Orchestration Patterns

Orchestration patterns define how multiple agents coordinate to process events and achieve complex goals. AgenticGoKit provides five distinct orchestration modes, each optimized for different use cases and configured through agentflow.toml files.

Understanding orchestration is crucial because it determines how agents collaborate, share workload, and build upon each other's results in multi-agent systems.

Prerequisites

Understanding of Message Passing and Event Flow
Familiarity with State Management
Knowledge of Agent Lifecycle
Basic understanding of Go concurrency patterns

Learning Objectives

By the end of this tutorial, you'll understand:

All five orchestration modes and their characteristics
Configuration-based orchestration through agentflow.toml
Performance trade-offs and use case selection
Advanced orchestration patterns and customization
Monitoring and debugging orchestrated workflows

Configuration-Driven Orchestration

The agentflow.toml Configuration System

AgenticGoKit uses configuration files to define orchestration behavior, making it easy to change patterns without code modifications:

Basic ConfigurationRunner Setup

toml

[agent_flow]
name = "multi-agent-system"
version = "1.0.0"

[llm]
provider = "ollama"
model = "gemma3:1b"
temperature = 0.7

[orchestration]
mode = "collaborative"              # route, collaborative, sequential, loop, mixed
timeout_seconds = 30               # Global timeout for orchestration
max_iterations = 5                 # For loop mode
retry_attempts = 3                 # Retry failed operations

# Mode-specific configurations
collaborative_agents = ["analyzer", "validator", "processor"]
sequential_agents = ["collector", "transformer", "publisher"]
loop_agent = "quality_checker"

[agents.analyzer]
role = "data_analyzer"
system_prompt = "You analyze data for patterns and insights."
enabled = true
capabilities = ["analysis", "pattern_recognition"]

[agents.validator]
role = "data_validator"
system_prompt = "You validate data quality and consistency."
enabled = true
capabilities = ["validation", "quality_check"]

[agents.processor]
role = "data_processor"
system_prompt = "You process and transform data."
enabled = true
capabilities = ["processing", "transformation"]

package main

import (
    "context"
    "fmt"
    "log"
    "time"
    
    "github.com/kunalkushwaha/agenticgokit/core"
)

func main() {
    // Create runner with configuration - orchestration mode determined by config
    config := core.DefaultRunnerConfig()
    runner := core.NewRunnerWithConfig(config)
    
    // Note: Orchestration patterns are typically configured through
    // the orchestrator setup, not the runner configuration
    
    // Register agent handlers
    err = registerAgents(runner)
    if err != nil {
        log.Fatal("Failed to register agents:", err)
    }
    
    // Start the orchestrated system
    ctx := context.Background()
    err = runner.Start(ctx)
    if err != nil {
        log.Fatal("Failed to start runner:", err)
    }
    defer runner.Stop()
    
    // Emit events - orchestration handles the coordination
    demonstrateOrchestration(runner)
    
    // Keep running to see orchestration in action
    time.Sleep(10 * time.Second)
}

func registerAgents(runner core.Runner) error {
    // Register all agents that might be used in orchestration
    agents := map[string]core.AgentHandler{
        "analyzer":        createAnalyzerAgent(),
        "validator":       createValidatorAgent(),
        "processor":       createProcessorAgent(),
        "collector":       createCollectorAgent(),
        "transformer":     createTransformerAgent(),
        "publisher":       createPublisherAgent(),
        "quality_checker": createQualityCheckerAgent(),
    }
    
    for name, handler := range agents {
        if err := runner.RegisterAgent(name, handler); err != nil {
            return fmt.Errorf("failed to register agent %s: %w", name, err)
        }
    }
    
    return nil
}

func demonstrateOrchestration(runner core.Runner) {
    // Create event - orchestration pattern determined by configuration
    event := core.NewEvent("analyzer", 
        core.EventData{
            "task": "analyze_market_data",
            "dataset": "Q4_sales_data.json",
            "priority": "high",
        }, 
        map[string]string{
            "session_id": "orchestration-demo-001",
            "workflow_id": "market-analysis",
        })
    
    err := runner.Emit(event)
    if err != nil {
        core.Logger().Error().Err(err).Msg("Failed to emit orchestration event")
    }
}

Benefits of Configuration-Based Approach

No Code Changes: Switch orchestration modes by editing TOML files
Environment-Specific: Different configs for dev/staging/production
Runtime Flexibility: Change orchestration without rebuilding
Validation: Built-in validation of orchestration parameters
Consistency: Same configuration format across all projects

The Five Orchestration Patterns

1. Route Pattern (Default)

The simplest orchestration mode where events are routed directly to specific agents based on the target agent ID.

agentflow.tomlUsage

toml

[orchestration]
mode = "route"
timeout_seconds = 30

// Route pattern - events go directly to target agents
runner, err := core.NewRunnerFromConfig("agentflow.toml")
if err != nil {
    log.Fatal(err)
}

// Register agents
runner.RegisterAgent("processor", processorAgent)
runner.RegisterAgent("analyzer", analyzerAgent)

// Events are routed based on target agent ID
event := core.NewEvent("processor", 
    core.EventData{"task": "process_data"},
    map[string]string{
        "session_id": "session-123",
        "route": "processor",
    })

runner.Start(ctx)
runner.Emit(event) // Goes directly to "processor" agent

Characteristics:

Direct routing: Events go straight to the specified agent
Low latency: No orchestration overhead
Simple debugging: Clear event-to-agent mapping
High performance: Minimal coordination required

Use Cases:

Simple request-response scenarios
Microservice-style architectures
When agents have distinct, non-overlapping responsibilities

2. Collaborative Pattern

All specified agents process the same event in parallel, providing multiple perspectives on the same input.

agentflow.tomlImplementation

toml

[orchestration]
mode = "collaborative"
timeout_seconds = 45
collaborative_agents = ["analyzer", "validator", "processor"]

[agents.analyzer]
role = "data_analyzer"
system_prompt = "You analyze data for patterns and insights."
enabled = true

[agents.validator]
role = "data_validator"
system_prompt = "You validate data quality and accuracy."
enabled = true

[agents.processor]
role = "data_processor"
system_prompt = "You process and transform data."
enabled = true

func demonstrateCollaborative(runner core.Runner) {
    // All collaborative agents will process this event
    event := core.NewEvent("analyzer", // Target can be any collaborative agent
        core.EventData{
            "dataset": "customer_data.csv",
            "task": "comprehensive_analysis",
            "priority": "high",
        },
        map[string]string{
            "session_id": "collab-session-001",
            "analysis_type": "comprehensive",
        })
    
    err := runner.Emit(event)
    if err != nil {
        core.Logger().Error().Err(err).Msg("Failed to emit collaborative event")
    }
    
    // All three agents (analyzer, validator, processor) will process this event
    // Results are automatically aggregated by the orchestrator
}

Flow Diagram:

Characteristics:

Parallel processing: All agents work simultaneously
Multiple perspectives: Each agent contributes unique insights
Fault tolerance: System continues if some agents fail
Result aggregation: Outputs are automatically combined

Use Cases:

Multi-expert analysis systems
Ensemble machine learning approaches
Quality assurance with multiple validators
Research systems requiring diverse perspectives

The Five Orchestration Patterns

1. Route Pattern (Default)

The simplest orchestration mode where events are routed directly to specific agents based on the target agent ID.

agentflow.tomlUsage

toml

[orchestration]
mode = "route"
timeout_seconds = 30

// Route pattern - events go directly to target agents
runner, err := core.NewRunnerFromConfig("agentflow.toml")
if err != nil {
    log.Fatal(err)
}

// Register agents
runner.RegisterAgent("processor", processorAgent)
runner.RegisterAgent("analyzer", analyzerAgent)

// Events are routed based on target agent ID
event := core.NewEvent("processor", 
    core.EventData{"task": "process_data"},
    map[string]string{
        "session_id": "session-123",
        "route": "processor",
    })

runner.Start(ctx)
runner.Emit(event) // Goes directly to "processor" agent

Characteristics:

Direct routing: Events go straight to the specified agent
Low latency: No orchestration overhead
Simple debugging: Clear event-to-agent mapping
High performance: Minimal coordination required

Use Cases:

Simple request-response scenarios
Microservice-style architectures
When agents have distinct, non-overlapping responsibilities

2. Collaborative Pattern

All specified agents process the same event in parallel, providing multiple perspectives on the same input.

agentflow.tomlImplementation

toml

[orchestration]
mode = "collaborative"
timeout_seconds = 45
collaborative_agents = ["analyzer", "validator", "processor"]

[agents.analyzer]
role = "data_analyzer"
system_prompt = "You analyze data for patterns and insights."
enabled = true

[agents.validator]
role = "data_validator"
system_prompt = "You validate data quality and accuracy."
enabled = true

[agents.processor]
role = "data_processor"
system_prompt = "You process and transform data."
enabled = true

func demonstrateCollaborative(runner core.Runner) {
    // All collaborative agents will process this event
    event := core.NewEvent("analyzer", // Target can be any collaborative agent
        core.EventData{
            "dataset": "customer_data.csv",
            "task": "comprehensive_analysis",
            "priority": "high",
        },
        map[string]string{
            "session_id": "collab-session-001",
            "analysis_type": "comprehensive",
        })
    
    err := runner.Emit(event)
    if err != nil {
        core.Logger().Error().Err(err).Msg("Failed to emit collaborative event")
    }
    
    // All three agents (analyzer, validator, processor) will process this event
    // Results are automatically aggregated by the orchestrator
}

Flow Diagram:

Characteristics:

Parallel processing: All agents work simultaneously
Multiple perspectives: Each agent contributes unique insights
Fault tolerance: System continues if some agents fail
Result aggregation: Outputs are automatically combined

