duplicates.sort((a, b) => b.score - a.score);
  }
  
  extractKeywords(text) {
    // Remove common words
    const stopwords = new Set([
      'the', 'a', 'an', 'in', 'on', 'at', 'to', 'for',
      'is', 'was', 'are', 'were', 'be', 'been',
    ]);
    
    // Tokenize and filter
    const words = text.toLowerCase()
      .replace(/[^\w\s]/g, ' ')
      .split(/\s+/)
      .filter(word => word.length > 3 && !stopwords.has(word));
    
    // Get unique words
    return [...new Set(words)];
  }
  
  calculateSimilarity(issue1, issue2) {
    // Title similarity (weighted 60%)
    const titleSim = this.jaccardSimilarity(
      issue1.title.toLowerCase(),
      issue2.title.toLowerCase()
    );
    
    // Body similarity (weighted 40%)
    const bodySim = this.jaccardSimilarity(
      issue1.body.toLowerCase(),
      issue2.body.toLowerCase()
    );
    
    // Label similarity (bonus)
    const labelSim = this.labelSimilarity(
      issue1.labels,
      issue2.labels
    );
    
    return (titleSim * 0.6 + bodySim * 0.4) * (1 + labelSim * 0.1);
  }
  
  jaccardSimilarity(str1, str2) {
    const set1 = new Set(str1.split(/\s+/));
    const set2 = new Set(str2.split(/\s+/));
    
    const intersection = new Set([...set1].filter(x => set2.has(x)));
    const union = new Set([...set1, ...set2]);
    
    return intersection.size / union.size;
  }
  
  labelSimilarity(labels1, labels2) {
    const set1 = new Set(labels1.map(l => l.name));
    const set2 = new Set(labels2.map(l => l.name));
    
    const intersection = new Set([...set1].filter(x => set2.has(x)));
    const union = new Set([...set1, ...set2]);
    
    return union.size > 0 ? intersection.size / union.size : 0;
  }
}

---

👀 Continuous Review Pattern

Automated Code Review on Every PR

# .github/workflows/continuous-review.yml
name: Continuous Code Review
on:
  pull_request:
    types: [opened, synchronize, reopened]

permissions:
  contents: read
  pull-requests: write

jobs:
  code-review:
    runs-on: ubuntu-latest
    steps:
      - name: Harden Runner
        uses: step-security/harden-runner@v2
        with:
          egress-policy: audit
      
      - name: Checkout PR
        uses: actions/checkout@v4
        with:
          ref: ${{ github.event.pull_request.head.sha }}
      
      - name: AI Code Review
        uses: github/copilot-agent@v1
        with:
          agent: code-reviewer
          task: |
            Review pull request #${{ github.event.pull_request.number }}:
            
            1. Code Quality:
               - Check for code smells
               - Identify potential bugs
               - Suggest improvements
            
            2. Security:
               - Look for security vulnerabilities
               - Check for hardcoded secrets
               - Validate input sanitization
            
            3. Performance:
               - Identify inefficient algorithms
               - Check for memory leaks
               - Suggest optimizations
            
            4. Best Practices:
               - Ensure consistent style
               - Check error handling
               - Validate test coverage
            
            5. Documentation:
               - Verify docstrings
               - Check README updates
               - Ensure CHANGELOG entry
            
            Provide specific, actionable feedback with code examples.
      
      - name: Post Review Comments
        uses: github/copilot-agent@v1
        with:
          agent: comment-poster
          task: |
            Post review comments on PR #${{ github.event.pull_request.number }}
            using the feedback from the code review.
            
            Use line-specific comments for code issues.
            Use general comment for overall feedback.

