E-commerce Return System
Orchestrator pattern with sequential execution for structured return processing
💡 Key Insights
- Central orchestrator coordinates all steps in a defined sequence
- Each agent has a single, clear responsibility
- State is passed forward from each agent to the next
- Failure at any step triggers compensation of previous actions
State Management
Ephemeral Workflow context passed between agents
Error Handling
Compensation pattern - rollback completed steps on failure
Coordination
Pessimistic locking for inventory updates
Market Analysis Report
Orchestrator pattern with parallel execution for fast data gathering
💡 Key Insights
- Multiple agents run simultaneously to minimize total execution time
- Each agent queries a different data source independently
- Synthesis agent combines all results into final report
- Ideal when tasks have no dependencies on each other
State Management
Ephemeral Results aggregated at synthesis
Error Handling
Fallback - use cached data if source unavailable
Performance
3x faster than sequential (tasks run simultaneously)
Customer Support Router
Orchestrator pattern with conditional branching for intelligent routing
💡 Key Insights
- Classifier agent determines the type and priority of request
- Different paths for different request types (technical, billing, account)
- Urgent requests escalate immediately to human agents
- Demonstrates decision trees in multi-agent systems
State Management
Persistent Ticket history stored
Error Handling
Human escalation for low-confidence classifications
Routing Strategy
Content-based + priority-based routing
Network Diagnostics System
Peer-to-peer pattern with dynamic agent coordination
💡 Key Insights
- No central orchestrator - agents communicate directly
- Monitoring agent decides which other agents to consult
- Agents coordinate dynamically based on what they discover
- More flexible but harder to trace and debug
State Management
Ephemeral Findings shared between agents
Error Handling
Retry + fallback for data source failures
Coordination
Event broadcasting for state changes
Multi-Agent RAG System
Specialized pattern for complex question answering with multiple sources
💡 Key Insights
- Coordinator breaks complex questions into sub-questions
- Specialized retrieval agents query different knowledge sources
- Synthesis agent combines information with source attribution
- Critical for questions requiring multiple perspectives or domains
State Management
Ephemeral Query context passed forward
Error Handling
Continue with available sources if one fails
Coordination
No concurrency issues (read-only operations)