Use Cases:

Multi-expert analysis systems
Ensemble machine learning approaches
Quality assurance with multiple validators
Research systems requiring diverse perspectives

3. Sequential Pattern

Agents process events in a specific order, with each agent building upon the previous agent's output.

agentflow.tomlPipeline Implementation

toml

[orchestration]
mode = "sequential"
timeout_seconds = 60
sequential_agents = ["collector", "processor", "validator", "publisher"]

[agents.collector]
role = "data_collector"
system_prompt = "You collect and prepare raw data."
enabled = true

[agents.processor]
role = "data_processor"
system_prompt = "You process and transform collected data."
enabled = true

[agents.validator]
role = "data_validator"
system_prompt = "You validate processed data quality."
enabled = true

[agents.publisher]
role = "data_publisher"
system_prompt = "You format and publish validated data."
enabled = true

func createDataPipelineAgents() map[string]core.AgentHandler {
    return map[string]core.AgentHandler{
        "collector": core.AgentHandlerFunc(
            func(ctx context.Context, event core.Event, state core.State) (core.AgentResult, error) {
                startTime := time.Now()
                
                // Collect raw data
                rawData, err := collectDataFromSource(ctx, event.GetData())
                if err != nil {
                    return core.AgentResult{}, fmt.Errorf("data collection failed: %w", err)
                }
                
                // Create output state with collected data
                outputState := state.Clone()
                outputState.Set("raw_data", rawData)
                outputState.Set("collection_timestamp", time.Now().Unix())
                outputState.SetMeta("stage", "collection")
                outputState.SetMeta("next_stage", "processing")
                
                return core.AgentResult{
                    OutputState: outputState,
                    StartTime:   startTime,
                    EndTime:     time.Now(),
                    Duration:    time.Since(startTime),
                }, nil
            }),
        
        "processor": core.AgentHandlerFunc(
            func(ctx context.Context, event core.Event, state core.State) (core.AgentResult, error) {
                startTime := time.Now()
                
                // Get raw data from previous stage
                rawData, ok := state.Get("raw_data")
                if !ok {
                    return core.AgentResult{}, fmt.Errorf("no raw data from collection stage")
                }
                
                // Process the data
                processedData, err := processRawData(ctx, rawData)
                if err != nil {
                    return core.AgentResult{}, fmt.Errorf("data processing failed: %w", err)
                }
                
                // Add processed data to state
                outputState := state.Clone()
                outputState.Set("processed_data", processedData)
                outputState.Set("processing_timestamp", time.Now().Unix())
                outputState.SetMeta("stage", "processing")
                outputState.SetMeta("next_stage", "validation")
                
                return core.AgentResult{
                    OutputState: outputState,
                    StartTime:   startTime,
                    EndTime:     time.Now(),
                    Duration:    time.Since(startTime),
                }, nil
            }),
        
        "validator": core.AgentHandlerFunc(
            func(ctx context.Context, event core.Event, state core.State) (core.AgentResult, error) {
                startTime := time.Now()
                
                // Get processed data
                processedData, ok := state.Get("processed_data")
                if !ok {
                    return core.AgentResult{}, fmt.Errorf("no processed data from processing stage")
                }
                
                // Validate the data
                validationResult, err := validateData(ctx, processedData)
                if err != nil {
                    return core.AgentResult{}, fmt.Errorf("data validation failed: %w", err)
                }
                
                // Add validation results
                outputState := state.Clone()
                outputState.Set("validation_result", validationResult)
                outputState.Set("validation_timestamp", time.Now().Unix())
                outputState.SetMeta("stage", "validation")
                outputState.SetMeta("next_stage", "publishing")
                
                return core.AgentResult{
                    OutputState: outputState,
                    StartTime:   startTime,
                    EndTime:     time.Now(),
                    Duration:    time.Since(startTime),
                }, nil
            }),
        
        "publisher": core.AgentHandlerFunc(
            func(ctx context.Context, event core.Event, state core.State) (core.AgentResult, error) {
                startTime := time.Now()
                
                // Get all previous results
                processedData, _ := state.Get("processed_data")
                validationResult, _ := state.Get("validation_result")
                
                // Publish the final result
                publishResult, err := publishData(ctx, processedData, validationResult)
                if err != nil {
                    return core.AgentResult{}, fmt.Errorf("data publishing failed: %w", err)
                }
                
                // Final state
                outputState := state.Clone()
                outputState.Set("publish_result", publishResult)
                outputState.Set("pipeline_complete", true)
                outputState.SetMeta("stage", "complete")
                
                return core.AgentResult{
                    OutputState: outputState,
                    StartTime:   startTime,
                    EndTime:     time.Now(),
                    Duration:    time.Since(startTime),
                }, nil
            }),
    }
}

func demonstrateSequential(runner core.Runner) {
    // Register pipeline agents
    agents := createDataPipelineAgents()
    for name, handler := range agents {
        runner.RegisterAgent(name, handler)
    }
    
    // Start pipeline with initial event
    event := core.NewEvent("collector", // First agent in sequence
        core.EventData{
            "source": "database",
            "query": "SELECT * FROM customers",
            "format": "json",
        },
        map[string]string{
            "session_id": "pipeline-session-001",
            "pipeline_id": "data-processing-v1",
        })
    
    runner.Emit(event)
    // Orchestrator will automatically route through: collector → processor → validator → publisher
}

Flow Diagram:

Characteristics:

Ordered execution: Agents run in specified sequence
State propagation: Each agent builds on previous results
Pipeline processing: Perfect for data transformation workflows
Clear data flow: Easy to understand and debug

Use Cases:

Data processing pipelines
Document processing workflows
Multi-stage validation systems
ETL (Extract, Transform, Load) operations

4. Loop Pattern

A single agent processes the same event repeatedly until a completion condition is met or maximum iterations are reached.

agentflow.tomlLoop Implementation

toml

[orchestration]
mode = "loop"
timeout_seconds = 120
max_iterations = 10
loop_agent = "quality_improver"

[agents.quality_improver]
role = "quality_improver"
system_prompt = "You iteratively improve content quality until it meets standards."
enabled = true

func createQualityImproverAgent() core.AgentHandler {
    return core.AgentHandlerFunc(
        func(ctx context.Context, event core.Event, state core.State) (core.AgentResult, error) {
            startTime := time.Now()
            
            // Get current content and iteration count
            content, ok := state.Get("content")
            if !ok {
                return core.AgentResult{}, fmt.Errorf("no content to improve")
            }
            
            iteration := 0
            if iter, ok := state.Get("iteration"); ok {
                if iterInt, ok := iter.(int); ok {
                    iteration = iterInt
                }
            }
            
            // Improve the content
            improvedContent, qualityScore, err := improveContent(ctx, content.(string))
            if err != nil {
                return core.AgentResult{}, fmt.Errorf("content improvement failed: %w", err)
            }
            
            // Update state
            outputState := state.Clone()
            outputState.Set("content", improvedContent)
            outputState.Set("iteration", iteration+1)
            outputState.Set("quality_score", qualityScore)
            outputState.Set("improvement_timestamp", time.Now().Unix())
            
            // Check completion condition
            if qualityScore >= 0.95 {
                outputState.Set("loop_complete", true)
                outputState.SetMeta("completion_reason", "quality_threshold_met")
            } else if iteration >= 9 { // Max iterations - 1 (0-based)
                outputState.Set("loop_complete", true)
                outputState.SetMeta("completion_reason", "max_iterations_reached")
            }
            
            outputState.SetMeta("loop_iteration", fmt.Sprintf("%d", iteration+1))
            outputState.SetMeta("quality_score", fmt.Sprintf("%.2f", qualityScore))
            
            core.Logger().Info().
                Int("iteration", iteration+1).
                Float64("quality_score", qualityScore).
                Bool("complete", outputState.Get("loop_complete") != nil).
                Msg("Quality improvement iteration completed")
            
            return core.AgentResult{
                OutputState: outputState,
                StartTime:   startTime,
                EndTime:     time.Now(),
                Duration:    time.Since(startTime),
            }, nil
        })
}

func demonstrateLoop(runner core.Runner) {
    // Register loop agent
    runner.RegisterAgent("quality_improver", createQualityImproverAgent())
    
    // Start loop with initial content
    event := core.NewEvent("quality_improver",
        core.EventData{
            "content": "This is a draft document that needs improvement.",
            "target_quality": 0.95,
            "improvement_type": "comprehensive",
        },
        map[string]string{
            "session_id": "loop-session-001",
            "loop_id": "quality-improvement-v1",
        })
    
    runner.Emit(event)
    // Agent will loop until quality threshold is met or max iterations reached
}

func improveContent(ctx context.Context, content string) (string, float64, error) {
    // Simulate content improvement logic
    improved := fmt.Sprintf("Improved: %s", content)
    qualityScore := 0.7 + (0.1 * rand.Float64()) // Simulate gradual improvement
    return improved, qualityScore, nil
}

Flow Diagram:

Characteristics:

Iterative processing: Same agent runs multiple times
Condition-based termination: Stops when criteria are met
Quality improvement: Perfect for refinement workflows
Built-in safeguards: Maximum iteration limits prevent infinite loops

Use Cases:

Content quality improvement
Iterative optimization algorithms
Code review and refinement
Gradual learning systems

5. Mixed Pattern

Combines collaborative and sequential patterns, running collaborative agents first, then sequential agents.

agentflow.tomlMixed Implementation

toml

[orchestration]
mode = "mixed"
timeout_seconds = 90
collaborative_agents = ["analyzer", "validator", "enricher"]
sequential_agents = ["synthesizer", "formatter", "publisher"]