Progressive Review Pattern

// progressive-review.js
class ProgressiveReviewer {
  constructor() {
    this.levels = [
      {
        name: 'quick-scan',
        timeout: 30,
        checks: [
          'syntax-errors',
          'obvious-bugs',
          'security-critical',
        ],
      },
      {
        name: 'standard-review',
        timeout: 120,
        checks: [
          'code-quality',
          'performance',
          'best-practices',
          'security',
        ],
      },
      {
        name: 'deep-analysis',
        timeout: 300,
        checks: [
          'architecture',
          'design-patterns',
          'edge-cases',
          'maintainability',
        ],
      },
    ];
  }
  
  async reviewPR(pr) {
    const findings = [];
    
    for (const level of this.levels) {
      console.log(`Running ${level.name}...`);
      
      try {
        const levelFindings = await this.runLevel(level, pr);
        findings.push(...levelFindings);
        
        // Stop if critical issues found
        if (this.hasCriticalIssues(levelFindings)) {
          console.log('Critical issues found, stopping review');
          break;
        }
      } catch (error) {
        console.error(`Level ${level.name} failed:`, error);
        break;
      }
    }
    
    return this.consolidateFindings(findings);
  }
  
  async runLevel(level, pr) {
    const findings = [];
    
    for (const check of level.checks) {
      const checkFindings = await this.runCheck(check, pr);
      findings.push(...checkFindings);
    }
    
    return findings;
  }
  
  async runCheck(check, pr) {
    // Implement specific check logic
    switch (check) {
      case 'syntax-errors':
        return this.checkSyntax(pr);
      case 'security-critical':
        return this.checkSecurity(pr);
      case 'code-quality':
        return this.checkQuality(pr);
      default:
        return [];
    }
  }
  
  hasCriticalIssues(findings) {
    return findings.some(f => f.severity === 'critical');
  }
  
  consolidateFindings(findings) {
    // Remove duplicates, prioritize by severity
    const seen = new Set();
    const unique = [];
    
    const sorted = findings.sort((a, b) => {
      const severityOrder = { critical: 0, high: 1, medium: 2, low: 3 };
      return severityOrder[a.severity] - severityOrder[b.severity];
    });
    
    for (const finding of sorted) {
      const key = `${finding.file}:${finding.line}:${finding.message}`;
      if (!seen.has(key)) {
        seen.add(key);
        unique.push(finding);
      }
    }
    
    return unique;
  }
}

---

🔧 Continuous Maintenance Pattern

Automated Dependency Updates

# .github/workflows/continuous-maintenance.yml
name: Continuous Maintenance
on:
  schedule:
    - cron: '0 2 * * 1'  # Every Monday at 2 AM
  workflow_dispatch:

permissions:
  contents: write
  pull-requests: write

jobs:
  update-dependencies:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout
        uses: actions/checkout@v4
      
      - name: Check for Dependency Updates
        id: updates
        run: |
          npm outdated --json > outdated.json || true
          
          # Filter to security updates and minor/patch versions
          jq '[.[] | select(.wanted != .current)]' outdated.json > updates.json
      
      - name: AI-Powered Update Decision
        uses: github/copilot-agent@v1
        with:
          agent: maintenance-agent
          task: |
            Analyze dependency updates in updates.json:
            
            For each update:
            1. Read CHANGELOG to understand changes
            2. Assess breaking change risk
            3. Check for known issues
            4. Prioritize security updates
            
            Create a plan:
            - Group compatible updates
            - Separate breaking changes
            - Schedule rollout (immediate vs. gradual)
            
            Generate PRs for approved updates.
      
      - name: Create Update PRs
        run: |
          # Agent creates PRs for approved updates
          copilot-cli execute --agent maintenance-agent \
            --task "Create PRs for approved dependency updates"

Automated Code Refactoring

# .github/workflows/refactor-bot.yml
name: Refactor Bot
on:
  schedule:
    - cron: '0 3 * * 0'  # Every Sunday at 3 AM

jobs:
  identify-refactoring-opportunities:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout
        uses: actions/checkout@v4
      
      - name: Run Static Analysis
        run: |
          # Find code smells
          npx jscpd src/ --format json > duplication.json
          
          # Find complex functions
          npx complexity-report src/ --format json > complexity.json
          
          # Find test coverage gaps
          npm test -- --coverage --json > coverage.json
      
      - name: AI Refactoring Analysis
        uses: github/copilot-agent@v1
        with:
          agent: refactor-agent
          task: |
            Analyze codebase for refactoring opportunities:
            
            1. Code Duplication (duplication.json):
               - Find duplicated code blocks
               - Suggest extraction to functions/modules
            
            2. Complexity (complexity.json):
               - Identify complex functions (CC > 10)
               - Suggest simplification strategies
            
            3. Test Coverage (coverage.json):
               - Find uncovered code
               - Suggest test cases
            
            4. Architectural Issues:
               - God classes (> 500 lines)
               - Long methods (> 50 lines)
               - Deep nesting (> 4 levels)
            
            Create issues for top 5 refactoring opportunities.
            For simple refactorings, create automated PRs.

