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ADVANCED Expert Level

Advanced Topics

Master complex orckAI patterns and techniques

Deep dive into sophisticated workflows, performance optimization, and enterprise-grade patterns for building robust AI automation systems.

Multi-Step Workflows

Complex business processes often require orchestrating multiple AI agents, external systems, and conditional logic.

Complex Workflow Patterns

Sequential Processing

Step 1: Document Analysis Agent Input: PDF contract Output: Structured data (parties, dates, terms) Step 2: Risk Assessment Agent Input: Structured data from Step 1 Output: Risk score and flags Step 3: Approval Workflow Input: Risk score Condition: If risk > 0.7, route to legal team Output: Approval status Step 4: Database Update Input: All previous step outputs Action: Create contract record with metadata

Parallel Execution

Run multiple agents simultaneously for different aspects of the same input (e.g., sentiment analysis + content categorization + fact checking)

Conditional Branching

Route workflow execution based on agent outputs or business rules (e.g., escalation paths, approval workflows)

Advanced Trigger Patterns

  • File Drop Triggers: Process files automatically when uploaded to specific folders
  • Schedule Combinations: Multiple cron schedules for different business scenarios
  • Webhook Triggers: React to external system events in real-time
  • Threshold-Based: Trigger when metrics exceed defined limits
  • State-Based: Execute based on system or data state changes

    • Agent Chaining

    Create sophisticated AI pipelines by connecting specialized agents in sequence or parallel.

    Chaining Strategies

    Specialization Chain

    Each agent focused on specific domain expertise:

    • Legal document agent
    • Financial analysis agent
    • Technical review agent
    • Summary compilation agent

    Refinement Chain

    Iterative improvement through multiple passes:

    • Draft generation agent
    • Quality review agent
    • Enhancement agent
    • Final approval agent

    Example: Content Creation Pipeline

    Agent 1: Research Agent Prompt: "Research the latest trends in {topic}" Tools: Web search, knowledge base Output: Research summary with sources Agent 2: Content Outline Agent Prompt: "Create detailed outline for {content_type} about {topic}" Input: Research from Agent 1 Output: Structured outline with key points Agent 3: Writing Agent Prompt: "Write engaging {content_type} following the outline" Input: Outline from Agent 2, research from Agent 1 Output: Full content draft Agent 4: Review Agent Prompt: "Review content for accuracy, tone, and completeness" Input: Draft from Agent 3 Output: Reviewed content with suggested improvements
    Chaining Best Practice

    Design each agent with a single, well-defined responsibility. Pass relevant context between agents while avoiding information overload.

    Conditional Logic

    Implement complex business logic with conditional steps, loops, and decision trees.

    Condition Types

    Value Comparisons

    Compare agent outputs or variables against thresholds, strings, or patterns

    Data Presence

    Check if required data exists or meets quality criteria

    Time-Based

    Execute different logic based on business hours, dates, or deadlines

    Advanced Conditional Examples

    # Multi-factor approval workflow If sentiment_score < 0.3 AND customer_tier == "Premium": Route to senior support manager ElseIf sentiment_score < 0.3: Route to standard escalation queue ElseIf customer_tier == "Premium": Priority handling with 2-hour SLA Else: Standard automated response # Dynamic pricing logic If demand_score > 0.8 AND inventory_level < 0.2: Apply surge pricing (+20%) ElseIf customer_segment == "VIP": Apply VIP discount (-15%) ElseIf order_volume > 100: Apply bulk discount (-10%) Else: Standard pricing
    Complexity Warning

    Complex conditional logic can become difficult to maintain. Consider breaking large decision trees into separate workflows or using decision tables.

    Variable Interpolation

    Dynamic content generation using variables from previous steps, external systems, and user inputs.

    Interpolation Syntax

    # Basic variable substitution {{step1.customer_name}} - Output from previous step {{workflow.trigger_data.file_name}} - From trigger event {{system.current_date}} - System variables {{external.crm.account_status}} - From MCP server # Complex expressions {{step2.order_total * 0.1}} - Mathematical operations {{step1.customer_tier | upper}} - Text transformations {{workflow.data.items | length}} - Array operations {{external.db.users[step1.user_id].email}} - Nested access

    Advanced Variable Patterns

    Context Accumulation

    Build rich context by combining outputs from multiple workflow steps

    Context for Final Agent: - Customer: {{step1.customer_profile}} - Analysis: {{step2.document_analysis}} - Risk Score: {{step3.risk_assessment}} - Recommendations: {{step4.suggestions}}

    Dynamic Prompting

    Modify agent prompts based on workflow state and external data

    Base Prompt: "Analyze this document" If {{document.type}} == "contract": Add: "Focus on terms and obligations" If {{customer.tier}} == "enterprise": Add: "Provide executive summary"

    Variable Scoping

  • Step Variables: Outputs from individual workflow steps
  • Workflow Variables: Global variables available throughout execution
  • System Variables: Built-in values like timestamps and user context
  • External Variables: Real-time data from MCP servers and APIs

    • Error Handling Strategies

    Build resilient workflows that gracefully handle failures and edge cases.