# Collaborative phase agents
[agents.analyzer]
role = "data_analyzer"
system_prompt = "You analyze data for insights and patterns."
enabled = true

[agents.validator]
role = "data_validator"
system_prompt = "You validate data accuracy and completeness."
enabled = true

[agents.enricher]
role = "data_enricher"
system_prompt = "You enrich data with additional context."
enabled = true

# Sequential phase agents
[agents.synthesizer]
role = "data_synthesizer"
system_prompt = "You synthesize analysis results into coherent insights."
enabled = true

[agents.formatter]
role = "report_formatter"
system_prompt = "You format synthesized data into professional reports."
enabled = true

[agents.publisher]
role = "report_publisher"
system_prompt = "You publish formatted reports to appropriate channels."
enabled = true

func demonstrateMixed(runner core.Runner) {
    // Register all agents for mixed orchestration
    collaborativeAgents := map[string]core.AgentHandler{
        "analyzer": createAnalyzerAgent(),
        "validator": createValidatorAgent(),
        "enricher": createEnricherAgent(),
    }
    
    sequentialAgents := map[string]core.AgentHandler{
        "synthesizer": createSynthesizerAgent(),
        "formatter": createFormatterAgent(),
        "publisher": createPublisherAgent(),
    }
    
    // Register all agents
    for name, handler := range collaborativeAgents {
        runner.RegisterAgent(name, handler)
    }
    for name, handler := range sequentialAgents {
        runner.RegisterAgent(name, handler)
    }
    
    // Start mixed orchestration
    event := core.NewEvent("analyzer", // Can target any collaborative agent
        core.EventData{
            "dataset": "market_research_data.json",
            "analysis_type": "comprehensive",
            "output_format": "executive_summary",
        },
        map[string]string{
            "session_id": "mixed-session-001",
            "workflow_id": "market-analysis-v1",
        })
    
    runner.Emit(event)
    
    // Orchestration flow:
    // 1. Collaborative phase: analyzer, validator, enricher run in parallel
    // 2. Results are aggregated
    // 3. Sequential phase: synthesizer → formatter → publisher
}

func createAnalyzerAgent() core.AgentHandler {
    return core.AgentHandlerFunc(
        func(ctx context.Context, event core.Event, state core.State) (core.AgentResult, error) {
            startTime := time.Now()
            
            // Perform analysis
            dataset, _ := event.GetData()["dataset"]
            analysisResult := map[string]interface{}{
                "trends": []string{"upward_trend", "seasonal_pattern"},
                "insights": []string{"market_growth", "customer_preference_shift"},
                "confidence": 0.92,
            }
            
            outputState := state.Clone()
            outputState.Set("analysis_result", analysisResult)
            outputState.SetMeta("analyzer_complete", "true")
            outputState.SetMeta("analysis_confidence", "0.92")
            
            return core.AgentResult{
                OutputState: outputState,
                StartTime:   startTime,
                EndTime:     time.Now(),
                Duration:    time.Since(startTime),
            }, nil
        })
}

func createSynthesizerAgent() core.AgentHandler {
    return core.AgentHandlerFunc(
        func(ctx context.Context, event core.Event, state core.State) (core.AgentResult, error) {
            startTime := time.Now()
            
            // Synthesize results from collaborative phase
            analysisResult, _ := state.Get("analysis_result")
            validationResult, _ := state.Get("validation_result")
            enrichmentResult, _ := state.Get("enrichment_result")
            
            synthesizedReport := map[string]interface{}{
                "executive_summary": "Market shows strong growth potential...",
                "key_findings": []string{"Finding 1", "Finding 2", "Finding 3"},
                "recommendations": []string{"Rec 1", "Rec 2", "Rec 3"},
                "data_sources": []string{"analysis", "validation", "enrichment"},
            }
            
            outputState := state.Clone()
            outputState.Set("synthesized_report", synthesizedReport)
            outputState.SetMeta("synthesis_complete", "true")
            
            return core.AgentResult{
                OutputState: outputState,
                StartTime:   startTime,
                EndTime:     time.Now(),
                Duration:    time.Since(startTime),
            }, nil
        })
}

Flow Diagram:

Characteristics:

Two-phase processing: Collaborative first, then sequential
Best of both worlds: Parallel analysis + ordered processing
Complex workflows: Handles sophisticated business processes
Automatic coordination: Orchestrator manages phase transitions

Use Cases:

Research and reporting workflows
Multi-expert analysis with structured output
Complex data processing with validation
Business intelligence systems

Pattern Selection Guide

Decision Matrix

Requirement	Route	Collaborative	Sequential	Loop	Mixed
Simple routing	✅	❌	❌	❌	❌
Multiple perspectives	❌	✅	❌	❌	✅
Data pipeline	❌	❌	✅	❌	✅
Iterative improvement	❌	❌	❌	✅	❌
Complex workflows	❌	❌	❌	❌	✅
Low latency	✅	❌	❌	❌	❌
High throughput	✅	✅	❌	❌	⚠️
Fault tolerance	❌	✅	❌	❌	✅

Performance Characteristics

Pattern	Latency	Throughput	Resource Usage	Complexity
Route	Low	High	Low	Low
Collaborative	Medium	High	High	Medium
Sequential	High	Low	Medium	Low
Loop	Very High	Very Low	Medium	Medium
Mixed	High	Medium	High	High

// Sequential processing with error handling
runner, _ := core.NewRunnerFromConfig("agentflow.toml")
_ = runner.RegisterAgent("validator", dataValidatorAgent)
_ = runner.RegisterAgent("transformer", dataTransformerAgent)
_ = runner.RegisterAgent("enricher", dataEnricherAgent)
_ = runner.RegisterAgent("publisher", dataPublisherAgent)

Quality Assurance Loop

// Loop until quality threshold is met
runner, _ := core.NewRunnerFromConfig("agentflow.toml")
_ = runner.RegisterAgent("quality-checker", qualityAgent)

Workflow Visualization

Use the CLI to generate and preview Mermaid diagrams of your orchestration flows.

API Reference

CLI Configuration Options

All CLI flags for multi-agent orchestration:

bash

# Orchestration mode flags
--orchestration-mode string          # collaborative, sequential, loop, mixed
--collaborative-agents string        # Comma-separated list of agents
--sequential-agents string           # Comma-separated list of agents
--loop-agent string                  # Single agent name for loop mode
--max-iterations int                 # Maximum loop iterations (default: 10)

# Configuration flags
--orchestration-timeout int          # Timeout in seconds (default: 60)
--failure-threshold float           # Failure threshold 0.0-1.0 (default: 0.5)
--max-concurrency int              # Maximum concurrent agents (default: 5)

# Visualization flags
--visualize                         # Generate Mermaid diagrams
--visualize-output string           # Custom output directory for diagrams

Orchestration Modes

type OrchestrationMode string

const (
    OrchestrationRoute       OrchestrationMode = "route"       // Route to single agent
    OrchestrationCollaborate OrchestrationMode = "collaborate" // Send to all agents
    OrchestrationSequential  OrchestrationMode = "sequential"  // Process in sequence
    OrchestrationLoop        OrchestrationMode = "loop"        // Loop single agent
    OrchestrationMixed       OrchestrationMode = "mixed"       // Combine patterns
)

Orchestrator Interface

type Orchestrator interface {
    Dispatch(ctx context.Context, event Event) (AgentResult, error)
    RegisterAgent(name string, handler AgentHandler) error
    GetCallbackRegistry() *CallbackRegistry
    Stop()
}

Advanced Configuration Options

OrchestrationConfig

type OrchestrationConfig struct {
    Timeout          time.Duration  // Overall orchestration timeout
    MaxConcurrency   int           // Maximum concurrent agents
    FailureThreshold float64       // Failure threshold (0.0-1.0)
    RetryPolicy      *RetryPolicy  // Retry configuration
}

RetryPolicy

type RetryPolicy struct {
    MaxRetries      int           // Maximum retry attempts
    InitialDelay    time.Duration // Initial delay before first retry
    MaxDelay        time.Duration // Maximum delay between retries  
    BackoffFactor   float64       // Exponential backoff multiplier
    Jitter          bool          // Add random jitter to delays
    RetryableErrors []string      // List of retryable error codes
}

Convenience Functions

The recommended approach is to construct runners using core.NewRunnerWithConfig() with appropriate configuration. Orchestration behavior is controlled through the orchestrator setup and agent registration patterns.

Migration from Internal APIs

If you were previously using internal orchestrator packages:

Replace internal/orchestrator imports with core
Use core.NewCollaborativeOrchestrator() instead of internal constructors
Update agent handlers to use public core.AgentHandler interface
Use public core.Event and core.State types

Performance Considerations

Collaborative orchestration runs agents concurrently for better performance
Configure MaxConcurrency to limit resource usage
Use timeouts to prevent resource leaks
Monitor agent execution times and optimize slow agents
Consider using FailureThreshold to fail fast when many agents are failing

Next Steps

Error Handling - Learn advanced error handling patterns
State Management - Understand state flow in orchestration
Visualization Guide - Learn workflow visualization
Performance Optimization - Scale your orchestration

Troubleshooting

Common Issues:

Deadlocks in Mixed Mode: Ensure proper state management between phases
Memory Leaks in Loop Mode: Implement proper termination conditions
Timeout Issues: Adjust timeout values based on your agent complexity
State Corruption: Use proper state isolation in collaborative mode
Agents Not Registered: Ensure all agents are registered before dispatching events
Configuration Errors: Validate your agentflow.toml configuration

For more help, see the Troubleshooting Guide.