---

📊 Continuous Monitoring Pattern

System Health Monitoring

# .github/workflows/continuous-monitoring.yml
name: Continuous Monitoring
on:
  schedule:
    - cron: '*/15 * * * *'  # Every 15 minutes

permissions:
  issues: write

jobs:
  health-check:
    runs-on: ubuntu-latest
    steps:
      - name: Check Production Health
        id: health
        run: |
          # Health check endpoints
          curl -f https://api.example.com/health > health.json
          
          # Performance metrics
          curl -f https://api.example.com/metrics > metrics.json
          
          # Error rates
          curl -f https://api.example.com/errors > errors.json
      
      - name: AI Anomaly Detection
        uses: github/copilot-agent@v1
        with:
          agent: monitoring-agent
          task: |
            Analyze system health data:
            
            1. Health Status (health.json):
               - Check all services are UP
               - Verify response times < 200ms
               - Check resource usage < 80%
            
            2. Performance Metrics (metrics.json):
               - Compare to historical baseline
               - Detect anomalies (> 2 std dev)
               - Identify trends
            
            3. Error Rates (errors.json):
               - Check error rate < 1%
               - Identify error spike patterns
               - Correlate with recent deployments
            
            If issues detected:
            - Create incident issue
            - Notify on-call engineer
            - Suggest remediation actions
            
            If trends detected:
            - Create warning issue
            - Provide trend analysis
            - Recommend preventive actions

Anomaly Detection Algorithm

// anomaly-detector.js
class AnomalyDetector {
  constructor(historicalData) {
    this.historicalData = historicalData;
    this.baseline = this.calculateBaseline();
  }
  
  calculateBaseline() {
    const values = this.historicalData.map(d => d.value);
    
    return {
      mean: this.mean(values),
      stdDev: this.standardDeviation(values),
      median: this.median(values),
      p95: this.percentile(values, 95),
      p99: this.percentile(values, 99),
    };
  }
  
  detectAnomalies(currentData) {
    const anomalies = [];
    
    for (const point of currentData) {
      const zScore = (point.value - this.baseline.mean) / this.baseline.stdDev;
      
      if (Math.abs(zScore) > 3) {
        // More than 3 standard deviations
        anomalies.push({
          metric: point.metric,
          value: point.value,
          baseline: this.baseline.mean,
          zScore,
          severity: Math.abs(zScore) > 5 ? 'critical' : 'high',
          timestamp: point.timestamp,
        });
      } else if (Math.abs(zScore) > 2) {
        // More than 2 standard deviations
        anomalies.push({
          metric: point.metric,
          value: point.value,
          baseline: this.baseline.mean,
          zScore,
          severity: 'medium',
          timestamp: point.timestamp,
        });
      }
    }
    
    return anomalies;
  }
  
  detectTrends(timeSeriesData, windowSize = 24) {
    const trends = [];
    
    for (let i = windowSize; i < timeSeriesData.length; i++) {
      const window = timeSeriesData.slice(i - windowSize, i);
      const values = window.map(d => d.value);
      
      // Linear regression
      const { slope, r2 } = this.linearRegression(values);
      
      // Significant trend if r² > 0.7 and |slope| > threshold
      if (r2 > 0.7 && Math.abs(slope) > 0.1) {
        trends.push({
          metric: timeSeriesData[i].metric,
          direction: slope > 0 ? 'increasing' : 'decreasing',
          slope,
          r2,
          strength: r2,
          timestamp: timeSeriesData[i].timestamp,
        });
      }
    }
    
    return trends;
  }
  
  mean(values) {
    return values.reduce((a, b) => a + b, 0) / values.length;
  }
  
  standardDeviation(values) {
    const avg = this.mean(values);
    const squareDiffs = values.map(value => Math.pow(value - avg, 2));
    return Math.sqrt(this.mean(squareDiffs));
  }
  
