Orchestration Coordination Framework

Purpose

Production-scale AI development requires sophisticated orchestration and coordination. This Skill provides comprehensive orchestration capabilities for:

1. Multi-Agent Coordination - Coordinate multiple AI agents working on complex tasks
2. Task Decomposition - Break down complex objectives into manageable subtasks
3. Workflow Orchestration - DAGs, state machines, event-driven patterns
4. Communication Protocols - Agent-to-agent messaging, pub/sub, queues
5. Error Handling - Retry logic, circuit breakers, fallback strategies
6. Resource Management - Load balancing, rate limiting, concurrency control
7. Observability - Monitoring, logging, tracing, metrics for distributed systems
8. Framework Integration - Apache Airflow, Temporal, Prefect, Celery, Step Functions

When to Use This Skill

Use orchestration coordination for:

• Orchestrating multiple agents or services working together
• Complex multi-step workflows requiring coordination
• Distributed task execution with dependencies
• Event-driven architectures and reactive systems
• Building CI/CD pipelines with complex dependencies
• Microservices coordination and saga patterns
• Data pipeline orchestration (ETL/ELT)
• Long-running workflows with state management
• Fault-tolerant distributed systems
• Resource allocation across multiple workers
• Implementing retries and error recovery strategies
• Monitoring and observability for distributed systems

Quick Start

Basic Multi-Agent Orchestration

Here's a minimal example:

import asyncio
from typing import List, Dict, Any

class SimpleOrchestrator:
    def __init__(self):
        self.agents = {}

    def register_agent(self, agent_id: str, agent_type: str):
        self.agents[agent_id] = {"type": agent_type, "busy": False}

    async def execute_task(self, agent_id: str, task: Dict[str, Any]) -> Any:
        print(f"Agent {agent_id} executing: {task['name']}")
        await asyncio.sleep(1)
        return {"status": "completed", "result": f"Result of {task['name']}"}

    async def orchestrate(self, tasks: List[Dict[str, Any]]) -> List[Any]:
        results = await asyncio.gather(*[
            self.execute_task(f"agent_{i}", task)
            for i, task in enumerate(tasks)
        ])
        return results

# Usage
async def main():
    orchestrator = SimpleOrchestrator()
    orchestrator.register_agent("agent_0", "analyzer")
    orchestrator.register_agent("agent_1", "security")

    tasks = [
        {"name": "analyze_code", "type": "analysis"},
        {"name": "scan_security", "type": "security"}
    ]

    results = await orchestrator.orchestrate(tasks)
    print(f"Results: {results}")

asyncio.run(main())

Core Orchestration Concepts

1. DAG (Directed Acyclic Graph) Pattern

Tasks execute based on dependencies, enabling parallel execution where possible.

Start
  │
  ├──► Task A ──┐
  │            │
  └──► Task B ──┼──► Task D ──► Task F ──► End
       │        │
       └──► Task C ──► Task E ───┘

Use when: You have tasks with clear dependencies and want parallel execution.

2. State Machine Pattern

Workflows transition through defined states with explicit transitions.

     ┌─────────┐
     │  IDLE   │
     └────┬────┘
          │ start()
     ┌────▼────┐
     │ RUNNING │◄──────┐
     └────┬────┘       │
          │            │ retry()
    ┌─────┴─────┐      │
    │           │      │
┌───▼───┐   ┌───▼───┐  │
│SUCCESS│   │FAILURE├──┘
└───────┘   └───┬───┘
                │ max_retries
            ┌───▼───┐
            │ ERROR │
            └───────┘

Use when: You need explicit state tracking and complex transition logic.

3. Event-Driven Pattern

Agents communicate via events and messages (pub/sub, message queues).

┌──────────┐      ┌─────────────┐      ┌──────────┐
│ Agent A  │─────►│  Event Bus  │─────►│ Agent B  │
└──────────┘      └─────────────┘      └──────────┘
                        │
                        ▼
                  ┌──────────┐
                  │ Agent C  │
                  └──────────┘

Use when: Agents need loose coupling and async communication.

4. Orchestrator Architecture

┌─────────────────────────────────────────────────────┐
│                  Orchestrator                       │
│  ┌─────────────┐  ┌──────────────┐  ┌───────────┐ │
│  │ Task Queue  │  │ State Manager│  │ Scheduler │ │
│  └─────────────┘  └──────────────┘  └───────────┘ │
└──────────────┬──────────────────────────────────────┘
               │
       ┌───────┴───────┐
       │               │
   ┌───▼───┐       ┌───▼───┐       ┌────────┐
   │Agent 1│       │Agent 2│       │Agent N │
   │       │       │       │       │        │
   │Task A │       │Task B │       │Task N  │
   └───┬───┘       └───┬───┘       └───┬────┘
       │               │               │
       └───────┬───────┴───────────────┘
               │
         ┌─────▼─────┐
         │  Results  │
         │Aggregator │
         └───────────┘

Critical Gotchas

1. Retry Storms

Problem: Failed tasks retry simultaneously, overwhelming systems. Solution: Use exponential backoff with jitter, circuit breakers, and max retry limits. See GOTCHAS.md for details.

