Multi-Agent Simulator
Experiment with agent systems and visualize emergent behaviors
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0 / 5 completedCoordination Architectures
How agents coordinate their actions fundamentally shapes system performance. Centralized patterns offer simplicity and control, decentralized patterns provide resilience and scalability, and hierarchical patterns balance both extremes. Each excels in different scenarios.
Coordination Patterns Compared
Centralized
Single coordinator directs all agents
- • Simple coordination
- • Global view
- • Easy to control
- • Single point of failure
- • Communication bottleneck
- • Scalability limits
Decentralized
Agents coordinate through peer-to-peer interaction
- • No bottleneck
- • Fault tolerant
- • Scales well
- • Complex coordination
- • No global view
- • Slower convergence
Hierarchical
Multi-level coordination with managers and workers
- • Balanced control
- • Manageable complexity
- • Flexible
- • Middle layer overhead
- • Moderate bottleneck
- • Complex design
Interactive: Pattern Comparison
Choose a coordination pattern and adjust system parameters. Observe how efficiency changes with different configurations.
COORDINATION PATTERN
When to Use Each Pattern
- • Small agent teams (<10)
- • Global optimization needed
- • Simple deployment
- • Low latency network
- • Large-scale systems
- • High fault tolerance required
- • Distributed environments
- • Local decision-making
- • Medium-large teams
- • Multi-level tasks
- • Balanced control needed
- • Complex workflows
💡 Key Insight
Coordination pattern choice has cascading effects. A centralized coordinator might be 10x faster for small teams but collapse under scale. Decentralized systems can handle thousands of agents but struggle with global optimization. Hierarchical designs split the difference but add architectural complexity. Match the pattern to your constraints—team size, network topology, fault tolerance requirements, and performance goals.