A

dvanced Configuration Patterns

Environment-Specific Configurations

Use different orchestration patterns for different environments:

development.tomlstaging.tomlproduction.toml

toml

[orchestration]
mode = "route"
timeout_seconds = 10
# Simple routing for fast development cycles

toml

[orchestration]
mode = "collaborative"
timeout_seconds = 30
collaborative_agents = ["analyzer", "validator"]
# Collaborative for testing multi-agent interactions

toml

[orchestration]
mode = "mixed"
timeout_seconds = 60
collaborative_agents = ["analyzer", "validator", "enricher"]
sequential_agents = ["synthesizer", "formatter", "publisher"]
# Full mixed orchestration for production workflows

Dynamic Configuration Loading

Load configurations dynamically based on runtime conditions:

func createRunnerForEnvironment(env string) (core.Runner, error) {
    configFile := fmt.Sprintf("configs/%s.toml", env)
    
    runner, err := core.NewRunnerFromConfig(configFile)
    if err != nil {
        return nil, fmt.Errorf("failed to create runner for %s: %w", env, err)
    }
    
    core.Logger().Info().
        Str("environment", env).
        Str("config_file", configFile).
        Msg("Runner created for environment")
    
    return runner, nil
}

func main() {
    env := os.Getenv("ENVIRONMENT")
    if env == "" {
        env = "development"
    }
    
    runner, err := createRunnerForEnvironment(env)
    if err != nil {
        log.Fatal(err)
    }
    
    // Register agents and start processing
    setupAgents(runner)
    
    ctx := context.Background()
    runner.Start(ctx)
    defer runner.Stop()
}

Conditional Orchestration

Use different patterns based on event characteristics:

func setupConditionalOrchestration() {
    // Create multiple runners for different scenarios
    simpleRunner, _ := core.NewRunnerFromConfig("simple.toml")    // route mode
    complexRunner, _ := core.NewRunnerFromConfig("complex.toml")  // mixed mode
    
    // Route events based on complexity
    eventRouter := func(event core.Event) core.Runner {
        if complexity, ok := event.GetMetadataValue("complexity"); ok {
            switch complexity {
            case "simple":
                return simpleRunner
            case "complex":
                return complexRunner
            }
        }
        return simpleRunner // default
    }
    
    // Use appropriate runner based on event
    event := core.NewEvent("processor",
        core.EventData{"task": "analyze_data"},
        map[string]string{
            "complexity": "complex",
            "session_id": "session-123",
        })
    
    selectedRunner := eventRouter(event)
    selectedRunner.Emit(event)
}

Error Handling in Orchestration

Pattern-Specific Error Handling

Different orchestration patterns require different error handling strategies:

func setupErrorHandling(runner core.Runner) {
    // Error handling for collaborative patterns
    runner.RegisterCallback(core.HookAgentError, "collaborative-error-handler",
        func(ctx context.Context, args core.CallbackArgs) (core.State, error) {
            // In collaborative mode, continue if some agents fail
            core.Logger().Warn().
                Str("agent", args.AgentID).
                Str("event_id", args.Event.GetID()).
                Err(args.Error).
                Msg("Agent failed in collaborative orchestration")
            
            // Mark agent as failed but continue orchestration
            if args.State != nil {
                args.State.SetMeta(fmt.Sprintf("%s_failed", args.AgentID), "true")
                args.State.SetMeta(fmt.Sprintf("%s_error", args.AgentID), args.Error.Error())
            }
            
            return args.State, nil // Don't propagate error
        })
    
    // Error handling for sequential patterns
    runner.RegisterCallback(core.HookAgentError, "sequential-error-handler",
        func(ctx context.Context, args core.CallbackArgs) (core.State, error) {
            // In sequential mode, failure stops the pipeline
            core.Logger().Error().
                Str("agent", args.AgentID).
                Str("event_id", args.Event.GetID()).
                Err(args.Error).
                Msg("Agent failed in sequential orchestration - stopping pipeline")
            
            // Implement retry logic for sequential failures
            if shouldRetrySequentialFailure(args.Error) {
                retryEvent := createRetryEvent(args.Event, args.AgentID)
                runner.Emit(retryEvent)
            }
            
            return args.State, args.Error // Propagate error to stop pipeline
        })
}

func shouldRetrySequentialFailure(err error) bool {
    // Retry on temporary failures
    errStr := strings.ToLower(err.Error())
    return strings.Contains(errStr, "timeout") ||
           strings.Contains(errStr, "connection") ||
           strings.Contains(errStr, "temporary")
}

func createRetryEvent(originalEvent core.Event, failedAgent string) core.Event {
    metadata := make(map[string]string)
    for k, v := range originalEvent.GetMetadata() {
        metadata[k] = v
    }
    
    // Add retry information
    retryCount := 0
    if retryStr, ok := metadata["retry_count"]; ok {
        if count, err := strconv.Atoi(retryStr); err == nil {
            retryCount = count
        }
    }
    
    metadata["retry_count"] = strconv.Itoa(retryCount + 1)
    metadata["failed_agent"] = failedAgent
    metadata["retry_reason"] = "agent_failure"
    
    return core.NewEvent(failedAgent, originalEvent.GetData(), metadata)
}

Circuit Breaker Pattern

Implement circuit breakers for resilient orchestration:

type CircuitBreaker struct {
    failureCount    int
    successCount    int
    lastFailureTime time.Time
    state          string // "closed", "open", "half-open"
    threshold      int
    timeout        time.Duration
    mu             sync.RWMutex
}

func NewCircuitBreaker(threshold int, timeout time.Duration) *CircuitBreaker {
    return &CircuitBreaker{
        threshold: threshold,
        timeout:   timeout,
        state:     "closed",
    }
}

func (cb *CircuitBreaker) Call(fn func() error) error {
    cb.mu.Lock()
    defer cb.mu.Unlock()
    
    switch cb.state {
    case "open":
        if time.Since(cb.lastFailureTime) > cb.timeout {
            cb.state = "half-open"
            cb.successCount = 0
        } else {
            return fmt.Errorf("circuit breaker is open")
        }
    }
    
    err := fn()
    
    if err != nil {
        cb.failureCount++
        cb.lastFailureTime = time.Now()
        
        if cb.failureCount >= cb.threshold {
            cb.state = "open"
        }
        
        return err
    }
    
    // Success
    cb.successCount++
    if cb.state == "half-open" && cb.successCount >= 3 {
        cb.state = "closed"
        cb.failureCount = 0
    }
    
    return nil
}

// Use circuit breaker in orchestration
func setupCircuitBreaker(runner core.Runner) {
    breakers := make(map[string]*CircuitBreaker)
    
    runner.RegisterCallback(core.HookBeforeAgentRun, "circuit-breaker",
        func(ctx context.Context, args core.CallbackArgs) (core.State, error) {
            agentID := args.AgentID
            
            // Get or create circuit breaker for agent
            if _, exists := breakers[agentID]; !exists {
                breakers[agentID] = NewCircuitBreaker(5, 30*time.Second)
            }
            
            breaker := breakers[agentID]
            
            // Check circuit breaker before agent execution
            err := breaker.Call(func() error {
                // This will be called if circuit is closed/half-open
                return nil
            })
            
            if err != nil {
                return args.State, fmt.Errorf("circuit breaker prevented agent execution: %w", err)
            }
            
            return args.State, nil
        })
}

Monitoring and Observability

Orchestration Metrics

Track orchestration performance and health:

type OrchestrationMetrics struct {
    EventsProcessed    int64
    AverageLatency     time.Duration
    PatternUsage       map[string]int64
    AgentPerformance   map[string]AgentMetrics
    ErrorRates         map[string]float64
    mu                 sync.RWMutex
}

type AgentMetrics struct {
    ExecutionCount int64
    AverageLatency time.Duration
    ErrorCount     int64
    SuccessRate    float64
}

func NewOrchestrationMetrics() *OrchestrationMetrics {
    return &OrchestrationMetrics{
        PatternUsage:     make(map[string]int64),
        AgentPerformance: make(map[string]AgentMetrics),
        ErrorRates:       make(map[string]float64),
    }
}

func (om *OrchestrationMetrics) RecordEvent(pattern string, duration time.Duration, success bool) {
    om.mu.Lock()
    defer om.mu.Unlock()
    
    om.EventsProcessed++
    om.PatternUsage[pattern]++
    
    // Update average latency
    om.AverageLatency = (om.AverageLatency + duration) / 2
}

func (om *OrchestrationMetrics) RecordAgentExecution(agentID string, duration time.Duration, success bool) {
    om.mu.Lock()
    defer om.mu.Unlock()
    
    metrics := om.AgentPerformance[agentID]
    metrics.ExecutionCount++
    
    if success {
        metrics.AverageLatency = (metrics.AverageLatency + duration) / 2
    } else {
        metrics.ErrorCount++
    }
    
    metrics.SuccessRate = float64(metrics.ExecutionCount-metrics.ErrorCount) / float64(metrics.ExecutionCount)
    om.AgentPerformance[agentID] = metrics
}

func setupMetricsCollection(runner core.Runner, metrics *OrchestrationMetrics) {
    runner.RegisterCallback(core.HookAfterEventHandling, "metrics-collector",
        func(ctx context.Context, args core.CallbackArgs) (core.State, error) {
            // Determine orchestration pattern from metadata
            pattern := "route" // default
            if mode, ok := args.Event.GetMetadataValue("orchestration_mode"); ok {
                pattern = mode
            }
            
            // Calculate event processing duration
            startTime := args.Event.GetTimestamp()
            duration := time.Since(startTime)
            
