Advanced Multi-Step Task Orchestration with Claude Code

When building complex AI-powered applications with Claude Code, you’ll often need to orchestrate sophisticated multi-step workflows that involve dependencies, conditional logic, and dynamic adaptation. This guide covers advanced patterns for implementing robust task orchestration systems.

Table of Contents

• Task Dependency Management with DAGs
• Topological Sorting in Prompt Chains
• Dynamic Task Planning
• State Management for Long-Running Tasks
• Error Handling and Recovery
• Production Patterns and Examples
• Integration with Claude Code Features

Task Dependency Management with DAGs

Directed Acyclic Graphs (DAGs) provide a powerful model for representing task dependencies in complex workflows. In Claude Code, we can implement DAG-based orchestration to ensure tasks execute in the correct order while maximizing parallelization opportunities.

Core Concepts

A DAG-based task orchestration system consists of:

1. Nodes: Individual tasks or prompts to be executed
2. Edges: Dependencies between tasks
3. Execution Engine: Manages task scheduling and execution
4. State Manager: Tracks task status and manages intermediate results

Implementation Pattern

interface TaskNode {
  id: string
  name: string
  prompt: string
  dependencies: string[]
  status: 'pending' | 'running' | 'completed' | 'failed'
  result?: any
  retryCount?: number
}
 
interface WorkflowDAG {
  nodes: Map<string, TaskNode>
  edges: Map<string, Set<string>>
}
 
class TaskOrchestrator {
  private dag: WorkflowDAG
  private claudeClient: ClaudeClient
  
  constructor(dag: WorkflowDAG, claudeClient: ClaudeClient) {
    this.dag = dag
    this.claudeClient = claudeClient
  }
  
  async execute(): Promise<Map<string, any>> {
    const results = new Map<string, any>()
    const inProgress = new Set<string>()
    
    while (this.hasUncompletedTasks()) {
      const readyTasks = this.getReadyTasks(inProgress)
      
      // Execute ready tasks in parallel
      const promises = readyTasks.map(task => 
        this.executeTask(task, results)
      )
      
      await Promise.allSettled(promises)
    }
    
    return results
  }
  
  private getReadyTasks(inProgress: Set<string>): TaskNode[] {
    return Array.from(this.dag.nodes.values()).filter(node => {
      if (node.status !== 'pending') return false
      if (inProgress.has(node.id)) return false
      
      // Check if all dependencies are completed
      return node.dependencies.every(depId => {
        const dep = this.dag.nodes.get(depId)
        return dep?.status === 'completed'
      })
    })
  }
  
  private async executeTask(task: TaskNode, results: Map<string, any>): Promise<void> {
    task.status = 'running'
    
    try {
      // Build context from dependencies
      const context = this.buildContext(task, results)
      
      // Execute with Claude
      const result = await this.claudeClient.sendMessage({
        messages: [{
          role: 'user',
          content: this.interpolatePrompt(task.prompt, context)
        }]
      })
      
      task.status = 'completed'
      task.result = result
      results.set(task.id, result)
      
    } catch (error) {
      task.status = 'failed'
      task.retryCount = (task.retryCount || 0) + 1
      
      if (task.retryCount < 3) {
        // Reset for retry
        task.status = 'pending'
      }
      
      throw error
    }
  }
}

Best Practices for DAG Design

1. Keep Tasks Atomic: Each node should represent a single, well-defined operation
2. Minimize Dependencies: Reduce coupling between tasks for better parallelization
3. Use Caching: Store intermediate results to enable resumption and debugging
4. Implement Timeouts: Prevent indefinite waiting on stuck tasks

Topological Sorting in Prompt Chains

Topological sorting ensures tasks execute in an order that respects all dependencies. This is crucial for prompt chains where outputs from one prompt serve as inputs to others.

Kahn’s Algorithm Implementation

class TopologicalSorter {
  static sort(dag: WorkflowDAG): TaskNode[] | null {
    const nodes = Array.from(dag.nodes.values())
    const inDegree = new Map<string, number>()
    
    // Calculate in-degrees
    nodes.forEach(node => {
      inDegree.set(node.id, 0)
    })
    
    dag.edges.forEach((destinations, source) => {
      destinations.forEach(dest => {
        inDegree.set(dest, (inDegree.get(dest) || 0) + 1)
      })
    })
    
    // Find nodes with no dependencies
    const queue: TaskNode[] = []
    nodes.forEach(node => {
      if (inDegree.get(node.id) === 0) {
        queue.push(node)
      }
    })
    
    const sorted: TaskNode[] = []
    
    while (queue.length > 0) {
      const current = queue.shift()!
      sorted.push(current)
      
