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AI Agent Workflows Example

This example demonstrates how to set up sophisticated AI agent workflows using TaskDriver for complex, multi-step analysis and automation tasks.

Scenario

You're building an AI-powered system where multiple specialized agents work together to:

  1. Analyze codebases for various aspects
  2. Generate reports and recommendations
  3. Create documentation
  4. Perform automated testing
  5. Coordinate complex multi-step workflows

Project Setup

Create a project with comprehensive instructions for AI agents:

# Create project with instructions from file
cat > ai-agent-instructions.md << 'EOF'
# AI Agent Instructions for Code Analysis Project

## Core Principles
- Always provide detailed, actionable analysis
- Use structured output formats for consistency
- Include confidence scores for recommendations
- Cite specific code locations (file:line) in findings
- Follow security-first approach in all analysis

## Before Starting Any Task
1. Read the complete task instructions and variable context
2. Understand the scope and expected deliverables
3. Check for any project-specific requirements
4. Validate access to required repositories and resources

## Output Standards
- Use markdown formatting for reports
- Include executive summaries for complex analysis
- Provide code examples for recommendations
- Rate findings by severity: Critical, High, Medium, Low
- Include estimated effort for implementing recommendations

## Quality Assurance
- Double-check all code references and line numbers
- Validate all URLs and links in reports
- Ensure recommendations are technically feasible
- Test any code suggestions before including them
EOF

taskdriver create-project "ai-code-analysis" "AI-powered comprehensive code analysis" \
  --instructions "@ai-agent-instructions.md" \
  --max-retries 2 \
  --lease-duration 45

Define Specialized Task Types

Create task types for different AI agent specializations:

Security Analysis with AI

cat > security-analysis-template.md << 'EOF'
# Security Analysis Task

## Objective
Perform comprehensive security analysis of {{repository_url}} focusing on {{security_scope}}.

## Analysis Requirements
- Scan for OWASP Top 10 vulnerabilities
- Check for common security anti-patterns
- Analyze authentication and authorization mechanisms
- Review data handling and privacy compliance
- Assess API security measures

## Deliverables
1. Executive summary with risk assessment
2. Detailed findings with CVSS scores
3. Remediation recommendations with priority levels
4. Code examples for secure implementations
5. Compliance checklist ({{compliance_standards}})

## Context
- Target Environment: {{environment}}
- Technology Stack: {{tech_stack}}
- Security Standards: {{compliance_standards}}
- Previous Issues: {{known_issues}}

## Output Format
Provide structured JSON output with detailed markdown report.
EOF

taskdriver create-task-type "ai-code-analysis" "ai-security-analysis" \
  --template "@security-analysis-template.md" \
  --variables "repository_url" "security_scope" "environment" "tech_stack" "compliance_standards" "known_issues" \
  --max-retries 2 \
  --lease-duration 60

AI-Powered Architecture Review

cat > architecture-review-template.md << 'EOF'
# Architecture Review Task

## Objective
Conduct comprehensive architecture analysis of {{repository_url}} for {{architecture_focus}}.

## Analysis Areas
- System design patterns and adherence
- Scalability and performance characteristics
- Technology stack appropriateness
- Integration patterns and API design
- Data flow and storage architecture
- Deployment and infrastructure considerations

## Deliverables
1. Architecture diagram (automated generation)
2. Pattern analysis with recommendations
3. Scalability assessment and bottleneck identification
4. Technology stack evaluation
5. Migration recommendations (if applicable)

## Context
- Current Scale: {{current_scale}}
- Growth Projections: {{growth_expectations}}
- Technical Constraints: {{constraints}}
- Business Requirements: {{business_requirements}}

## Analysis Depth
Focus on {{architecture_focus}} with emphasis on {{priority_areas}}.
EOF

taskdriver create-task-type "ai-code-analysis" "ai-architecture-review" \
  --template "@architecture-review-template.md" \
  --variables "repository_url" "architecture_focus" "current_scale" "growth_expectations" "constraints" "business_requirements" "priority_areas" \
  --max-retries 1 \
  --lease-duration 90

AI Code Quality & Best Practices

cat > code-quality-template.md << 'EOF'
# Code Quality Analysis Task

## Objective
Analyze code quality in {{repository_url}} with focus on {{quality_aspects}}.

## Analysis Scope
- Code style and formatting consistency
- Best practices adherence for {{language}}
- Code complexity and maintainability metrics
- Documentation quality and coverage
- Test coverage and quality
- Performance implications of code patterns

## AI-Specific Requirements
- Use static analysis tools and pattern recognition
- Generate automated refactoring suggestions
- Identify code smells and anti-patterns
- Suggest modern language features and patterns
- Evaluate technical debt and maintenance burden

