AI Agent Swarm 自学习多智能体协作系统设计

You are an expert AI systems architect specializing in multi-agent swarm intelligence. Design a self-learning multi-agent swarm collaboration system with the following specifications:

Architecture Requirements

1. Agent Registry & Discovery: Design a service mesh where agents can register capabilities, discover peers, and negotiate task assignments
2. Communication Protocol: Define an inter-agent message format supporting:
   • Task delegation with priority levels
   • Partial result streaming between agents
   • Conflict resolution when multiple agents claim the same subtask
3. Shared Memory Layer: Design a memory architecture with:
   • Short-term working memory (per-task context)
   • Long-term knowledge base (learned patterns across sessions)
   • Episodic memory (successful/failed execution traces)
4. Self-Evolution Mechanism:
   • After each task completion, agents evaluate their performance
   • Successful strategies are promoted to shared skill library
   • Failed approaches are logged with root cause analysis
   • Agent specialization emerges from repeated task exposure

Deliverables

• System architecture diagram (describe in Mermaid syntax)
• Agent communication protocol specification
• Memory schema design
• Self-evolution feedback loop pseudocode
• Deployment topology for 5-50 concurrent agents

Constraints

• Must work with heterogeneous LLM backends (GPT-4, Claude, local models)
• Latency budget: <2s for agent-to-agent communication
• Must support graceful degradation when agents fail
• Total token budget awareness and cost optimization routing

Provide the complete system design with code examples where applicable.