      // Process neighbors
      const neighbors = dag.edges.get(current.id) || new Set()
      neighbors.forEach(neighborId => {
        const degree = (inDegree.get(neighborId) || 0) - 1
        inDegree.set(neighborId, degree)
        
        if (degree === 0) {
          const neighbor = dag.nodes.get(neighborId)
          if (neighbor) queue.push(neighbor)
        }
      })
    }
    
    // Check for cycles
    return sorted.length === nodes.length ? sorted : null
  }
}

Prompt Chain Patterns

Sequential Chaining

Used when each step depends on the previous one:

const sequentialChain: TaskNode[] = [
  {
    id: 'analyze',
    name: 'Analyze Requirements',
    prompt: 'Analyze the following requirements and identify key components: {requirements}',
    dependencies: [],
    status: 'pending'
  },
  {
    id: 'design',
    name: 'Design Architecture',
    prompt: 'Based on the analysis: {analyze.result}, design a system architecture',
    dependencies: ['analyze'],
    status: 'pending'
  },
  {
    id: 'implement',
    name: 'Generate Implementation',
    prompt: 'Implement the following architecture: {design.result}',
    dependencies: ['design'],
    status: 'pending'
  }
]

Parallel Chaining

Execute independent tasks simultaneously:

const parallelChain: TaskNode[] = [
  {
    id: 'requirements',
    name: 'Gather Requirements',
    prompt: 'Extract functional requirements from: {input}',
    dependencies: [],
    status: 'pending'
  },
  {
    id: 'frontend_design',
    name: 'Design Frontend',
    prompt: 'Design frontend based on: {requirements.result}',
    dependencies: ['requirements'],
    status: 'pending'
  },
  {
    id: 'backend_design',
    name: 'Design Backend',
    prompt: 'Design backend based on: {requirements.result}',
    dependencies: ['requirements'],
    status: 'pending'
  },
  {
    id: 'integration',
    name: 'Design Integration',
    prompt: 'Integrate frontend ({frontend_design.result}) with backend ({backend_design.result})',
    dependencies: ['frontend_design', 'backend_design'],
    status: 'pending'
  }
]

Dynamic Task Planning

Dynamic task planning allows workflows to adapt based on intermediate results. This is essential for handling conditional logic and unknown task structures at design time.

Adaptive Workflow Pattern

interface DynamicTaskPlanner {
  evaluateConditions(context: Map<string, any>): TaskNode[]
  generateSubtasks(parentResult: any): TaskNode[]
  shouldContinue(results: Map<string, any>): boolean
}
 
class AdaptiveOrchestrator extends TaskOrchestrator {
  private planner: DynamicTaskPlanner
  
  async executeDynamic(): Promise<Map<string, any>> {
    const results = new Map<string, any>()
    
    while (this.planner.shouldContinue(results)) {
      // Generate next tasks based on current state
      const newTasks = this.planner.evaluateConditions(results)
      
      // Add to DAG
      newTasks.forEach(task => {
        this.dag.nodes.set(task.id, task)
        this.updateDependencies(task)
      })
      
      // Execute one iteration
      await this.executeIteration(results)
    }
    
    return results
  }
}

Claude-Powered Task Generation

Leverage Claude to dynamically generate task plans:

class ClaudeTaskPlanner implements DynamicTaskPlanner {
  async generateSubtasks(parentResult: any): Promise<TaskNode[]> {
    const planningPrompt = `
Given the following result from the previous task:
${JSON.stringify(parentResult, null, 2)}
 
Generate a task breakdown for the next steps. Return a JSON array of tasks with:
- id: unique identifier
- name: descriptive name
- prompt: the prompt to execute
- dependencies: array of task IDs this depends on
 
Focus on tasks that:
1. Build upon the previous result
2. Can be parallelized where possible
3. Lead toward the overall goal
`
 
    const response = await claudeClient.sendMessage({
      messages: [{ role: 'user', content: planningPrompt }]
    })
    
    return JSON.parse(response.content)
  }
}

State Management for Long-Running Tasks

Long-running workflows require robust state management to handle interruptions, resume from checkpoints, and maintain context across sessions.