  median(values) {
    const sorted = [...values].sort((a, b) => a - b);
    const mid = Math.floor(sorted.length / 2);
    return sorted.length % 2 === 0
      ? (sorted[mid - 1] + sorted[mid]) / 2
      : sorted[mid];
  }
  
  percentile(values, p) {
    const sorted = [...values].sort((a, b) => a - b);
    const index = (p / 100) * (sorted.length - 1);
    const lower = Math.floor(index);
    const upper = Math.ceil(index);
    const weight = index % 1;
    
    return sorted[lower] * (1 - weight) + sorted[upper] * weight;
  }
  
  linearRegression(values) {
    const n = values.length;
    const x = Array.from({ length: n }, (_, i) => i);
    const y = values;
    
    const sumX = x.reduce((a, b) => a + b, 0);
    const sumY = y.reduce((a, b) => a + b, 0);
    const sumXY = x.reduce((sum, xi, i) => sum + xi * y[i], 0);
    const sumX2 = x.reduce((sum, xi) => sum + xi * xi, 0);
    const sumY2 = y.reduce((sum, yi) => sum + yi * yi, 0);
    
    const slope = (n * sumXY - sumX * sumY) / (n * sumX2 - sumX * sumX);
    const intercept = (sumY - slope * sumX) / n;
    
    // Calculate R²
    const yMean = sumY / n;
    const ssRes = y.reduce((sum, yi, i) => {
      const predicted = slope * x[i] + intercept;
      return sum + Math.pow(yi - predicted, 2);
    }, 0);
    const ssTot = y.reduce((sum, yi) => sum + Math.pow(yi - yMean, 2), 0);
    const r2 = 1 - ssRes / ssTot;
    
    return { slope, intercept, r2 };
  }
}

---

⏰ Scheduling Strategies

Cron-Based Scheduling

on:
  schedule:
    # Daily at 2 AM UTC
    - cron: '0 2 * * *'
    
    # Every hour
    - cron: '0 * * * *'
    
    # Every 15 minutes
    - cron: '*/15 * * * *'
    
    # Monday-Friday at 9 AM
    - cron: '0 9 * * 1-5'
    
    # First day of month
    - cron: '0 0 1 * *'

Adaptive Scheduling

// adaptive-scheduler.js
class AdaptiveScheduler {
  constructor() {
    this.metrics = {
      issueRate: 0,
      prRate: 0,
      errorRate: 0,
    };
  }
  
  calculateOptimalInterval() {
    // Base intervals (in minutes)
    const baseIntervals = {
      triage: 360,      // 6 hours
      review: 60,       // 1 hour
      monitoring: 15,   // 15 minutes
    };
    
    // Adjust based on activity
    const activityMultiplier = this.calculateActivityMultiplier();
    
    return {
      triage: Math.max(30, baseIntervals.triage / activityMultiplier),
      review: Math.max(15, baseIntervals.review / activityMultiplier),
      monitoring: Math.max(5, baseIntervals.monitoring / activityMultiplier),
    };
  }
  
  calculateActivityMultiplier() {
    // Higher activity → shorter intervals
    const issueScore = Math.log(this.metrics.issueRate + 1);
    const prScore = Math.log(this.metrics.prRate + 1);
    const errorScore = Math.log(this.metrics.errorRate + 1) * 2;
    
    return 1 + (issueScore + prScore + errorScore) / 10;
  }
  
  updateMetrics(newMetrics) {
    // Exponential moving average
    const alpha = 0.3;
    
    this.metrics.issueRate =
      alpha * newMetrics.issueRate + (1 - alpha) * this.metrics.issueRate;
    this.metrics.prRate =
      alpha * newMetrics.prRate + (1 - alpha) * this.metrics.prRate;
    this.metrics.errorRate =
      alpha * newMetrics.errorRate + (1 - alpha) * this.metrics.errorRate;
  }
}

---

🎛️ Event-Driven Automation

GitHub Events

on:
  # Issue events
  issues:
    types:
      - opened
      - edited
      - labeled
      - assigned
  