            // Record metrics
            success := args.Error == nil
            metrics.RecordEvent(pattern, duration, success)
            
            return args.State, nil
        })
    
    runner.RegisterCallback(core.HookAfterAgentRun, "agent-metrics-collector",
        func(ctx context.Context, args core.CallbackArgs) (core.State, error) {
            success := args.Error == nil
            duration := args.AgentResult.Duration
            
            metrics.RecordAgentExecution(args.AgentID, duration, success)
            
            return args.State, nil
        })
}

Distributed Tracing

Implement distributed tracing for complex orchestrations:

func setupDistributedTracing(runner core.Runner) {
    runner.RegisterCallback(core.HookBeforeEventHandling, "trace-start",
        func(ctx context.Context, args core.CallbackArgs) (core.State, error) {
            traceID := generateTraceID()
            
            core.Logger().Info().
                Str("trace_id", traceID).
                Str("event_id", args.Event.GetID()).
                Str("orchestration_start", time.Now().Format(time.RFC3339)).
                Msg("Orchestration trace started")
            
            if args.State != nil {
                args.State.SetMeta("trace_id", traceID)
            }
            
            return args.State, nil
        })
    
    runner.RegisterCallback(core.HookBeforeAgentRun, "agent-trace",
        func(ctx context.Context, args core.CallbackArgs) (core.State, error) {
            traceID, _ := args.State.GetMeta("trace_id")
            spanID := generateSpanID()
            
            core.Logger().Debug().
                Str("trace_id", traceID).
                Str("span_id", spanID).
                Str("agent_id", args.AgentID).
                Str("event_id", args.Event.GetID()).
                Msg("Agent execution span started")
            
            args.State.SetMeta("current_span_id", spanID)
            
            return args.State, nil
        })
    
    runner.RegisterCallback(core.HookAfterAgentRun, "agent-trace-end",
        func(ctx context.Context, args core.CallbackArgs) (core.State, error) {
            traceID, _ := args.State.GetMeta("trace_id")
            spanID, _ := args.State.GetMeta("current_span_id")
            
            core.Logger().Debug().
                Str("trace_id", traceID).
                Str("span_id", spanID).
                Str("agent_id", args.AgentID).
                Dur("duration", args.AgentResult.Duration).
                Bool("success", args.Error == nil).
                Msg("Agent execution span completed")
            
            return args.State, nil
        })
}

func generateTraceID() string {
    return fmt.Sprintf("trace-%d", time.Now().UnixNano())
}

func generateSpanID() string {
    return fmt.Sprintf("span-%d", time.Now().UnixNano())
}

Best Practices and Guidelines

1. Pattern Selection Guidelines

Choose Route Pattern When:

You have simple, independent agent responsibilities
Low latency is critical
Agents don't need to collaborate
You want minimal orchestration overhead

Choose Collaborative Pattern When:

You need multiple perspectives on the same data
Fault tolerance is important
You want to maximize throughput
Agents provide complementary analysis

Choose Sequential Pattern When:

You have clear data transformation steps
Each step depends on the previous one
You need predictable data flow
You're building ETL or processing pipelines

Choose Loop Pattern When:

You need iterative improvement
Quality thresholds must be met
You have optimization algorithms
Gradual refinement is required

Choose Mixed Pattern When:

You need both analysis and processing
Workflows are complex and multi-stage
You want the benefits of both collaborative and sequential patterns

2. Performance Optimization

// Optimize collaborative orchestration
func optimizeCollaborative(runner core.Runner) {
    // Use timeouts to prevent slow agents from blocking
    runner.RegisterCallback(core.HookBeforeAgentRun, "timeout-guard",
        func(ctx context.Context, args core.CallbackArgs) (core.State, error) {
            timeout := 30 * time.Second
            if timeoutStr, ok := args.Event.GetMetadataValue("agent_timeout"); ok {
                if parsedTimeout, err := time.ParseDuration(timeoutStr); err == nil {
                    timeout = parsedTimeout
                }
            }
            
            ctx, cancel := context.WithTimeout(ctx, timeout)
            defer cancel()
            
            return args.State, nil
        })
}

// Optimize sequential orchestration
func optimizeSequential(runner core.Runner) {
    // Implement state compression to reduce memory usage
    runner.RegisterCallback(core.HookAfterAgentRun, "state-compressor",
        func(ctx context.Context, args core.CallbackArgs) (core.State, error) {
            // Remove unnecessary data from state to reduce memory
            if args.AgentResult.OutputState != nil {
                compressedState := compressState(args.AgentResult.OutputState)
                args.AgentResult.OutputState = compressedState
            }
            
            return args.State, nil
        })
}

func compressState(state core.State) core.State {
    // Keep only essential data for next stage
    essentialKeys := []string{"processed_data", "metadata", "session_info"}
    
    compressedState := core.NewState()
    for _, key := range essentialKeys {
        if value, ok := state.Get(key); ok {
            compressedState.Set(key, value)
        }
    }
    
    // Copy all metadata
    for _, key := range state.MetaKeys() {
        if value, ok := state.GetMeta(key); ok {
            compressedState.SetMeta(key, value)
        }
    }
    
    return compressedState
}

3. Testing Orchestration Patterns

func TestOrchestrationPatterns(t *testing.T) {
    testCases := []struct {
        name           string
        configFile     string
        expectedAgents []string
        expectedMode   string
    }{
        {
            name:           "Collaborative Pattern",
            configFile:     "test-collaborative.toml",
            expectedAgents: []string{"analyzer", "validator", "processor"},
            expectedMode:   "collaborative",
        },
        {
            name:           "Sequential Pattern",
            configFile:     "test-sequential.toml",
            expectedAgents: []string{"collector", "processor", "publisher"},
            expectedMode:   "sequential",
        },
    }
    
    for _, tc := range testCases {
        t.Run(tc.name, func(t *testing.T) {
            runner, err := core.NewRunnerFromConfig(tc.configFile)
            assert.NoError(t, err)
            
            // Register test agents
            for _, agentName := range tc.expectedAgents {
                err := runner.RegisterAgent(agentName, createTestAgent(agentName))
                assert.NoError(t, err)
            }
            
            // Test orchestration
            ctx := context.Background()
            err = runner.Start(ctx)
            assert.NoError(t, err)
            defer runner.Stop()
            
            // Emit test event
            event := core.NewEvent(tc.expectedAgents[0],
                core.EventData{"test": "data"},
                map[string]string{"session_id": "test-session"})
            
            err = runner.Emit(event)
            assert.NoError(t, err)
            
            // Verify orchestration behavior
            // (Add specific assertions based on expected behavior)
        })
    }
}

func createTestAgent(name string) core.AgentHandler {
    return core.AgentHandlerFunc(
        func(ctx context.Context, event core.Event, state core.State) (core.AgentResult, error) {
            startTime := time.Now()
            
            outputState := state.Clone()
            outputState.Set(fmt.Sprintf("%s_processed", name), true)
            outputState.SetMeta(fmt.Sprintf("%s_timestamp", name), time.Now().Format(time.RFC3339))
            
            return core.AgentResult{
                OutputState: outputState,
                StartTime:   startTime,
                EndTime:     time.Now(),
                Duration:    time.Since(startTime),
            }, nil
        })
}

Troubleshooting Common Issues

1. Orchestration Not Working

// Debug orchestration configuration
func debugOrchestration(configFile string) {
    runner, err := core.NewRunnerFromConfig(configFile)
    if err != nil {
        core.Logger().Error().Err(err).Msg("Failed to create runner")
        return
    }
    
    // Check registered agents
    // Note: This would require exposing agent registry in the Runner interface
    core.Logger().Info().Msg("Debugging orchestration setup")
    
    // Verify configuration
    core.Logger().Info().Str("config_file", configFile).Msg("Configuration loaded")
}

2. Performance Issues

// Monitor orchestration performance
func monitorPerformance(runner core.Runner) {
    runner.RegisterCallback(core.HookAfterEventHandling, "performance-monitor",
        func(ctx context.Context, args core.CallbackArgs) (core.State, error) {
            duration := time.Since(args.Event.GetTimestamp())
            
            if duration > 5*time.Second {
                core.Logger().Warn().
                    Str("event_id", args.Event.GetID()).
                    Dur("duration", duration).
                    Msg("Slow orchestration detected")
            }
            
            return args.State, nil
        })
}

3. Agent Coordination Issues

// Debug agent coordination
func debugAgentCoordination(runner core.Runner) {
    runner.RegisterCallback(core.HookBeforeAgentRun, "coordination-debug",
        func(ctx context.Context, args core.CallbackArgs) (core.State, error) {
            core.Logger().Debug().
                Str("agent", args.AgentID).
                Str("event_id", args.Event.GetID()).
                Interface("state_keys", args.State.Keys()).
                Msg("Agent coordination debug")
            
            return args.State, nil
        })
}

Next Steps

Now that you understand orchestration patterns in AgenticGoKit:

Explore Error Handling - Learn resilience patterns for orchestrated systems
Study Message Passing - Understand how events flow through orchestration
Review State Management - See how state flows through different patterns
Check out Agent Lifecycle - Learn how agents participate in orchestration

Summary

Orchestration patterns in AgenticGoKit provide:

Five distinct patterns for different use cases and requirements
Configuration-driven approach for easy pattern switching
Comprehensive error handling and resilience features
Built-in monitoring and observability capabilities
Performance optimization strategies for each pattern
Testing and debugging tools for complex workflows

Choose the right orchestration pattern based on your specific requirements, and leverage the configuration system to adapt your workflows as they evolve.### 3. Sequential Pattern

Agents process events in a specific order, with each agent building upon the previous agent's output.

