Why Multiple Agents
A single agent trying to handle a complex task can struggle with context overload, role confusion, and reliability degradation as the task grows in scope. Breaking the task into pieces and assigning each piece to a specialized agent can improve both quality and reliability.
Think of it like a team. A team with a researcher, an analyst, and a writer produces better reports than a single person trying to do all three roles. Each specialist focuses on what they do best, and the orchestration layer coordinates their work.
Orchestration Patterns
The simplest orchestration pattern is sequential. Agent A does its work, passes the result to Agent B, which passes to Agent C. This pipeline pattern works well when the task decomposes into clear, independent stages. A research agent gathers information, a synthesis agent processes it, and a writing agent produces the final output.
The supervisor pattern uses a coordinating agent that assigns tasks to worker agents and integrates their results. The supervisor decides what needs to be done, delegates to the most appropriate worker, receives results, and determines the next step. This pattern handles more dynamic workflows where the sequence of operations isn't predetermined.
The debate pattern has multiple agents work on the same problem independently and then compare results. Disagreements between agents surface potential errors that a single agent might miss. This is particularly useful for tasks where accuracy is critical and verification is possible.
Communication Between Agents
How agents communicate affects the quality of their collaboration. The most common approach is message passing: one agent produces output that becomes input for another. This is simple but can lose context. The receiving agent only sees the formatted output, not the reasoning behind it.
Shared workspace models give all agents access to a common data store where they can read each other's intermediate results, notes, and plans. This provides richer context but requires careful management to prevent conflicts and confusion.
Frameworks like CrewAI and AutoGen provide built-in communication mechanisms that handle the plumbing. This lets you focus on defining agent roles and task decomposition rather than implementing message routing.
Common Pitfalls
Coordination overhead can negate the benefits of multi-agent systems. If agents spend more tokens communicating and coordinating than they save through specialization, a single agent might be more efficient. Start with a single agent and add more only when you can demonstrate that the additional agent improves outcomes.
Blame attribution is harder in multi-agent systems. When the final output is wrong, determining which agent made the mistake requires tracing through inter-agent communication, which is more complex than tracing a single agent's execution.
Token costs multiply with agents. Each agent consumes its own context window. In a three-agent system, the total token consumption is roughly three times what a single agent would use, plus the communication overhead between them.
When Multi-Agent Systems Shine
Multi-agent orchestration provides the most value for tasks that naturally decompose into independent subtasks requiring different capabilities. A task that needs web research, data analysis, and report writing benefits from three specialized agents because each subtask requires different tools and different prompting strategies.
Tasks that benefit from multiple perspectives also suit multi-agent systems. Code review by two independent agents, each focusing on different quality dimensions, catches more issues than a single reviewer. Analysis from agents with different expertise areas produces more comprehensive results.
Related Reading
- The Agent Loop: How Autonomous AI Systems Make Decisions
- How to Choose the Right AI Agent Framework
- The Cost Economics of Running AI Agents
Discover AI agents on Skillful.sh. Search 137,000+ AI tools.