ai-agent-orchestration

name	ai-agent-orchestration
description	Orchestrate and coordinate multiple AI agents for complex workflows. Use when managing agent interactions, coordinating multi-agent tasks, or implementing agent communication patterns.
argument-hint	[action] [agentType] [workflowType] [parameters]
context	fork
agent	Plan
disable-model-invocation	false
user-invocable	true
allowed-tools	["Bash","Read","Write","Grep"]

AI Agent Orchestration Skill

Advanced AI agent orchestration using isolated subagent execution for complex multi-agent workflows. This skill coordinates multiple specialized agents to handle sophisticated tasks through intelligent collaboration.

Usage

/ai-agent-orchestration orchestrate compliance-audit production --priority=high
/ai-agent-orchestration coordinate security-analysis cost-optimization --target=all-resources
/ai-agent-orchestration deploy-agent compliance-check --config=compliance-config.yaml
/ai-agent-orchestration monitor-agents --status=detailed

Subagent Architecture

This skill uses context: fork with agent: Plan to create an isolated orchestration environment optimized for:

• Agent Coordination: Intelligent agent selection and task distribution
• Workflow Management: Complex multi-agent workflow execution
• Resource Allocation: Dynamic resource management for agent execution
• Conflict Resolution: Handling competing agent priorities and dependencies

Agent Types

Compliance Agent

name: compliance-check
capabilities:
  - SOC2 validation
  - GDPR compliance
  - HIPAA verification
  - Policy enforcement
triggers:
  - audit requests
  - configuration changes
  - scheduled scans

Security Agent

name: security-analysis
capabilities:
  - Vulnerability scanning
  - Threat detection
  - Security policy validation
  - Incident response
triggers:
  - security events
  - code changes
  - threat intelligence

Cost Agent

name: cost-optimization
capabilities:
  - Resource optimization
  - Spending analysis
  - Budget monitoring
  - Cost forecasting
triggers:
  - cost anomalies
  - resource changes
  - budget alerts

Infrastructure Agent

name: infrastructure-discovery
capabilities:
  - Resource discovery
  - Topology mapping
  - Configuration analysis
  - Dependency tracking
triggers:
  - infrastructure changes
  - discovery requests
  - monitoring alerts

Orchestration Patterns

Sequential Agent Execution

# Execute agents in sequence with dependency management
/ai-agent-orchestration orchestrate sequential \
  --agents=infrastructure-discovery,compliance-check,security-analysis \
  --target=production-cluster \
  --fail-fast=true

Parallel Agent Execution

# Execute multiple agents simultaneously
/ai-agent-orchestration orchestrate parallel \
  --agents=compliance-check,security-analysis,cost-optimization \
  --target=all-resources \
  --timeout=3600

Conditional Agent Workflows

# Conditional agent execution based on previous results
/ai-agent-orchestration orchestrate conditional \
  --workflow="if compliance-check.passed then security-analysis else remediation"
  --target=new-service

Agent Coordination with Communication

# Agents that communicate and share context
/ai-agent-orchestration orchestrate collaborative \
  --agents=compliance-check,cost-optimization \
  --communication-channel=shared-context \
  --decision-consensus=true

Agent Communication Protocols

Message Passing

communication:
  type: message-passing
  channels:
    - agent-events
    - shared-context
    - decision-coordination
  protocols:
    - async-messages
    - request-response
    - broadcast

Shared Memory

communication:
  type: shared-memory
  context-store:
    type: redis
    endpoint: localhost:6379
  data-types:
    - agent-results
    - workflow-state
    - coordination-metadata

Event-Driven Coordination

communication:
  type: event-driven
  events:
    - agent-completed
    - agent-failed
    - workflow-milestone
  handlers:
    - on-agent-completed: schedule-next-agent
    - on-agent-failed: initiate-fallback