## Deliverables
1. Quality metrics dashboard
2. Automated refactoring suggestions
3. Best practices compliance report
4. Documentation improvement recommendations
5. Technical debt assessment

## Context
- Language: {{language}}
- Framework: {{framework}}
- Team Experience: {{team_experience}}
- Quality Standards: {{quality_standards}}
EOF

taskdriver create-task-type "ai-code-analysis" "ai-code-quality" \
  --template "@code-quality-template.md" \
  --variables "repository_url" "quality_aspects" "language" "framework" "team_experience" "quality_standards" \
  --duplicate-handling "ignore" \
  --max-retries 3 \
  --lease-duration 40

AI Documentation Generation

cat > documentation-template.md << 'EOF'
# Documentation Generation Task

## Objective
Generate comprehensive documentation for {{repository_url}} covering {{documentation_type}}.

## Documentation Requirements
- API documentation with examples
- Code architecture documentation
- Setup and deployment guides
- Usage examples and tutorials
- Troubleshooting guides

## AI Capabilities to Utilize
- Automated API documentation from code
- Code flow diagram generation
- Example code generation
- Natural language explanation of complex algorithms
- Interactive documentation features

## Deliverables
1. Complete API documentation
2. Developer onboarding guide
3. Architecture documentation with diagrams
4. Usage examples and tutorials
5. Troubleshooting and FAQ sections

## Context
- Documentation Type: {{documentation_type}}
- Target Audience: {{target_audience}}
- Existing Documentation: {{existing_docs}}
- Documentation Standards: {{doc_standards}}
EOF

taskdriver create-task-type "ai-code-analysis" "ai-documentation" \
  --template "@documentation-template.md" \
  --variables "repository_url" "documentation_type" "target_audience" "existing_docs" "doc_standards" \
  --max-retries 2 \
  --lease-duration 75

Register Specialized AI Agents

# Security specialist AI agent
taskdriver register-agent "ai-code-analysis" "ai-security-specialist"

# Architecture specialist AI agent
taskdriver register-agent "ai-code-analysis" "ai-architect"

# Code quality specialist AI agent
taskdriver register-agent "ai-code-analysis" "ai-quality-expert"

# Documentation specialist AI agent
taskdriver register-agent "ai-code-analysis" "ai-documentation-expert"

# Multi-purpose AI agent
taskdriver register-agent "ai-code-analysis" "ai-generalist"

Complex Multi-Step Workflow Example

Let's create a comprehensive analysis workflow for a new codebase:

Phase 1: Initial Analysis Tasks

# Security analysis
taskdriver create-task "ai-code-analysis" "ai-security-analysis-task-type-id" \
  "Comprehensive security analysis for new e-commerce platform" \
  --variables '{
    "repository_url": "https://github.com/company/ecommerce-platform",
    "security_scope": "authentication,payment-processing,data-protection,api-security",
    "environment": "production",
    "tech_stack": "Node.js,React,MongoDB,Redis",
    "compliance_standards": "PCI-DSS,GDPR,SOC2",
    "known_issues": "Previous audit found session management issues"
  }' \
  --batch-id "ecommerce-analysis-phase1"

# Architecture review
taskdriver create-task "ai-code-analysis" "ai-architecture-review-task-type-id" \
  "Architecture assessment for e-commerce platform scalability" \
  --variables '{
    "repository_url": "https://github.com/company/ecommerce-platform",
    "architecture_focus": "scalability,performance,maintainability",
    "current_scale": "10000-users-daily",
    "growth_expectations": "500000-users-daily-in-12-months",
    "constraints": "budget-conscious,existing-team-skills",
    "business_requirements": "high-availability,fast-checkout,mobile-first",
    "priority_areas": "payment-processing,user-authentication,product-catalog"
  }' \
  --batch-id "ecommerce-analysis-phase1"

# Code quality analysis
taskdriver create-task "ai-code-analysis" "ai-code-quality-task-type-id" \
  "Code quality assessment for e-commerce platform" \
  --variables '{
    "repository_url": "https://github.com/company/ecommerce-platform",
    "quality_aspects": "maintainability,testing,performance,documentation",
    "language": "javascript,typescript",
    "framework": "react,express,mongoose",
    "team_experience": "intermediate",
    "quality_standards": "eslint-recommended,jest-testing,jsdoc-documentation"
  }' \
  --batch-id "ecommerce-analysis-phase1"

Phase 2: Specialized Deep-Dive Tasks

# Payment system security deep dive
taskdriver create-task "ai-code-analysis" "ai-security-analysis-task-type-id" \
  "Deep security analysis of payment processing system" \
  --variables '{
    "repository_url": "https://github.com/company/ecommerce-platform",
    "security_scope": "payment-processing,pci-compliance,fraud-detection",
    "environment": "production",
    "tech_stack": "Node.js,Stripe-API,MongoDB",
    "compliance_standards": "PCI-DSS-Level1",
    "known_issues": "Need to validate PCI compliance before launch"
  }' \
  --batch-id "ecommerce-analysis-phase2"