  # PR events
  pull_request:
    types:
      - opened
      - synchronize
      - reopened
      - ready_for_review
  
  # PR review events
  pull_request_review:
    types:
      - submitted
  
  # Comment events
  issue_comment:
    types:
      - created
  
  # Push events
  push:
    branches:
      - main
      - 'release/**'
  
  # Release events
  release:
    types:
      - published
  
  # Workflow run events
  workflow_run:
    workflows:
      - CI
    types:
      - completed

Event-Driven Agent Dispatch

// event-dispatcher.js
class EventDispatcher {
  constructor(agents) {
    this.agents = agents;
    this.eventHandlers = new Map();
    
    this.registerHandlers();
  }
  
  registerHandlers() {
    // Issue events
    this.on('issues.opened', async (event) => {
      await this.agents.triage.handleNewIssue(event.issue);
      await this.agents.duplicate.checkDuplicate(event.issue);
    });
    
    // PR events
    this.on('pull_request.opened', async (event) => {
      await this.agents.review.reviewPR(event.pull_request);
      await this.agents.test.triggerTests(event.pull_request);
    });
    
    this.on('pull_request.synchronize', async (event) => {
      await this.agents.review.reviewChanges(event.pull_request);
    });
    
    // Security events
    this.on('security_advisory.published', async (event) => {
      await this.agents.security.handleAdvisory(event.advisory);
      await this.agents.maintenance.checkDependencies(event.advisory);
    });
    
    // Workflow events
    this.on('workflow_run.completed', async (event) => {
      if (event.workflow_run.conclusion === 'failure') {
        await this.agents.incident.handleFailure(event.workflow_run);
      }
    });
  }
  
  on(eventType, handler) {
    if (!this.eventHandlers.has(eventType)) {
      this.eventHandlers.set(eventType, []);
    }
    this.eventHandlers.get(eventType).push(handler);
  }
  
  async dispatch(event) {
    const eventType = `${event.type}.${event.action}`;
    const handlers = this.eventHandlers.get(eventType) || [];
    
    console.log(`Dispatching event: ${eventType}`);
    
    for (const handler of handlers) {
      try {
        await handler(event);
      } catch (error) {
        console.error(`Handler failed for ${eventType}:`, error);
      }
    }
  }
}

---

👤 Human-in-the-Loop Patterns

Approval Gates

# .github/workflows/critical-change.yml
name: Critical Change with Approval
on:
  workflow_dispatch:
    inputs:
      change_description:
        description: 'What change to make'
        required: true

jobs:
  analyze-change:
    runs-on: ubuntu-latest
    steps:
      - name: AI Analysis
        id: analysis
        uses: github/copilot-agent@v1
        with:
          agent: change-analyzer
          task: |
            Analyze the proposed change: "${{ inputs.change_description }}"
            
            Provide:
            1. Impact assessment
            2. Risk level (low/medium/high/critical)
            3. Affected systems
            4. Rollback plan
            5. Recommendation (approve/reject/modify)
      
      - name: Upload Analysis
        uses: actions/upload-artifact@v4
        with:
          name: change-analysis
          path: analysis.json
  
  human-approval:
    needs: analyze-change
    runs-on: ubuntu-latest
    environment:
      name: production-approval
      required-reviewers: 2
    steps:
      - name: Download Analysis
        uses: actions/download-artifact@v4
        with:
          name: change-analysis
      
      - name: Display Analysis
        run: cat analysis.json
      
      - name: Wait for Approval
        run: echo "Waiting for human approval..."
  
  execute-change:
    needs: human-approval
    runs-on: ubuntu-latest
    steps:
      - name: Execute Change
        uses: github/copilot-agent@v1
        with:
          agent: change-executor
          task: |
            Execute the approved change: "${{ inputs.change_description }}"
            Follow the plan from the analysis.