Workflow Templates

Compliance Audit Workflow

name: compliance-audit
agents:
  - name: infrastructure-discovery
    action: discover-resources
    output: resource-inventory
  - name: compliance-check
    action: validate-compliance
    input: resource-inventory
    output: compliance-report
  - name: security-analysis
    action: security-scan
    input: resource-inventory
    output: security-report
  - name: remediation-agent
    action: generate-remediation
    input: [compliance-report, security-report]
    output: remediation-plan
coordination:
  type: sequential
  error-handling: continue-on-failure

Cost Optimization Workflow

name: cost-optimization-cycle
agents:
  - name: infrastructure-discovery
    action: map-resources
  - name: cost-optimization
    action: analyze-costs
  - name: compliance-check
    action: validate-changes
    action-condition: cost-optimization.has-recommendations
coordination:
  type: conditional
  retry-policy:
    max-attempts: 3
    backoff: exponential

Multi-Agent Security Response

name: security-incident-response
agents:
  - name: security-analysis
    action: investigate-incident
    priority: critical
  - name: compliance-check
    action: assess-compliance-impact
    parallel: true
  - name: infrastructure-discovery
    action: map-affected-resources
    parallel: true
  - name: remediation-agent
    action: execute-remediation
    depends-on: [security-analysis, compliance-check, infrastructure-discovery]
coordination:
  type: parallel-then-sequential
  timeout: 1800

Agent Deployment

Agent Configuration

agent:
  name: compliance-check
  version: 1.0.0
  runtime:
    type: temporal-workflow
    workflow-id: compliance-check-workflow
    task-queue: compliance-queue
  resources:
    memory: 512Mi
    cpu: 500m
    timeout: 3600s
  environment:
    LOG_LEVEL: info
    COMPLIANCE_FRAMEWORK: SOC2

Dynamic Agent Registration

# Register new agent type
/ai-agent-orchestration register-agent \
  --name=new-agent \
  --type=workflow \
  --definition=agent-def.yaml \
  --capabilities=capability-list

# Update agent configuration
/ai-agent-orchestration update-agent \
  --name=compliance-check \
  --config=new-config.yaml \
  --restart=true

Agent Health Monitoring

# Check agent health
/ai-agent-orchestration health-check --agent=all

# Monitor agent performance
/ai-agent-orchestration monitor --agent=compliance-check --metrics=detailed

# Agent lifecycle management
/ai-agent-orchestration restart-agent security-analysis --graceful=true

Integration Points

Temporal Workflow Integration

// Agent orchestration workflow
func AgentOrchestrationWorkflow(ctx workflow.Context, input OrchestrationInput) error {
    // Initialize agents
    agents := []Agent{
        NewComplianceAgent(),
        NewSecurityAgent(),
        NewCostAgent(),
    }
    
    // Execute orchestration pattern
    switch input.Pattern {
    case "sequential":
        return executeSequential(ctx, agents, input.Target)
    case "parallel":
        return executeParallel(ctx, agents, input.Target)
    case "conditional":
        return executeConditional(ctx, agents, input.Target)
    }
    
    return nil
}

Backend API Integration

# Start orchestration
curl -X POST http://localhost:8081/api/v1/orchestration/start \
  -H "Content-Type: application/json" \
  -d '{
    "agents": ["compliance-check", "security-analysis"],
    "pattern": "parallel",
    "target": "production-cluster"
  }'

# Get orchestration status
curl http://localhost:8081/api/v1/orchestration/{orchestration-id}/status

Performance Optimization

Agent Pool Management

agent-pools:
  compliance-check:
    size: 3
    scaling: auto
    min-size: 1
    max-size: 10
  security-analysis:
    size: 2
    scaling: on-demand
    min-size: 1
    max-size: 5

Resource Optimization

# Optimize agent resource allocation
/ai-agent-orchestration optimize-resources --agent=all

# Balance agent load
/ai-agent-orchestration balance-load --strategy=round-robin

# Scale agents based on demand
/ai-agent-orchestration auto-scale --metric=queue-depth

Error Handling and Recovery

Agent Failure Handling

error-handling:
  strategy: retry-with-fallback
  retry-policy:
    max-attempts: 3
    backoff: exponential
  fallback-agents:
    compliance-check: compliance-check-lite
    security-analysis: security-scan-basic
  escalation:
    threshold: 2-failures
    action: human-intervention