# Performance optimization architecture
taskdriver create-task "ai-code-analysis" "ai-architecture-review-task-type-id" \
  "Performance optimization architecture analysis" \
  --variables '{
    "repository_url": "https://github.com/company/ecommerce-platform",
    "architecture_focus": "performance,caching,database-optimization",
    "current_scale": "peak-5000-concurrent-users",
    "growth_expectations": "peak-50000-concurrent-users",
    "constraints": "current-infrastructure,mongodb-primary",
    "business_requirements": "sub-200ms-response-time,99.9-uptime",
    "priority_areas": "product-search,shopping-cart,checkout-flow"
  }' \
  --batch-id "ecommerce-analysis-phase2"

# API documentation generation
taskdriver create-task "ai-code-analysis" "ai-documentation-task-type-id" \
  "Generate comprehensive API documentation" \
  --variables '{
    "repository_url": "https://github.com/company/ecommerce-platform",
    "documentation_type": "api-documentation,integration-guide",
    "target_audience": "external-developers,internal-team,partners",
    "existing_docs": "basic-readme,postman-collection",
    "doc_standards": "openapi-3.0,interactive-examples,sdk-generation"
  }' \
  --batch-id "ecommerce-analysis-phase2"

Phase 3: Integration and Final Reports

# Integration testing recommendations
taskdriver create-task "ai-code-analysis" "ai-code-quality-task-type-id" \
  "Integration testing strategy and implementation" \
  --variables '{
    "repository_url": "https://github.com/company/ecommerce-platform",
    "quality_aspects": "integration-testing,e2e-testing,performance-testing",
    "language": "javascript,typescript",
    "framework": "jest,cypress,k6",
    "team_experience": "intermediate",
    "quality_standards": "90-percent-coverage,automated-ci-cd"
  }' \
  --batch-id "ecommerce-analysis-phase3"

# Final security compliance report
taskdriver create-task "ai-code-analysis" "ai-security-analysis-task-type-id" \
  "Final security compliance and readiness assessment" \
  --variables '{
    "repository_url": "https://github.com/company/ecommerce-platform",
    "security_scope": "pre-launch-assessment,compliance-verification,security-checklist",
    "environment": "production",
    "tech_stack": "complete-stack-assessment",
    "compliance_standards": "PCI-DSS,GDPR,SOC2,production-readiness",
    "known_issues": "consolidate-all-previous-findings"
  }' \
  --batch-id "ecommerce-analysis-phase3"

Agent Execution Examples

AI Security Specialist Response

# Security agent picks up task
taskdriver get-next-task "ai-security-specialist" "ai-code-analysis"

# Security agent completes comprehensive analysis
taskdriver complete-task "ai-security-specialist" "ai-code-analysis" "task-id" \
  --result '{
    "status": "completed",
    "executive_summary": "Security analysis identified 3 critical, 7 high, and 12 medium priority vulnerabilities",
    "confidence_score": 0.95,
    "analysis_results": {
      "vulnerabilities": {
        "critical": [
          {
            "id": "CRIT-001",
            "title": "SQL Injection in User Authentication",
            "location": "src/auth/login.js:45-52",
            "cvss_score": 9.8,
            "description": "Direct SQL query construction allows injection attacks",
            "remediation": "Use parameterized queries or ORM methods",
            "effort_estimate": "4 hours"
          }
        ],
        "high": [
          {
            "id": "HIGH-001",
            "title": "Inadequate Session Management",
            "location": "src/middleware/session.js:23-35",
            "cvss_score": 7.5,
            "description": "Session tokens not properly invalidated on logout",
            "remediation": "Implement proper session lifecycle management",
            "effort_estimate": "8 hours"
          }
        ]
      },
      "compliance_status": {
        "PCI-DSS": "non-compliant",
        "GDPR": "partially-compliant",
        "SOC2": "requires-assessment"
      },
      "security_metrics": {
        "total_endpoints_scanned": 47,
        "authenticated_endpoints": 32,
        "encryption_coverage": 0.78,
        "input_validation_coverage": 0.65
      }
    },
    "recommendations": [
      "Implement comprehensive input validation framework",
      "Add automated security testing to CI/CD pipeline",
      "Conduct penetration testing before production deployment"
    ],
    "next_steps": [
      "Prioritize critical vulnerabilities for immediate fix",
      "Schedule security code review training for team",
      "Implement security monitoring and alerting"
    ]
  }'

