1. Multi-Agent Architectures
Hierarchical: manager delegates to workers. Peer-to-peer: agents negotiate directly. Sequential: pipeline of specialized agents. Choose based on task complexity.
2. Communication Patterns
Message passing: agents send structured messages. Shared state: all agents read/write to common memory. Pub-sub: agents subscribe to topics. Hybrid common in practice.
3. Task Delegation
Coordinator agent assigns tasks based on: agent capabilities, current workload, task urgency. Use LLM to route: 'this is a coding task → send to CodeAgent'.
4. Shared Memory
Vector DB for knowledge (Pinecone). SQL for structured data. Redis for state. Conflict resolution: last-write-wins, CRDTs, version vectors.
5. Agent Coordination
Consensus: all agents agree before proceeding. Voting: majority decides. Leader election: one agent coordinates. Use based on task criticality.
6. Conflict Resolution
Priority-based: manager overrides worker. Argumentation: agents present reasoning, LLM decides. Escalation: human breaks tie. Log all conflicts.
7. AutoGen Framework
Microsoft's framework. Define agents: {name, system_prompt, tools}. Conversation: agent_a.initiate_chat(agent_b). Auto-routing based on messages. Built-in code execution.
8. CrewAI Framework
Role-based agents: researcher, writer, editor. Crew = team of agents. Tasks assigned to roles. Sequential or parallel execution. Focus: content creation workflows.
9. Use Cases
Research: gather agent + analysis agent + writer agent. Code: planner + coder + tester + reviewer. Customer service: intent classifier + KB lookup + response generator.
10. Production Deployment
Orchestrate with: Temporal (workflow engine), Kubernetes (containers), Ray (distributed). Monitor: agent response times, task success rates. Scale agents independently.