Lilly Tech Systems
Designing Multi-Agent AI Systems
Master the architecture of systems where multiple AI agents collaborate to solve complex tasks. From orchestration patterns and inter-agent communication to tool infrastructure and production scaling — everything you need to build multi-agent workflows that work in the real world.
Your Learning Path
Follow these lessons in order to design and build a complete multi-agent system, or jump to any topic you need right now.
1. Multi-Agent Architecture Patterns
Single agent vs multi-agent, orchestration patterns (sequential, parallel, hierarchical, debate), when multi-agent makes sense, real examples from coding agents and research workflows.
2. Individual Agent Design
Agent components (planner, executor, memory, tools), ReAct pattern, tool use architecture, agent state management, error recovery, and a production agent framework.
3. Agent Orchestration Engine
Workflow DAGs for agents, supervisor pattern, round-robin delegation, dynamic task routing, parallel execution with aggregation, and a complete orchestrator implementation.
4. Inter-Agent Communication
Message passing patterns, shared memory and blackboard architecture, event-driven communication, structured output contracts between agents, and conflict resolution.
5. Tool & Action Infrastructure
Tool registry and discovery, sandboxed execution environments, permission models for agents, rate limiting tool calls, audit logging, and production tool infrastructure code.
6. Reliability & Scaling
Agent failure handling, timeout management, cost budgets per workflow, human-in-the-loop checkpoints, horizontal scaling strategies, and monitoring agent workflows.
7. Best Practices & Checklist
Multi-agent production checklist, debugging agent workflows, when NOT to use multi-agent systems, and a comprehensive FAQ for multi-agent engineers.
What You'll Learn
By the end of this course, you will be able to:
Design Agent Architectures
Architect multi-agent systems with the right orchestration pattern — sequential, parallel, hierarchical, or debate — matched to your use case.
Build Agent Infrastructure
Implement tool registries, sandboxed execution, inter-agent communication, and orchestration engines using production Python code you can deploy at work.
Handle Failures Gracefully
Design for agent failures, implement cost budgets, add human-in-the-loop checkpoints, and build retry strategies that keep multi-agent workflows reliable.
Scale to Production
Monitor agent workflows end-to-end, scale horizontally, manage costs, and debug complex multi-agent interactions in production environments.