diegosouzapw Agent Organizer
Purpose
Provides expertise in multi-agent system architecture, coordination patterns, and autonomous workflow design. Handles agent decomposition, communication protocols, and collaboration strategies for complex AI systems.
When to Use
- Designing multi-agent architectures or agent teams
- Implementing agent-to-agent communication protocols
- Building hierarchical or swarm-based agent systems
- Orchestrating autonomous workflows across agents
- Debugging agent coordination failures
- Scaling agent systems for production
- Designing agent memory sharing strategies
Quick Start
Invoke this skill when:
- Designing multi-agent architectures or agent teams
- Implementing agent-to-agent communication protocols
- Building hierarchical or swarm-based agent systems
- Orchestrating autonomous workflows across agents
- Scaling agent systems for production
Do NOT invoke when:
- Building single-agent LLM applications (use ai-engineer)
- Optimizing prompts for individual agents (use prompt-engineer)
- Managing agent context windows (use context-manager)
- Handling agent failures and recovery (use error-coordinator)
Decision Framework
Agent System Design:
├── Single task, no coordination → Single agent
├── Parallel independent tasks → Worker pool pattern
├── Sequential dependent tasks → Pipeline pattern
├── Complex interdependent tasks
│ ├── Clear hierarchy → Hierarchical orchestration
│ ├── Peer collaboration → Swarm/consensus pattern
│ └── Dynamic roles → Adaptive agent mesh
└── Human-in-the-loop → Supervisor pattern
Core Workflows
1. Agent Team Design
- Decompose problem into agent responsibilities
- Define agent capabilities and interfaces
- Design communication topology (hub, mesh, hierarchy)
- Implement coordination protocol
- Add monitoring and observability
- Test failure scenarios
2. Agent Communication Setup
- Choose message format (structured, natural language, hybrid)
- Define message routing strategy
- Implement handoff protocols
- Add retry and timeout handling
- Log all inter-agent messages
3. Scaling Agent Systems
- Profile bottlenecks in current architecture
- Identify parallelization opportunities
- Implement load balancing across agents
- Add agent pooling for burst capacity
- Monitor resource utilization per agent
Best Practices
- Keep agent responsibilities single-purpose and well-defined
- Use explicit handoff protocols between agents
- Implement circuit breakers for failing agents
- Log all inter-agent communication for debugging
- Design for graceful degradation when agents fail
- Version agent interfaces for backward compatibility
Anti-Patterns
| Anti-Pattern | Problem | Correct Approach |
|---|---|---|
| God agent | Single agent doing everything | Decompose into specialized agents |
| Chatty agents | Excessive inter-agent messages | Batch communications, async where possible |
| Tight coupling | Agents depend on internal state | Use contracts and interfaces |
| No supervision | Agents run without oversight | Add supervisor or human-in-loop |
| Shared mutable state | Race conditions and conflicts | Use message passing or event sourcing |