Choose the Right Agent Architecture
Describe your AI agent system and get research-backed recommendations on whether to use single-agent or multi-agent architectures.
Describe Your Problem
Describe the AI agent system you want to build. Include details about: tools/APIs needed, latency requirements, complexity, and use case.
Or try an example:
Research Foundation
Based on “Towards a Science of Scaling Agent Systems”by MIT & Google DeepMind
180
Experiments
87%
Prediction Accuracy
5
Architectures
Key Insight
Multi-agent performance ranges from +80.9% improvement to -70.0% degradation depending on task structure, not team size.
Domain Performance
Real benchmark results from the research
finance Agent
SAS: 35% → MAS: 63%
workbench
SAS: 63% → MAS: 66%
browse Comp Plus
SAS: 32% → MAS: 35%
plan Craft
SAS: 57% → MAS: 35%
Scaling Principles
Tool-heavy tasks (T>4) suffer disproportionately from multi-agent coordination overhead.
Single-agent efficiency penalty is minimal despite lower absolute efficiency because the interaction magnifies cost for architectures with many tools.
Coordination yields diminishing returns beyond ~45% single-agent baseline.
When single-agent already achieves high performance, multi-agent adds overhead without proportional benefit.
Domain complexity threshold at D≈0.40 determines MAS viability.
Below threshold: MAS yields net positive returns. Above threshold: coordination overhead consumes resources otherwise allocated to reasoning.