Feedback Collection

# .github/workflows/collect-feedback.yml
name: Collect Agent Feedback
on:
  issue_comment:
    types: [created]

jobs:
  check-feedback:
    if: contains(github.event.comment.body, '@agent-feedback')
    runs-on: ubuntu-latest
    steps:
      - name: Parse Feedback
        id: feedback
        run: |
          # Extract feedback sentiment
          if [[ "${{ github.event.comment.body }}" =~ "👍" ]]; then
            echo "sentiment=positive" >> $GITHUB_OUTPUT
          elif [[ "${{ github.event.comment.body }}" =~ "👎" ]]; then
            echo "sentiment=negative" >> $GITHUB_OUTPUT
          else
            echo "sentiment=neutral" >> $GITHUB_OUTPUT
          fi
      
      - name: Store Feedback
        run: |
          # Store feedback for model training
          cat << EOF > feedback.json
          {
            "issue": ${{ github.event.issue.number }},
            "comment": ${{ github.event.comment.id }},
            "sentiment": "${{ steps.feedback.outputs.sentiment }}",
            "text": "${{ github.event.comment.body }}",
            "timestamp": "$(date -u +%Y-%m-%dT%H:%M:%SZ)"
          }
          EOF
          
          # Upload to feedback dataset
          curl -X POST https://feedback-api.example.com/collect \
            -H "Content-Type: application/json" \
            -d @feedback.json

---

🔁 Feedback Loops

Performance Feedback

// feedback-collector.js
class FeedbackCollector {
  constructor() {
    this.metrics = {
      accuracy: [],
      latency: [],
      satisfaction: [],
    };
  }
  
  async collectFeedback(agentAction) {
    const feedback = {
      actionId: agentAction.id,
      timestamp: new Date().toISOString(),
      agent: agentAction.agent,
      action: agentAction.action,
      outcome: await this.measureOutcome(agentAction),
    };
    
    // Store feedback
    await this.storeFeedback(feedback);
    
    // Update metrics
    this.updateMetrics(feedback);
    
    // Trigger retraining if needed
    if (this.shouldRetrain()) {
      await this.triggerRetraining();
    }
    
    return feedback;
  }
  
  async measureOutcome(agentAction) {
    switch (agentAction.action) {
      case 'triage':
        return this.measureTriageOutcome(agentAction);
      case 'review':
        return this.measureReviewOutcome(agentAction);
      default:
        return null;
    }
  }
  
  async measureTriageOutcome(action) {
    // Check if labels were manually changed
    const issue = await this.getIssue(action.issueNumber);
    const initialLabels = new Set(action.appliedLabels);
    const currentLabels = new Set(issue.labels.map(l => l.name));
    
    // Calculate accuracy
    const correctLabels = [...currentLabels].filter(l => initialLabels.has(l));
    const accuracy = correctLabels.length / initialLabels.size;
    
    return {
      accuracy,
      manualChanges: initialLabels.size - correctLabels.length,
    };
  }
  
  async measureReviewOutcome(action) {
    // Check if review comments were helpful
    const pr = await this.getPR(action.prNumber);
    const reviewComments = action.comments;
    
    let helpful = 0;
    let unhelpful = 0;
    
    for (const comment of reviewComments) {
      const reactions = await this.getReactions(comment.id);
      helpful += reactions['+1'] || 0;
      unhelpful += reactions['-1'] || 0;
    }
    
    const satisfaction = helpful / (helpful + unhelpful + 1);
    
    return {
      satisfaction,
      helpful,
      unhelpful,
    };
  }
  
  updateMetrics(feedback) {
    if (feedback.outcome?.accuracy !== undefined) {
      this.metrics.accuracy.push(feedback.outcome.accuracy);
    }
    
    if (feedback.outcome?.satisfaction !== undefined) {
      this.metrics.satisfaction.push(feedback.outcome.satisfaction);
    }
    
    // Keep only last 1000 data points
    for (const key in this.metrics) {
      if (this.metrics[key].length > 1000) {
        this.metrics[key] = this.metrics[key].slice(-1000);
      }
    }
  }
  
  shouldRetrain() {
    // Retrain if accuracy drops below threshold
    const recentAccuracy = this.metrics.accuracy.slice(-100);
    const avgAccuracy =
      recentAccuracy.reduce((a, b) => a + b, 0) / recentAccuracy.length;
    
    return avgAccuracy < 0.7;
  }
  
  async triggerRetraining() {
    console.log('🔄 Triggering model retraining...');
    