Checkpoint System

interface WorkflowCheckpoint {
  id: string
  timestamp: Date
  dag: WorkflowDAG
  results: Map<string, any>
  metadata: {
    totalTasks: number
    completedTasks: number
    failedTasks: number
  }
}
 
class CheckpointManager {
  private storage: CheckpointStorage
  
  async saveCheckpoint(orchestrator: TaskOrchestrator): Promise<string> {
    const checkpoint: WorkflowCheckpoint = {
      id: generateId(),
      timestamp: new Date(),
      dag: orchestrator.getDag(),
      results: orchestrator.getResults(),
      metadata: orchestrator.getMetadata()
    }
    
    await this.storage.save(checkpoint)
    return checkpoint.id
  }
  
  async resumeFromCheckpoint(checkpointId: string): Promise<TaskOrchestrator> {
    const checkpoint = await this.storage.load(checkpointId)
    return TaskOrchestrator.fromCheckpoint(checkpoint)
  }
}

Context Window Management

For workflows that exceed Claude’s context window:

class ContextManager {
  private maxTokens = 100000 // Claude's context limit
  
  async pruneContext(
    fullContext: string, 
    priority: 'recency' | 'relevance'
  ): Promise<string> {
    if (this.estimateTokens(fullContext) <= this.maxTokens) {
      return fullContext
    }
    
    if (priority === 'recency') {
      return this.keepRecentContext(fullContext)
    } else {
      return this.keepRelevantContext(fullContext)
    }
  }
  
  private async keepRelevantContext(fullContext: string): Promise<string> {
    // Use embeddings to identify most relevant portions
    const chunks = this.chunkContext(fullContext)
    const embeddings = await this.generateEmbeddings(chunks)
    const scores = await this.scoreRelevance(embeddings)
    
    // Keep highest scoring chunks within token limit
    return this.selectTopChunks(chunks, scores, this.maxTokens)
  }
}

Error Handling and Recovery

Robust error handling is crucial for production workflows. Implement multiple layers of resilience:

Retry Strategies

interface RetryStrategy {
  shouldRetry(error: Error, attempt: number): boolean
  getDelay(attempt: number): number
}
 
class ExponentialBackoffStrategy implements RetryStrategy {
  constructor(
    private maxAttempts: number = 3,
    private baseDelay: number = 1000
  ) {}
  
  shouldRetry(error: Error, attempt: number): boolean {
    // Retry on rate limits and transient errors
    const retryableErrors = ['rate_limit', 'timeout', 'network_error']
    return attempt < this.maxAttempts && 
           retryableErrors.includes(error.name)
  }
  
  getDelay(attempt: number): number {
    return this.baseDelay * Math.pow(2, attempt) + 
           Math.random() * 1000 // Add jitter
  }
}

Cascading Failure Management

class FailureHandler {
  async handleTaskFailure(
    task: TaskNode, 
    error: Error,
    dag: WorkflowDAG
  ): Promise<void> {
    // Mark dependent tasks as blocked
    const dependents = this.findDependentTasks(task.id, dag)
    dependents.forEach(dep => {
      dep.status = 'blocked'
      dep.blockReason = `Dependency ${task.id} failed: ${error.message}`
    })
    
    // Generate recovery plan
    const recoveryPlan = await this.generateRecoveryPlan(task, error)
    
    if (recoveryPlan.canRecover) {
      // Insert recovery tasks
      recoveryPlan.tasks.forEach(recoveryTask => {
        dag.nodes.set(recoveryTask.id, recoveryTask)
      })
    } else {
      // Initiate graceful shutdown
      await this.gracefulShutdown(dag)
    }
  }
}

Production Patterns and Examples

Multi-Agent Code Review Workflow

A real-world example implementing a comprehensive code review process:

const codeReviewWorkflow: WorkflowDAG = {
  nodes: new Map([
    ['parse_pr', {
      id: 'parse_pr',
      name: 'Parse Pull Request',
      prompt: 'Extract changed files and context from PR: {pr_url}',
      dependencies: [],
      status: 'pending'
    }],
    ['security_review', {
      id: 'security_review',
      name: 'Security Analysis',
      prompt: 'Analyze security implications of changes: {parse_pr.result}',
      dependencies: ['parse_pr'],
      status: 'pending'
    }],
    ['performance_review', {
      id: 'performance_review',
      name: 'Performance Analysis',
      prompt: 'Analyze performance impact: {parse_pr.result}',
      dependencies: ['parse_pr'],
      status: 'pending'
    }],
    ['style_review', {
      id: 'style_review',
      name: 'Code Style Check',
      prompt: 'Check code style and conventions: {parse_pr.result}',
      dependencies: ['parse_pr'],
      status: 'pending'
    }],
    ['synthesize', {
      id: 'synthesize',
      name: 'Synthesize Feedback',
      prompt: `Combine all review feedback:
        Security: {security_review.result}
        Performance: {performance_review.result}
        Style: {style_review.result}
        Generate actionable recommendations`,
      dependencies: ['security_review', 'performance_review', 'style_review'],
      status: 'pending'
    }]
  ]),
  edges: new Map([
    ['parse_pr', new Set(['security_review', 'performance_review', 'style_review'])],
    ['security_review', new Set(['synthesize'])],
    ['performance_review', new Set(['synthesize'])],
    ['style_review', new Set(['synthesize'])]
  ])
}