Workflow Recovery

# Resume failed orchestration
/ai-agent-orchestration resume --orchestration-id=12345

# Restart from failed agent
/ai-agent-orchestration restart-agent --orchestration-id=12345 --agent=security-analysis

# Manual intervention
/ai-agent-orchestration manual-override --orchestration-id=12345 --action=skip-agent

Monitoring and Observability

Agent Metrics

# Agent performance metrics
/ai-agent-orchestration metrics --agent=all --format=prometheus

# Orchestration success rates
/ai-agent-orchestration analytics --metric=success-rate --timeframe=24h

# Agent resource usage
/ai-agent-orchestration resource-usage --agent=compliance-check --detailed

Dashboard Integration

dashboard:
  agent-status:
    - agent: compliance-check
      endpoint: /metrics/compliance
    - agent: security-analysis
      endpoint: /metrics/security
  orchestration-overview:
    endpoint: /api/v1/orchestration/status
    refresh: 5s

Best Practices

1. Agent Specialization: Keep agent responsibilities focused and single-purpose
2. Clear Interfaces: Define well-structured agent communication protocols
3. Fault Tolerance: Implement robust error handling and recovery mechanisms
4. Resource Management: Monitor and optimize agent resource usage
5. Observability: Implement comprehensive monitoring and logging
6. Testing: Test agent interactions and orchestration patterns thoroughly

Related Skills

• /workflow-management: Orchestrate individual workflows
• /compliance-check: Compliance validation agent
• /security-analysis: Security scanning agent
• /cost-optimization: Cost optimization agent
• /infrastructure-discovery: Resource discovery agent

File Locations

• Agent Definitions: backend/agents/
• Orchestration Logic: backend/orchestration/
• Agent Configurations: config/agents/
• Monitoring: monitoring/agents/
• Tests: tests/orchestration/

OpenAI Codex Integration

This section documents the OpenAI Codex-style AI agent orchestration that has been integrated into the Claude skills framework.

Basic Agent Orchestration Principles

When orchestrating AI agents, follow these principles:

1. Agent Design

• Define clear agent responsibilities and boundaries
• Design agent interfaces and communication protocols
• Implement proper agent state management
• Handle agent lifecycle events

2. Workflow Coordination

• Create agent workflow definitions
• Define agent execution sequences
• Handle agent dependencies and prerequisites
• Implement agent failover and recovery

3. Communication Patterns

• Design agent-to-agent messaging
• Implement event-driven communication
• Handle asynchronous agent interactions
• Manage agent conversation context

4. Resource Management

• Allocate agent resources appropriately
• Monitor agent performance and health
• Handle agent scaling and load balancing
• Manage agent configuration and secrets

Agent Types in This Sandbox

• Compliance Agents: Validate policies and regulations
• Security Agents: Perform security analysis and monitoring
• Cost Agents: Optimize resource usage and spending
• Infrastructure Agents: Manage and discover infrastructure
• Workflow Agents: Coordinate and execute workflows

Implementation Patterns

// Agent workflow example
func AgentWorkflow(ctx workflow.Context, input AgentInput) error {
    // Initialize agent
    agent := NewAgent(input.Type)
    
    // Execute agent logic
    result, err := workflow.ExecuteActivity(ctx, agent.Run, input.Params)
    if err != nil {
        return err
    }
    
    // Handle agent output
    return workflow.ExecuteActivity(ctx, ProcessAgentResult, result.Value)
}

Configuration Files

• Agent definitions: backend/agents/
• Workflow configurations: backend/workflows/
• Skill definitions: .agents/skills/ and .claude/skills/

Monitoring and Debugging

• Track agent execution metrics
• Monitor agent communication patterns
• Log agent decision points
• Debug agent failures with detailed logs

Best Practices

• Keep agent responsibilities focused and single-purpose
• Implement proper error handling and retries
• Design for agent isolation and fault tolerance
• Use structured logging for agent activities
• Test agent interactions thoroughly