Beyond Single-Agent Systems
Single-agent architectures hit a wall quickly in production. Real-world workflows demand multiple specialized agents working in concert — each with distinct capabilities, knowledge domains, and decision boundaries.
The challenge isn't building individual agents. It's orchestrating them reliably at scale.
Core Orchestration Patterns
1. Fan-Out / Fan-In
A coordinator agent distributes subtasks to specialized agents in parallel, then aggregates their results. Ideal for research tasks, data enrichment, and multi-source analysis where speed matters.
2. Pipeline (Sequential)
Agents process data in sequence, each transforming the output of the previous agent. Best for workflows with natural stages: extraction → validation → enrichment → action.
3. Hierarchical Delegation
A supervisor agent breaks complex goals into subgoals and delegates to specialist agents, who may further delegate to sub-specialists. This mirrors how human organizations handle complex projects.
4. Consensus / Voting
Multiple agents independently analyze the same input and vote on the correct output. Critical for high-stakes decisions where accuracy matters more than speed — medical diagnosis, financial risk assessment, legal analysis.
Production Considerations
- Failure handling: What happens when one agent in a pipeline fails? Design for graceful degradation.
- State management: Shared state between agents must be consistent. Use event sourcing or CQRS patterns.
- Observability: Every agent decision must be traceable. Log reasoning chains, not just outputs.
- Cost control: Multi-agent systems multiply API costs. Implement intelligent routing to use the cheapest model that meets accuracy requirements.
The art of multi-agent orchestration is knowing when to coordinate and when to let agents operate independently.