Document Processing Pipeline

Complex document analysis with conditional paths:

class DocumentProcessor {
  async processDocument(documentPath: string): Promise<any> {
    const workflow = new AdaptiveOrchestrator(
      this.createInitialDAG(),
      this.claudeClient
    )
    
    // Dynamic planning based on document type
    workflow.setPlanner({
      evaluateConditions: async (results) => {
        const docType = results.get('classify_document')?.type
        
        switch (docType) {
          case 'technical':
            return this.generateTechnicalTasks()
          case 'legal':
            return this.generateLegalTasks()
          case 'financial':
            return this.generateFinancialTasks()
          default:
            return this.generateGenericTasks()
        }
      }
    })
    
    return await workflow.executeDynamic()
  }
}

Integration with Claude Code Features

Leveraging Claude Code Hooks

Integrate task orchestration with Claude Code’s hook system:

// hooks/task-orchestration.ts
export const onBeforeTaskExecute = async (task: TaskNode) => {
  console.log(`Starting task: ${task.name}`)
  await telemetry.trackTaskStart(task)
}
 
export const onAfterTaskComplete = async (task: TaskNode, result: any) => {
  console.log(`Completed task: ${task.name}`)
  await telemetry.trackTaskComplete(task, result)
  
  // Trigger dependent workflows
  if (task.id === 'critical_task') {
    await triggerDownstreamWorkflows(result)
  }
}
 
export const onTaskError = async (task: TaskNode, error: Error) => {
  console.error(`Task failed: ${task.name}`, error)
  await alerting.notifyTaskFailure(task, error)
}

Memory File Integration

Store workflow state in Claude Code memory files:

// CLAUDE.md additions for workflow context
const workflowMemory = `
## Active Workflows
 
### Code Review Pipeline
- Status: In Progress
- Current Task: performance_review
- Started: 2025-01-24T10:30:00Z
- Checkpoint: checkpoint_abc123
 
### Data Processing Pipeline  
- Status: Completed
- Total Tasks: 15
- Duration: 2h 34m
- Results: stored in /results/pipeline_xyz789.json
`

Subagent Coordination

Use Claude Code’s subagent pattern for specialized tasks:

class SubagentOrchestrator {
  async delegateToSpecialist(task: TaskNode): Promise<any> {
    const specialist = this.selectSpecialist(task)
    
    return await this.claudeClient.sendMessage({
      messages: [{
        role: 'system',
        content: `You are a ${specialist.role} specialist. ${specialist.expertise}`
      }, {
        role: 'user',
        content: task.prompt
      }],
      model: specialist.preferredModel
    })
  }
  
  private selectSpecialist(task: TaskNode): Specialist {
    // Route to appropriate specialist based on task type
    const specialists = {
      'code_generation': {
        role: 'Senior Developer',
        expertise: 'Expert in TypeScript, React, and system design',
        preferredModel: 'claude-3-opus'
      },
      'security_analysis': {
        role: 'Security Analyst',
        expertise: 'Expert in OWASP, secure coding, threat modeling',
        preferredModel: 'claude-3-opus'
      },
      'performance_optimization': {
        role: 'Performance Engineer',
        expertise: 'Expert in profiling, optimization, scalability',
        preferredModel: 'claude-3-sonnet'
      }
    }
    
    return specialists[task.type] || specialists['code_generation']
  }
}

Best Practices Summary

1. Design for Failure: Assume tasks will fail and plan recovery strategies
2. Minimize Context: Keep prompts focused to stay within token limits
3. Enable Observability: Log extensively for debugging complex workflows
4. Test Incrementally: Validate each task in isolation before orchestrating
5. Version Control Prompts: Track prompt changes for reproducibility
6. Monitor Costs: Track token usage and optimize expensive operations
7. Document Dependencies: Make task relationships explicit and visible

Related Resources

• Subagent Orchestration Deep Dive
• Testing Multi-Step Workflows
• Monitoring Task Execution
• Debugging Complex Workflows
• Using Hooks for Workflow Events

Next Steps

1. Explore Advanced State Management Techniques
2. Learn about Memory and Context Patterns
3. Implement Advanced Error Recovery Strategies

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This guide represents current best practices as of January 2025. As Claude Code and the broader AI landscape evolve, these patterns will be updated to reflect new capabilities and approaches.