    // Trigger retraining workflow
    await this.github.actions.createWorkflowDispatch({
      owner: 'org',
      repo: 'repo',
      workflow_id: 'retrain-model.yml',
      ref: 'main',
      inputs: {
        reason: 'accuracy_drop',
        metrics: JSON.stringify(this.getMetricsSummary()),
      },
    });
  }
  
  getMetricsSummary() {
    return {
      accuracy: {
        mean: this.mean(this.metrics.accuracy),
        stdDev: this.stdDev(this.metrics.accuracy),
      },
      satisfaction: {
        mean: this.mean(this.metrics.satisfaction),
        stdDev: this.stdDev(this.metrics.satisfaction),
      },
    };
  }
  
  mean(values) {
    return values.reduce((a, b) => a + b, 0) / values.length;
  }
  
  stdDev(values) {
    const avg = this.mean(values);
    const squareDiffs = values.map(v => Math.pow(v - avg, 2));
    return Math.sqrt(this.mean(squareDiffs));
  }
}

---

🎓 Related Skills

• gh-aw-security-architecture: Security for continuous AI
• gh-aw-mcp-configuration: MCP server configuration
• gh-aw-tools-ecosystem: Available tools for agents
• github-actions-workflows: CI/CD workflows

---

📚 References

• GitHub Actions Documentation
• Agent Factory Blog Series
• Continuous AI (GitHub Next)

🆕 Agent Factory Patterns (Lessons from github/gh-aw)

Proven Workflow Categories

The GitHub Next team operates 100+ agentic workflows. Key categories:

Category	Examples	Impact
Issue Triage	Auto-label, auto-assign, duplicate detection	Instant response to new issues
Code Quality	Code Simplifier, Dead Code Remover, Typist	Continuous incremental improvement
Documentation	Doc Healer, Doc Updater, Glossary Maintainer	Always-current docs
Security	Red Team Agent, Secrets Analysis, Malicious Code Scan	Daily security posture
Metrics	Code Metrics, Token Consumption, Performance Summary	Data-driven decisions
Analytics	Session Insights, PR NLP Analysis, Prompt Clustering	Meta-analysis of AI behavior
Project Coordination	Plan Command, Discussion Task Miner	67% PR merge rate

Key Insights

1. Specialized agents > generic agents — Customize for your repo context
2. Incremental > heroic — Small daily improvements compound over time
3. Observability is essential — Meta-analyze agent behavior patterns
4. Schedule staggering — Avoid resource contention with varied cron times
5. Merge rate matters — Track accepted vs. rejected agent PRs

Multi-Agent Coordination

# Pattern: Sequential task chaining
# Step 1: Task Miner discovers work from discussions
# Step 2: Plan Command decomposes into sub-issues
# Step 3: Copilot Coding Agent implements each sub-issue
# Step 4: Code review and merge

# Verified causal chain example:
# Discussion #7631 → Issue #8058 → PR #8110 (merged)

---

✅ Remember

• ✅ Design agents for continuous 24/7 operation
• ✅ Use adaptive scheduling based on repository activity
• ✅ Implement event-driven dispatch (issues, PRs, comments)
• ✅ Add human approval gates for critical operations
• ✅ Monitor agent merge rates as quality signal
• ✅ Specialize agents — generic agents underperform
• ✅ Incremental improvements compound over time
• ✅ Meta-analyze agent behavior (NLP, clustering, session insights)
• ✅ Stagger schedules to avoid contention
• ✅ Log all actions for audit trail

---

Last Updated: 2026-04-02
Version: 2.0.0
License: Apache-2.0

---

🔗 Integration with Riksdagsmonitor agentic workflows

This gh-aw skill is applied by the 11 agentic news workflows in .github/workflows/news-*.md. Their domain contract (analysis-artifact product, gate, article contract) lives in:

• .github/prompts/README.md — module catalogue, import rules, AI-FIRST 2-pass rule.
• analysis/methodologies/ai-driven-analysis-guide.md + analysis/templates/ — 9 core / 14 Tier-C artifacts.
• 05-analysis-gate.md — the single blocking gate before any article content is written.

Upstream gh-aw docs (v0.69.3): abridged · complete · agentic-workflows blog series · source repo · GitHub CLI manual.