    Error Types and Responses

    Agent Failures

    Causes: API limits, prompt issues, timeout

    Response: Retry with backoff, fallback prompts, human escalation

    Integration Failures

    Causes: API downtime, authentication issues, network problems

    Response: Circuit breakers, cached fallbacks, alternative services

    Data Quality Issues

    Causes: Invalid inputs, missing required fields, format errors

    Response: Validation steps, data cleaning, user notification

    Resilience Patterns

    # Retry with exponential backoff Try Agent Execution: Max Retries: 3 Backoff: 2s, 4s, 8s On Final Failure: Route to human queue # Graceful degradation Primary: AI Analysis with full context Fallback 1: AI Analysis with reduced context Fallback 2: Rule-based classification Fallback 3: Manual review queue # Circuit breaker pattern If External API failure rate > 50% in 5min: Open circuit - use cached responses After 60s: Test with single request If success: Close circuit, resume normal operation
    Monitoring Integration

    Implement comprehensive logging and alerting to detect patterns in failures and optimize retry strategies.

    Performance Optimization

    Optimize workflow execution speed, reduce costs, and improve reliability.

    Optimization Strategies

    Parallel Execution

    Run independent steps simultaneously to reduce total execution time

    Parallel Steps: - Step A: Sentiment Analysis - Step B: Content Categorization - Step C: Fact Verification Join Results: Combine all outputs

    Caching Strategies

    Cache expensive operations and reuse results when possible

  • Knowledge base embeddings
  • External API responses
  • Document analysis results
  • User profile data

    • Resource Management

    Token Optimization

    Minimize LLM token usage through prompt engineering and context management

    Queue Management

    Implement priority queues and load balancing for high-volume workflows

    Resource Pooling

    Share connections and instances across multiple workflow executions

    Performance Metrics

    Execution Time: Track step and total workflow duration | Token Usage: Monitor LLM costs per workflow | Success Rate: Measure completion vs failure rates | Throughput: Workflows processed per hour

    Security Best Practices

    Implement enterprise-grade security for AI workflows handling sensitive data.

    Data Protection

    Data Classification

    Classify data by sensitivity level and apply appropriate handling policies

  • Public - No restrictions
  • Internal - Organization only
  • Confidential - Need-to-know basis
  • Restricted - Highest security

    • Access Controls

    Implement role-based access control (RBAC) for workflows and data

  • Workflow execution permissions
  • Knowledge base access rights
  • MCP server connection limits
  • Output data restrictions

    • Secure Integration Patterns

    # Secure MCP Server Configuration { "connection": { "type": "docker", "auth": "api_key_header", "network_isolation": true, "read_only": true }, "data_access": { "allowed_tables": ["customers", "orders"], "forbidden_columns": ["ssn", "credit_card"], "row_level_security": true } } # Data Sanitization Before AI Processing: - Remove PII fields - Tokenize sensitive identifiers - Apply data masking rules - Log access attempts
    Compliance Considerations

    GDPR: Ensure data processing lawful basis and user consent | HIPAA: Implement technical safeguards for healthcare data | SOX: Maintain audit trails for financial processes

    API Integration Patterns

    Advanced patterns for integrating external systems and services into AI workflows.

    Integration Architectures

    Event-Driven Integration

    React to external system events in real-time

    Webhook Endpoint: /api/workflows/trigger Event: New customer signup Workflow: - Welcome email generation - Account setup automation - Onboarding workflow initiation

    Batch Processing

    Process large volumes of data efficiently

    Schedule: Daily at 2 AM Source: Data warehouse export Process: - Chunk data into batches - Parallel AI analysis - Aggregate results - Generate executive report

    Advanced MCP Patterns

  • Multi-Database Access: Connect to multiple databases with unified query interface
  • API Aggregation: Combine data from multiple REST APIs in single MCP server
  • Real-time Sync: Keep local cache synchronized with external system changes
  • Transform Layer: Apply data transformations and business logic in MCP layer
  • Connection Pooling: Optimize database connections for high-volume access
    • Integration Testing

    Implement comprehensive testing for external integrations including mock services, contract testing, and failure simulation.

    Ready for Advanced Implementation?

    Apply these advanced patterns to build enterprise-grade AI automation.

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