AI Glossary
What is Multi-Agent Systems?
Overview
Multi-agent systems use multiple AI agents that coordinate, debate, delegate, or specialize across a task. They matter because many real workflows are too broad for a single model call: teams are testing planner, researcher, coder, reviewer, and tool-using agents that work together with shared state and guardrails.
Why it matters
Multi-agent design can improve coverage and specialization, but it also adds coordination, cost, security, and evaluation complexity.
Where it appears in AI research
- Agent workflow products
- AI coding and research assistants
- Enterprise automation systems
- Agent safety and evaluation papers
Related terms
Related DeepSignal articles
Arbor: Tree Search as a Cognition Layer for Autonomous Agents
Arbor introduces a framework utilizing structured tree search for optimizing LLM inference, achieving up to 193% throughput-latency improvement compared to vendor-optimized systems. It employs an Orchestrator and Critic agent for stability and coordination, demonstrating hardware-agnostic performance with minimal variance.