AI Architecture Specialist Response

# Architecture agent completes analysis
taskdriver complete-task "ai-architect" "ai-code-analysis" "task-id" \
  --result '{
    "status": "completed",
    "executive_summary": "Architecture shows good foundational patterns but requires optimization for target scale",
    "confidence_score": 0.92,
    "analysis_results": {
      "architecture_assessment": {
        "current_patterns": ["MVC", "REST-API", "microservices-partial"],
        "scalability_score": 6.5,
        "maintainability_score": 7.2,
        "performance_score": 5.8
      },
      "bottlenecks_identified": [
        {
          "area": "Database Layer",
          "issue": "N+1 query patterns in product catalog",
          "impact": "high",
          "solution": "Implement query optimization and caching layer"
        },
        {
          "area": "Authentication",
          "issue": "Session storage in application memory",
          "impact": "medium",
          "solution": "Move to Redis-based session storage"
        }
      ],
      "scalability_recommendations": [
        {
          "priority": "high",
          "recommendation": "Implement horizontal scaling for API layer",
          "estimated_effort": "3 weeks",
          "expected_improvement": "5x throughput increase"
        },
        {
          "priority": "medium",
          "recommendation": "Add caching layer for product catalog",
          "estimated_effort": "1 week",
          "expected_improvement": "40% response time reduction"
        }
      ]
    },
    "architecture_diagrams": {
      "current_architecture": "https://diagrams.example.com/current-arch.svg",
      "recommended_architecture": "https://diagrams.example.com/recommended-arch.svg",
      "migration_roadmap": "https://diagrams.example.com/migration-roadmap.svg"
    }
  }'

Monitoring the AI Workflow

Track progress across all phases:

# Monitor overall project progress
taskdriver get-project-stats "ai-code-analysis"

# Check phase completion
taskdriver list-tasks "ai-code-analysis" --batch-id "ecommerce-analysis-phase1" --status completed
taskdriver list-tasks "ai-code-analysis" --batch-id "ecommerce-analysis-phase2" --status running
taskdriver list-tasks "ai-code-analysis" --batch-id "ecommerce-analysis-phase3" --status queued

# Monitor specific task types
taskdriver list-tasks "ai-code-analysis" --type-id "ai-security-analysis-task-type-id" --format detailed
taskdriver list-tasks "ai-code-analysis" --type-id "ai-architecture-review-task-type-id" --format detailed

# Check for any issues
taskdriver list-tasks "ai-code-analysis" --status failed
taskdriver cleanup-leases "ai-code-analysis"

Advanced AI Agent Coordination

Workflow Dependencies

Create tasks that depend on previous analysis results:

# After initial security analysis, create targeted tasks
taskdriver create-task "ai-code-analysis" "ai-security-analysis-task-type-id" \
  "Follow-up security analysis based on initial findings" \
  --variables '{
    "repository_url": "https://github.com/company/ecommerce-platform",
    "security_scope": "sql-injection-remediation,session-management-fixes",
    "environment": "production",
    "tech_stack": "Node.js,MongoDB",
    "compliance_standards": "PCI-DSS",
    "known_issues": "Reference task-id-123 for specific vulnerabilities to address"
  }' \
  --batch-id "ecommerce-analysis-remediation"

Cross-Agent Communication

Agents can reference previous analysis results:

# Documentation task that incorporates security findings
taskdriver create-task "ai-code-analysis" "ai-documentation-task-type-id" \
  "Security-focused developer documentation" \
  --variables '{
    "repository_url": "https://github.com/company/ecommerce-platform",
    "documentation_type": "security-guide,secure-coding-standards",
    "target_audience": "developers,security-team",
    "existing_docs": "security-analysis-report-from-task-id-456",
    "doc_standards": "security-best-practices,compliance-requirements"
  }' \
  --batch-id "ecommerce-analysis-documentation"

Benefits of AI Agent Workflows

  1. Specialized Expertise: Each agent focuses on their area of expertise
  2. Comprehensive Analysis: Multi-faceted analysis of complex systems
  3. Scalable Processing: Parallel execution of different analysis types
  4. Consistent Quality: Standardized templates and instructions ensure quality
  5. Audit Trail: Complete tracking of all analysis activities
  6. Iterative Improvement: Agents can build on previous analysis results

Best Practices for AI Agent Workflows

  1. Clear Instructions: Provide detailed, specific instructions for each agent
  2. Structured Output: Use consistent output formats for easier integration
  3. Confidence Scoring: Include confidence levels in AI-generated analysis
  4. Cross-References: Enable agents to reference previous analysis results
  5. Quality Validation: Implement checks for AI-generated content quality
  6. Human Review: Include human review steps for critical analysis
  7. Continuous Learning: Update instructions based on agent performance

This approach enables sophisticated AI-powered analysis workflows while maintaining control, quality, and traceability through TaskDriver's task management system.