Multi-Agent Orchestration Patterns
Building systems where a single LLM call is not enough. Multi-agent orchestration enables complex workflows by breaking tasks across specialized agents that collaborate, share context, and recover from failures.
Why Multi-Agent?​
| Single Agent | Multi-Agent |
|---|---|
| One prompt, one response | Task decomposition across specialists |
| Limited by single context window | Each agent has focused context |
| All-or-nothing failure | Graceful degradation per agent |
| Hard to test individual capabilities | Each agent independently testable |
Architecture Overview​
A typical multi-agent system on AWS involves:
- Orchestrator agent - Receives user intent, decomposes into tasks, routes to sub-agents, synthesizes results
- Task agents - Single-purpose agents (research, code generation, data analysis, summarization)
- Tool layer - Lambda functions, API calls, database queries that agents can invoke
- State management - DynamoDB or Step Functions for tracking workflow progress
- Communication - Direct invocation, event-driven (EventBridge), or queue-based (SQS)
What This Course Covers​
| Module | Topic |
|---|---|
| 1 | Orchestrator design patterns (router, planner, supervisor) |
| 2 | Agent-to-agent communication protocols |
| 3 | State management with Step Functions |
| 4 | Error handling and retry strategies |
| 5 | Parallel vs sequential agent execution |
| 6 | Context sharing and memory across agents |
| 7 | Testing multi-agent workflows |
| 8 | Production deployment on AWS |
Premium
Multi-Agent Orchestration Patterns
Get the complete 8-module course with orchestrator implementations using Strands SDK, Bedrock Agents, and Step Functions.