2. State Management Complexity

Problem: Losing track of workflow state across restarts. Solution: Use durable execution platforms (Temporal), persist state externally, make tasks idempotent. See GOTCHAS.md for details.

3. Serialization Issues

Problem: Large data passed between tasks causes memory issues. Solution: Pass references (S3 URLs) not data, use streaming, implement chunking. See GOTCHAS.md for details.

For all common gotchas and solutions, see GOTCHAS.md.

Orchestration Patterns

This framework provides 7 production-ready patterns:

1. Multi-Agent Task Decomposition - Break complex objectives into subtasks
2. Airflow DAG Coordination - Orchestrate agents with Apache Airflow
3. Temporal Durable Execution - Long-running workflows with automatic retries
4. Event-Driven Coordination - Pub/Sub messaging for loose coupling
5. Circuit Breaker Pattern - Prevent cascading failures
6. Resource Pool with Load Balancing - Manage agent resources efficiently
7. Distributed Tracing - Monitor and debug distributed workflows

See PATTERNS.md for detailed pattern descriptions and when to use each.

Working Examples

All patterns include complete, runnable code examples:

• Multi-Agent Orchestrator - Coordinate specialized agents with dependency resolution
• Airflow DAG - Production DAG for agent coordination with XCom
• Temporal Workflow - Durable workflow with activities and retry policies
• Event-Driven System - Redis-based pub/sub for agent communication
• Circuit Breaker - Fault-tolerant agent calls with state management
• Resource Pool - Load-balanced agent pool with multiple strategies
• Distributed Tracing - OpenTelemetry-style tracing for workflows

See EXAMPLES.md for complete code examples you can copy and adapt.

Framework Selection

Choose the right orchestration framework for your needs:

See KNOWLEDGE.md for detailed framework comparisons, concepts, and learning resources.

Best Practices

DO's

1. Design for Idempotency - Tasks should be safely retryable without side effects
2. Use Correlation IDs - Track requests across distributed systems
3. Implement Timeouts - Every operation should have a timeout
4. Monitor Everything - Metrics, logs, traces for all components
5. Implement Circuit Breakers - Prevent cascading failures
6. Use Exponential Backoff - Space out retries to avoid thundering herd
7. Validate DAGs - Check for cycles before execution
8. Version Workflows - Track workflow changes over time

DON'Ts

1. Don't Pass Large Data - Use references (S3 URLs, database IDs) instead
2. Don't Ignore Partial Failures - Handle them explicitly
3. Don't Use Distributed Transactions - Use saga pattern instead
4. Don't Synchronous Chain - Use async/parallel execution where possible
5. Don't Skip Health Checks - Monitor agent health continuously
6. Don't Hardcode Timeouts - Make them configurable per task type
7. Don't Trust Clocks - Use logical ordering, not wall clock time
8. Don't Forget Cleanup - Clean up zombie workflows and stale state

See REFERENCE.md for extended best practices guide.

Production Deployment

Essential Checklist

Before deploying orchestrated workflows to production:

• Define clear task boundaries and responsibilities
• Implement comprehensive error handling and retries
• Set up distributed tracing (OpenTelemetry, Jaeger)
• Configure monitoring and alerting (Prometheus, Grafana)
• Implement circuit breakers for external dependencies
• Use message queues for async communication (RabbitMQ, Kafka)
• Set up health checks for all agents
• Implement graceful shutdown handling
• Configure resource limits and quotas
• Set up log aggregation (ELK, Loki)

See REFERENCE.md for complete deployment guide.

Documentation Structure

This skill is organized for progressive disclosure:

• SKILL.md (this file) - Quick start, core concepts, overview
• KNOWLEDGE.md - Frameworks, theory, distributed systems concepts
• PATTERNS.md - Implementation patterns and when to use them
• EXAMPLES.md - Complete working code examples
• GOTCHAS.md - Common pitfalls and troubleshooting
• REFERENCE.md - API docs, configuration, production deployment

Related Skills

• multi-agent-coordination-framework - For advanced agent architectures
• mcp-integration-toolkit - For agent communication via MCP protocol
• git-mastery-suite - For orchestrating Git-based workflows
• security-scanning-suite - For security orchestration and SAST/DAST coordination

Learning Path

1. Start Here: Read this SKILL.md for quick start and core concepts
2. Choose Framework: Review KNOWLEDGE.md to select your orchestration framework
3. Pick Pattern: Browse PATTERNS.md to find the pattern matching your use case
4. Copy Code: Use EXAMPLES.md to get working code
5. Avoid Pitfalls: Check GOTCHAS.md for common mistakes
6. Go to Production: Follow REFERENCE.md deployment guide

Quick Reference

Common Operations

# Register agents
orchestrator.register_agent(agent_id, agent_type, capacity)

# Submit task
result = await orchestrator.execute_task(task)

# Execute workflow
results = await orchestrator.execute_workflow(tasks)

# Check circuit breaker status
status = circuit_breaker.get_state()

# Monitor resource pool
status = resource_pool.get_pool_status()

Key Metrics to Monitor

• Task queue depth
• Agent utilization (%)
• Task success rate (%)
• Average task duration
• Circuit breaker state
• Error rate by agent type

See REFERENCE.md for complete monitoring guide.