agentflow.tomlPipeline Implementation

toml

[orchestration]
mode = "sequential"
timeout_seconds = 60
sequential_agents = ["collector", "processor", "validator", "publisher"]

[agents.collector]
role = "data_collector"
system_prompt = "You collect and prepare raw data."
enabled = true

[agents.processor]
role = "data_processor"
system_prompt = "You process and transform collected data."
enabled = true

[agents.validator]
role = "data_validator"
system_prompt = "You validate processed data quality."
enabled = true

[agents.publisher]
role = "data_publisher"
system_prompt = "You format and publish validated data."
enabled = true

func createDataPipelineAgents() map[string]core.AgentHandler {
    return map[string]core.AgentHandler{
        "collector": core.AgentHandlerFunc(
            func(ctx context.Context, event core.Event, state core.State) (core.AgentResult, error) {
                startTime := time.Now()
                
                // Collect raw data
                rawData, err := collectDataFromSource(ctx, event.GetData())
                if err != nil {
                    return core.AgentResult{}, fmt.Errorf("data collection failed: %w", err)
                }
                
                // Create output state with collected data
                outputState := state.Clone()
                outputState.Set("raw_data", rawData)
                outputState.Set("collection_timestamp", time.Now().Unix())
                outputState.SetMeta("stage", "collection")
                outputState.SetMeta("next_stage", "processing")
                
                return core.AgentResult{
                    OutputState: outputState,
                    StartTime:   startTime,
                    EndTime:     time.Now(),
                    Duration:    time.Since(startTime),
                }, nil
            }),
        
        "processor": core.AgentHandlerFunc(
            func(ctx context.Context, event core.Event, state core.State) (core.AgentResult, error) {
                startTime := time.Now()
                
                // Get raw data from previous stage
                rawData, ok := state.Get("raw_data")
                if !ok {
                    return core.AgentResult{}, fmt.Errorf("no raw data from collection stage")
                }
                
                // Process the data
                processedData, err := processRawData(ctx, rawData)
                if err != nil {
                    return core.AgentResult{}, fmt.Errorf("data processing failed: %w", err)
                }
                
                // Add processed data to state
                outputState := state.Clone()
                outputState.Set("processed_data", processedData)
                outputState.Set("processing_timestamp", time.Now().Unix())
                outputState.SetMeta("stage", "processing")
                outputState.SetMeta("next_stage", "validation")
                
                return core.AgentResult{
                    OutputState: outputState,
                    StartTime:   startTime,
                    EndTime:     time.Now(),
                    Duration:    time.Since(startTime),
                }, nil
            }),
        
        "validator": core.AgentHandlerFunc(
            func(ctx context.Context, event core.Event, state core.State) (core.AgentResult, error) {
                startTime := time.Now()
                
                // Get processed data
                processedData, ok := state.Get("processed_data")
                if !ok {
                    return core.AgentResult{}, fmt.Errorf("no processed data from processing stage")
                }
                
                // Validate the data
                validationResult, err := validateData(ctx, processedData)
                if err != nil {
                    return core.AgentResult{}, fmt.Errorf("data validation failed: %w", err)
                }
                
                // Add validation results
                outputState := state.Clone()
                outputState.Set("validation_result", validationResult)
                outputState.Set("validation_timestamp", time.Now().Unix())
                outputState.SetMeta("stage", "validation")
                outputState.SetMeta("next_stage", "publishing")
                
                return core.AgentResult{
                    OutputState: outputState,
                    StartTime:   startTime,
                    EndTime:     time.Now(),
                    Duration:    time.Since(startTime),
                }, nil
            }),
        
        "publisher": core.AgentHandlerFunc(
            func(ctx context.Context, event core.Event, state core.State) (core.AgentResult, error) {
                startTime := time.Now()
                
                // Get all previous results
                processedData, _ := state.Get("processed_data")
                validationResult, _ := state.Get("validation_result")
                
                // Publish the final result
                publishResult, err := publishData(ctx, processedData, validationResult)
                if err != nil {
                    return core.AgentResult{}, fmt.Errorf("data publishing failed: %w", err)
                }
                
                // Final state
                outputState := state.Clone()
                outputState.Set("publish_result", publishResult)
                outputState.Set("pipeline_complete", true)
                outputState.SetMeta("stage", "complete")
                
                return core.AgentResult{
                    OutputState: outputState,
                    StartTime:   startTime,
                    EndTime:     time.Now(),
                    Duration:    time.Since(startTime),
                }, nil
            }),
    }
}

func demonstrateSequential(runner core.Runner) {
    // Register pipeline agents
    agents := createDataPipelineAgents()
    for name, handler := range agents {
        runner.RegisterAgent(name, handler)
    }
    
    // Start pipeline with initial event
    event := core.NewEvent("collector", // First agent in sequence
        core.EventData{
            "source": "database",
            "query": "SELECT * FROM customers",
            "format": "json",
        },
        map[string]string{
            "session_id": "pipeline-session-001",
            "pipeline_id": "data-processing-v1",
        })
    
    runner.Emit(event)
    // Orchestrator will automatically route through: collector → processor → validator → publisher
}

Flow Diagram:

Characteristics:

Ordered execution: Agents run in specified sequence
State propagation: Each agent builds on previous results
Pipeline processing: Perfect for data transformation workflows
Clear data flow: Easy to understand and debug

Use Cases:

Data processing pipelines
Document processing workflows
Multi-stage validation systems
ETL (Extract, Transform, Load) operations

4. Loop Pattern

A single agent processes the same event repeatedly until a completion condition is met or maximum iterations are reached.

agentflow.tomlLoop Implementation

toml

[orchestration]
mode = "loop"
timeout_seconds = 120
max_iterations = 10
loop_agent = "quality_improver"

[agents.quality_improver]
role = "quality_improver"
system_prompt = "You iteratively improve content quality until it meets standards."
enabled = true

func createQualityImproverAgent() core.AgentHandler {
    return core.AgentHandlerFunc(
        func(ctx context.Context, event core.Event, state core.State) (core.AgentResult, error) {
            startTime := time.Now()
            
            // Get current content and iteration count
            content, ok := state.Get("content")
            if !ok {
                return core.AgentResult{}, fmt.Errorf("no content to improve")
            }
            
            iteration := 0
            if iter, ok := state.Get("iteration"); ok {
                if iterInt, ok := iter.(int); ok {
                    iteration = iterInt
                }
            }
            
            // Improve the content
            improvedContent, qualityScore, err := improveContent(ctx, content.(string))
            if err != nil {
                return core.AgentResult{}, fmt.Errorf("content improvement failed: %w", err)
            }
            
            // Update state
            outputState := state.Clone()
            outputState.Set("content", improvedContent)
            outputState.Set("iteration", iteration+1)
            outputState.Set("quality_score", qualityScore)
            outputState.Set("improvement_timestamp", time.Now().Unix())
            
            // Check completion condition
            if qualityScore >= 0.95 {
                outputState.Set("loop_complete", true)
                outputState.SetMeta("completion_reason", "quality_threshold_met")
            } else if iteration >= 9 { // Max iterations - 1 (0-based)
                outputState.Set("loop_complete", true)
                outputState.SetMeta("completion_reason", "max_iterations_reached")
            }
            
            outputState.SetMeta("loop_iteration", fmt.Sprintf("%d", iteration+1))
            outputState.SetMeta("quality_score", fmt.Sprintf("%.2f", qualityScore))
            
            core.Logger().Info().
                Int("iteration", iteration+1).
                Float64("quality_score", qualityScore).
                Bool("complete", outputState.Get("loop_complete") != nil).
                Msg("Quality improvement iteration completed")
            
            return core.AgentResult{
                OutputState: outputState,
                StartTime:   startTime,
                EndTime:     time.Now(),
                Duration:    time.Since(startTime),
            }, nil
        })
}

func demonstrateLoop(runner core.Runner) {
    // Register loop agent
    runner.RegisterAgent("quality_improver", createQualityImproverAgent())
    
    // Start loop with initial content
    event := core.NewEvent("quality_improver",
        core.EventData{
            "content": "This is a draft document that needs improvement.",
            "target_quality": 0.95,
            "improvement_type": "comprehensive",
        },
        map[string]string{
            "session_id": "loop-session-001",
            "loop_id": "quality-improvement-v1",
        })
    
    runner.Emit(event)
    // Agent will loop until quality threshold is met or max iterations reached
}

func improveContent(ctx context.Context, content string) (string, float64, error) {
    // Simulate content improvement logic
    improved := fmt.Sprintf("Improved: %s", content)
    qualityScore := 0.7 + (0.1 * rand.Float64()) // Simulate gradual improvement
    return improved, qualityScore, nil
}

Flow Diagram:

Characteristics:

Iterative processing: Same agent runs multiple times
Condition-based termination: Stops when criteria are met
Quality improvement: Perfect for refinement workflows
Built-in safeguards: Maximum iteration limits prevent infinite loops

Use Cases:

Content quality improvement
Iterative optimization algorithms
Code review and refinement
Gradual learning systems

5. Mixed Pattern

Combines collaborative and sequential patterns, running collaborative agents first, then sequential agents.

agentflow.tomlMixed Implementation

toml

[orchestration]
mode = "mixed"
timeout_seconds = 90
collaborative_agents = ["analyzer", "validator", "enricher"]
sequential_agents = ["synthesizer", "formatter", "publisher"]

# Collaborative phase agents
[agents.analyzer]
role = "data_analyzer"
system_prompt = "You analyze data for insights and patterns."
enabled = true

[agents.validator]
role = "data_validator"
system_prompt = "You validate data accuracy and completeness."
enabled = true

[agents.enricher]
role = "data_enricher"
system_prompt = "You enrich data with additional context."
enabled = true

