When To Use Each Agentic Pattern Based On Task Requirements
Selecting the appropriate agentic pattern depends on several key task characteristics: problem complexity, degree of required autonomy, need for coordination between agents, and whether the task has a clear decomposition strategy. Single-agent patterns suit well-defined problems with clear objectives and linear workflows, while multi-agent patterns become necessary when tasks require diverse expertise, parallel processing, or distributed decision-making. The choice also reflects practical constraints around computational resources, latency requirements, and the availability of pre-built capabilities versus the need for emergent behavior.
Task Complexity and Agent Count
Simple, sequential tasks typically benefit from reactive or deliberative single-agent architectures that follow predetermined logic or respond to specific stimuli. As task complexity increases—particularly when problems involve multiple interdependent sub-problems or require handling uncertainty across domains—multi-agent systems become more appropriate. Hierarchical patterns work well when clear authority structures and task decomposition strategies exist, whereas peer-to-peer or swarm patterns better suit problems where no single agent has sufficient information to guide the entire process.
Coordination Requirements
Tasks requiring tight coupling between specialized agents often use coordinated multi-agent patterns with explicit communication protocols and shared goals. Loosely coupled tasks where agents can operate more independently may benefit from stigmergic or blackboard-style coordination, where agents interact indirectly through shared information spaces rather than direct messaging. High-stakes domains like logistics or scientific research may require deterministic coordination strategies, while exploratory or creative tasks might leverage more emergent coordination mechanisms.
Scalability and Runtime Constraints
Pattern selection must account for scalability demands and computational budgets. Single-agent patterns scale in capability but not in distributed problem-solving. Swarm and multi-agent patterns can scale horizontally but introduce coordination overhead and potential communication bottlenecks. Real-time systems often favor simpler reactive patterns, while batch or offline tasks can support more computationally intensive deliberative approaches with extensive planning and negotiation phases.