Understanding Single-Agent vs Multi-Agent AI Architectures
goyalshaliniuk · x · 2026-08-07
The article outlines mainstream AI agent architectures, categorizing them into single-agent and multi-agent systems, and analyzes their mechanisms and use cases.
Single-Agent Architectures
Operate independently for focused tasks:
- Simple Reflex: Responds instantly via predefined rules
- Model-Based Reflex: Decides using an internal world model
- Goal-Based: Evaluates actions against specific goals
- Utility-Based: Picks actions maximizing overall benefit
- Learning: Improves via feedback loops
Multi-Agent Architectures
Collaborate for complex, dynamic tasks:
- Centralized: A central controller processes data and assigns tasks
- Decentralized: Local decision-making with sync as needed
- Hierarchical: Supervisors delegate to lower-level agents
- Peer-to-Peer: Direct collaboration without central control
- Blackboard: Coordinates via a shared knowledge base
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