# Sequential phase agents
[agents.synthesizer]
role = "data_synthesizer"
system_prompt = "You synthesize analysis results into coherent insights."
enabled = true

[agents.formatter]
role = "report_formatter"
system_prompt = "You format synthesized data into professional reports."
enabled = true

[agents.publisher]
role = "report_publisher"
system_prompt = "You publish formatted reports to appropriate channels."
enabled = true

func demonstrateMixed(runner core.Runner) {
    // Register all agents for mixed orchestration
    collaborativeAgents := map[string]core.AgentHandler{
        "analyzer": createAnalyzerAgent(),
        "validator": createValidatorAgent(),
        "enricher": createEnricherAgent(),
    }
    
    sequentialAgents := map[string]core.AgentHandler{
        "synthesizer": createSynthesizerAgent(),
        "formatter": createFormatterAgent(),
        "publisher": createPublisherAgent(),
    }
    
    // Register all agents
    for name, handler := range collaborativeAgents {
        runner.RegisterAgent(name, handler)
    }
    for name, handler := range sequentialAgents {
        runner.RegisterAgent(name, handler)
    }
    
    // Start mixed orchestration
    event := core.NewEvent("analyzer", // Can target any collaborative agent
        core.EventData{
            "dataset": "market_research_data.json",
            "analysis_type": "comprehensive",
            "output_format": "executive_summary",
        },
        map[string]string{
            "session_id": "mixed-session-001",
            "workflow_id": "market-analysis-v1",
        })
    
    runner.Emit(event)
    
    // Orchestration flow:
    // 1. Collaborative phase: analyzer, validator, enricher run in parallel
    // 2. Results are aggregated
    // 3. Sequential phase: synthesizer → formatter → publisher
}

func createAnalyzerAgent() core.AgentHandler {
    return core.AgentHandlerFunc(
        func(ctx context.Context, event core.Event, state core.State) (core.AgentResult, error) {
            startTime := time.Now()
            
            // Perform analysis
            dataset, _ := event.GetData()["dataset"]
            analysisResult := map[string]interface{}{
                "trends": []string{"upward_trend", "seasonal_pattern"},
                "insights": []string{"market_growth", "customer_preference_shift"},
                "confidence": 0.92,
            }
            
            outputState := state.Clone()
            outputState.Set("analysis_result", analysisResult)
            outputState.SetMeta("analyzer_complete", "true")
            outputState.SetMeta("analysis_confidence", "0.92")
            
            return core.AgentResult{
                OutputState: outputState,
                StartTime:   startTime,
                EndTime:     time.Now(),
                Duration:    time.Since(startTime),
            }, nil
        })
}

func createSynthesizerAgent() core.AgentHandler {
    return core.AgentHandlerFunc(
        func(ctx context.Context, event core.Event, state core.State) (core.AgentResult, error) {
            startTime := time.Now()
            
            // Synthesize results from collaborative phase
            analysisResult, _ := state.Get("analysis_result")
            validationResult, _ := state.Get("validation_result")
            enrichmentResult, _ := state.Get("enrichment_result")
            
            synthesizedReport := map[string]interface{}{
                "executive_summary": "Market shows strong growth potential...",
                "key_findings": []string{"Finding 1", "Finding 2", "Finding 3"},
                "recommendations": []string{"Rec 1", "Rec 2", "Rec 3"},
                "data_sources": []string{"analysis", "validation", "enrichment"},
            }
            
            outputState := state.Clone()
            outputState.Set("synthesized_report", synthesizedReport)
            outputState.SetMeta("synthesis_complete", "true")
            
            return core.AgentResult{
                OutputState: outputState,
                StartTime:   startTime,
                EndTime:     time.Now(),
                Duration:    time.Since(startTime),
            }, nil
        })
}

Flow Diagram:

Characteristics:

Two-phase processing: Collaborative first, then sequential
Best of both worlds: Parallel analysis + ordered processing
Complex workflows: Handles sophisticated business processes
Automatic coordination: Orchestrator manages phase transitions

Use Cases:

Research and reporting workflows
Multi-expert analysis with structured output
Complex data processing with validation
Business intelligence systems## Pattern Selection Guide

Decision Matrix

Requirement	Route	Collaborative	Sequential	Loop	Mixed
Simple routing	✅	❌	❌	❌	❌
Multiple perspectives	❌	✅	❌	❌	✅
Data pipeline	❌	❌	✅	❌	✅
Iterative improvement	❌	❌	❌	✅	❌
Complex workflows	❌	❌	❌	❌	✅
Low latency	✅	❌	❌	❌	❌
High throughput	✅	✅	❌	❌	⚠️
Fault tolerance	❌	✅	❌	❌	✅

Performance Characteristics

Pattern	Latency	Throughput	Resource Usage	Complexity
Route	Low	High	Low	Low
Collaborative	Medium	High	High	Medium
Sequential	High	Low	Medium	Low
Loop	Very High	Very Low	Medium	Medium
Mixed	High	Medium	High	High

Selection Guidelines

Choose your orchestration pattern based on these criteria:

Route Pattern when:

You have simple, independent tasks
Each agent has a specific, non-overlapping responsibility
You need maximum performance and minimal latency
Debugging and monitoring simplicity is important

Collaborative Pattern when:

You need multiple expert opinions on the same data
Fault tolerance is critical (some agents can fail)
You want to leverage ensemble approaches
Different agents provide complementary insights

Sequential Pattern when:

You have a clear data transformation pipeline
Each step depends on the previous step's output
You need ordered, predictable processing
Data flows through distinct processing stages

Loop Pattern when:

You need iterative refinement or improvement
Quality thresholds must be met
Gradual optimization is required
You have convergence criteria

Mixed Pattern when:

You need both parallel analysis and ordered processing
Complex business workflows require multiple phases
You want comprehensive analysis followed by structured output
Maximum flexibility and capability is required

Advanced Orchestration Patterns

Dynamic Pattern Switching

Switch orchestration patterns at runtime based on conditions:

func createDynamicRunner(configPath string) (core.Runner, error) {
    // Load base configuration
    runner, err := core.NewRunnerFromConfig(configPath)
    if err != nil {
        return nil, err
    }
    
    // Add callback to switch patterns based on event metadata
    runner.RegisterCallback(core.HookBeforeEventHandling, "pattern-switcher",
        func(ctx context.Context, args core.CallbackArgs) (core.State, error) {
            // Check if event requests specific orchestration
            if pattern, exists := args.Event.GetMetadata()["orchestration_override"]; exists {
                // Dynamically switch orchestration pattern
                // Note: This would require custom orchestrator implementation
                core.Logger().Info().
                    Str("requested_pattern", pattern).
                    Str("event_id", args.Event.GetID()).
                    Msg("Switching orchestration pattern")
            }
            
            return args.State, nil
        })
    
    return runner, nil
}

Conditional Orchestration

Use callbacks to implement conditional orchestration logic:

func setupConditionalOrchestration(runner core.Runner) {
    // Route based on data characteristics
    runner.RegisterCallback(core.HookBeforeEventHandling, "conditional-router",
        func(ctx context.Context, args core.CallbackArgs) (core.State, error) {
            data := args.Event.GetData()
            
            // Route based on data size
            if dataSize, ok := data["size"]; ok {
                if size, ok := dataSize.(int); ok {
                    var targetPattern string
                    switch {
                    case size < 1000:
                        targetPattern = "route"
                    case size < 10000:
                        targetPattern = "collaborative"
                    default:
                        targetPattern = "sequential"
                    }
                    
                    // Add routing hint to state
                    enrichedState := args.State.Clone()
                    enrichedState.SetMeta("suggested_pattern", targetPattern)
                    enrichedState.SetMeta("routing_reason", fmt.Sprintf("data_size: %d", size))
                    
                    return enrichedState, nil
                }
            }
            
            return args.State, nil
        })
}

Nested Orchestration

Implement orchestration within orchestration for complex workflows:

func createNestedOrchestrationExample() {
    // Main orchestrator uses sequential pattern
    mainRunner, _ := core.NewRunnerFromConfig("main-sequential.toml")
    
    // Sub-orchestrator for collaborative analysis
    analysisRunner, _ := core.NewRunnerFromConfig("analysis-collaborative.toml")
    
    // Create nested agent that uses sub-orchestrator
    nestedAgent := core.AgentHandlerFunc(
        func(ctx context.Context, event core.Event, state core.State) (core.AgentResult, error) {
            startTime := time.Now()
            
            // Use sub-orchestrator for complex analysis
            analysisEvent := core.NewEvent("analyzer",
                event.GetData(),
                map[string]string{
                    "session_id": event.GetSessionID(),
                    "parent_event_id": event.GetID(),
                })
            
            // Emit to sub-orchestrator
            err := analysisRunner.Emit(analysisEvent)
            if err != nil {
                return core.AgentResult{}, fmt.Errorf("nested orchestration failed: %w", err)
            }
            
            // Wait for sub-orchestrator results (simplified)
            // In practice, you'd use proper synchronization
            time.Sleep(2 * time.Second)
            
            // Create result state
            outputState := state.Clone()
            outputState.Set("nested_analysis_complete", true)
            outputState.SetMeta("nested_orchestration", "collaborative")
            
            return core.AgentResult{
                OutputState: outputState,
                StartTime:   startTime,
                EndTime:     time.Now(),
                Duration:    time.Since(startTime),
            }, nil
        })
    
    mainRunner.RegisterAgent("nested_analyzer", nestedAgent)
}

Error Handling in Orchestration

Orchestration-Specific Error Patterns

Different patterns have different error characteristics:

func setupOrchestrationErrorHandling(runner core.Runner) {
    runner.RegisterCallback(core.HookAgentError, "orchestration-error-handler",
        func(ctx context.Context, args core.CallbackArgs) (core.State, error) {
            // Get orchestration mode from configuration
            orchestrationMode := getOrchestrationMode(runner)
            
            switch orchestrationMode {
            case "collaborative":
                // In collaborative mode, continue if some agents succeed
                return handleCollaborativeError(args)
            case "sequential":
                // In sequential mode, stop the pipeline on error
                return handleSequentialError(args)
            case "loop":
                // In loop mode, retry with backoff
                return handleLoopError(args)
            default:
                return args.State, nil
            }
        })
}

func handleCollaborativeError(args core.CallbackArgs) (core.State, error) {
    // Mark this agent as failed but continue with others
    errorState := args.State.Clone()
    errorState.SetMeta(fmt.Sprintf("%s_failed", args.AgentID), "true")
    errorState.SetMeta(fmt.Sprintf("%s_error", args.AgentID), args.Error.Error())
    
    core.Logger().Warn().
        Str("agent", args.AgentID).
        Err(args.Error).
        Msg("Collaborative agent failed, continuing with others")
    
    return errorState, nil
}

func handleSequentialError(args core.CallbackArgs) (core.State, error) {
    // Stop the entire pipeline
    errorState := args.State.Clone()
    errorState.Set("pipeline_failed", true)
    errorState.Set("failed_stage", args.AgentID)
    errorState.SetMeta("pipeline_error", args.Error.Error())
    
    core.Logger().Error().
        Str("agent", args.AgentID).
        Err(args.Error).
        Msg("Sequential pipeline failed")
    
    return errorState, fmt.Errorf("pipeline failed at stage %s: %w", args.AgentID, args.Error)
}

func handleLoopError(args core.CallbackArgs) (core.State, error) {
    // Implement retry logic for loop pattern
    retryCount := 0
    if count, ok := args.State.Get("retry_count"); ok {
        if countInt, ok := count.(int); ok {
            retryCount = countInt
        }
    }
    
    if retryCount < 3 {
        // Retry the loop iteration
        retryState := args.State.Clone()
        retryState.Set("retry_count", retryCount+1)
        retryState.SetMeta("last_error", args.Error.Error())
        
        core.Logger().Warn().
            Int("retry_count", retryCount+1).
            Err(args.Error).
            Msg("Loop iteration failed, retrying")
        
        return retryState, nil
    }
    
    // Max retries exceeded
    return args.State, fmt.Errorf("loop failed after %d retries: %w", retryCount, args.Error)
}

Monitoring and Observability

Orchestration Metrics

Track orchestration-specific metrics:

func setupOrchestrationMonitoring(runner core.Runner) {
    // Track orchestration performance
    runner.RegisterCallback(core.HookAfterEventHandling, "orchestration-metrics",
        func(ctx context.Context, args core.CallbackArgs) (core.State, error) {
            orchestrationMode := getOrchestrationMode(runner)
            
            // Log orchestration completion
            core.Logger().Info().
                Str("orchestration_mode", orchestrationMode).
                Str("event_id", args.Event.GetID()).
                Str("session_id", args.Event.GetSessionID()).
                Dur("total_duration", time.Since(args.Event.GetTimestamp())).
                Msg("Orchestration completed")
            
            // Add metrics to state
            metricsState := args.State.Clone()
            metricsState.SetMeta("orchestration_mode", orchestrationMode)
            metricsState.SetMeta("completion_time", time.Now().Format(time.RFC3339))
            
            return metricsState, nil
        })
    
    // Track agent participation in orchestration
    runner.RegisterCallback(core.HookAfterAgentRun, "agent-participation",
        func(ctx context.Context, args core.CallbackArgs) (core.State, error) {
            participationState := args.State.Clone()
            participationState.SetMeta(fmt.Sprintf("%s_participated", args.AgentID), "true")
            participationState.SetMeta(fmt.Sprintf("%s_duration", args.AgentID), args.AgentResult.Duration.String())
            
            return participationState, nil
        })
}

Best Practices

Configuration Management

Environment-specific configs: Use different TOML files for dev/staging/production
Version control: Track configuration changes alongside code
Validation: Always validate configurations before deployment
Documentation: Document the purpose and trade-offs of each orchestration choice

Performance Optimization

Resource allocation: Consider CPU and memory usage for each pattern
Timeout tuning: Set appropriate timeouts for each orchestration mode
Batch processing: Use collaborative patterns for batch operations
Caching: Implement state caching for expensive operations

Testing Strategies

Unit testing: Test individual agents in isolation
Integration testing: Test complete orchestration flows
Load testing: Verify performance under expected load
Chaos testing: Test failure scenarios and recovery

Debugging and Troubleshooting

Structured logging: Use consistent log formats across all agents
Correlation IDs: Track events through complex orchestrations
State inspection: Log state changes at key points
Performance profiling: Monitor resource usage and bottlenecks

Next Steps

Now that you understand orchestration patterns, explore:

Error Handling - Learn about comprehensive error management
State Management - Deep dive into state handling across patterns
Message Passing - Understand event flow in orchestrated systems
Agent Lifecycle - Learn how agents work within orchestration

Summary

Orchestration patterns in AgenticGoKit provide powerful ways to coordinate multiple agents:

Route: Simple, fast, direct agent targeting
Collaborative: Parallel processing with multiple perspectives
Sequential: Ordered pipeline processing
Loop: Iterative improvement and refinement
Mixed: Complex workflows combining collaborative and sequential phases

Choose the right pattern based on your use case, performance requirements, and complexity tolerance. Use configuration-driven orchestration to maintain flexibility and enable easy pattern switching without code changes.

Orchestration Patterns ​

Prerequisites ​

Learning Objectives ​

Configuration-Driven Orchestration ​

The agentflow.toml Configuration System ​

Benefits of Configuration-Based Approach ​

The Five Orchestration Patterns ​

1. Route Pattern (Default) ​

2. Collaborative Pattern ​

The Five Orchestration Patterns ​

1. Route Pattern (Default) ​

2. Collaborative Pattern ​

3. Sequential Pattern ​

4. Loop Pattern ​

5. Mixed Pattern ​

Pattern Selection Guide ​

Decision Matrix ​

Performance Characteristics ​

Quality Assurance Loop ​

Workflow Visualization ​

API Reference ​

CLI Configuration Options ​

Orchestration Modes ​

Orchestrator Interface ​

Advanced Configuration Options ​

OrchestrationConfig ​

RetryPolicy ​

Convenience Functions ​

Migration from Internal APIs ​

Performance Considerations ​

Next Steps ​

Troubleshooting ​

A ​

Environment-Specific Configurations ​

Dynamic Configuration Loading ​

Conditional Orchestration ​

Error Handling in Orchestration ​

Pattern-Specific Error Handling ​

Circuit Breaker Pattern ​

Monitoring and Observability ​

Orchestration Metrics ​

Distributed Tracing ​

Best Practices and Guidelines ​

1. Pattern Selection Guidelines ​

2. Performance Optimization ​

3. Testing Orchestration Patterns ​

Troubleshooting Common Issues ​

1. Orchestration Not Working ​

2. Performance Issues ​

3. Agent Coordination Issues ​

Next Steps ​

Summary ​

4. Loop Pattern ​

5. Mixed Pattern ​

Decision Matrix ​

Performance Characteristics ​

Selection Guidelines ​

Advanced Orchestration Patterns ​

Dynamic Pattern Switching ​

Conditional Orchestration ​

Nested Orchestration ​

Error Handling in Orchestration ​

Orchestration-Specific Error Patterns ​

Monitoring and Observability ​

Orchestration Metrics ​

Best Practices ​

Configuration Management ​

Performance Optimization ​

Testing Strategies ​

Debugging and Troubleshooting ​

Next Steps ​

Summary ​

Orchestration Patterns

Prerequisites

Learning Objectives

Configuration-Driven Orchestration

The agentflow.toml Configuration System

Benefits of Configuration-Based Approach

The Five Orchestration Patterns

1. Route Pattern (Default)

2. Collaborative Pattern

The Five Orchestration Patterns

1. Route Pattern (Default)

2. Collaborative Pattern

3. Sequential Pattern

4. Loop Pattern

5. Mixed Pattern

Pattern Selection Guide

Decision Matrix

Performance Characteristics

Quality Assurance Loop

Workflow Visualization

API Reference

CLI Configuration Options

Orchestration Modes

Orchestrator Interface

Advanced Configuration Options

OrchestrationConfig

RetryPolicy

Convenience Functions

Migration from Internal APIs

Performance Considerations

Next Steps

Troubleshooting

A

Environment-Specific Configurations

Dynamic Configuration Loading

Conditional Orchestration

Error Handling in Orchestration

Pattern-Specific Error Handling

Circuit Breaker Pattern

Monitoring and Observability

Orchestration Metrics

Distributed Tracing

Best Practices and Guidelines

1. Pattern Selection Guidelines

2. Performance Optimization

3. Testing Orchestration Patterns

Troubleshooting Common Issues

1. Orchestration Not Working

2. Performance Issues

3. Agent Coordination Issues

Next Steps

Summary

4. Loop Pattern

5. Mixed Pattern

Decision Matrix

Performance Characteristics

Selection Guidelines

Advanced Orchestration Patterns

Dynamic Pattern Switching

Conditional Orchestration

Nested Orchestration

Error Handling in Orchestration

Orchestration-Specific Error Patterns

Monitoring and Observability

Orchestration Metrics

Best Practices

Configuration Management

Performance Optimization

Testing Strategies

Debugging and Troubleshooting

Next Steps

Summary