Multi-Agent System
Definition
A Multi-Agent System is a coordinated set of AI agents working together on a shared goal, sharing context, handing off tasks, and avoiding conflicts. Multi-agent systems are required for any workflow that crosses departmental or functional boundaries.
Why it matters
The business case for Multi-Agent System.
Single agents work in narrow contexts. Real enterprise workflows cross boundaries, which is why multi-agent coordination is where production-grade agentic AI actually delivers value.
How SynthesisArc applies it
From concept to production.
Collective Mind is the multi-agent coordination layer in the SynthesisArc platform.
Related terms in AI Architecture
Cognitive Architecture
A Cognitive Architecture is the structural design of an AI reasoning system, including how it perceives input, accesses memory, plans actions, and learns from feedback. Cognitive architectures are what differentiate sophisticated AI from simple model wrappers.
PRISM
PRISM is SynthesisArc's seven-layer cognitive architecture for enterprise AI. The layers, perception, context, memory, reasoning, planning, action, and learning, combine deterministic and generative AI to deliver consistent, auditable outcomes.
LLM (Large Language Model)
A Large Language Model (LLM) is a foundation model trained on massive text datasets to predict and generate language. GPT, Claude, Gemini, and Llama are all LLMs.
Agentic AI
Agentic AI refers to AI systems that autonomously execute multi-step tasks toward a defined goal, using reasoning, tool use, memory, and self-correction. Agentic AI moves beyond chatbots that respond to systems that act.
RAG (Retrieval-Augmented Generation)
Retrieval-Augmented Generation (RAG) is an architecture pattern where an AI system retrieves relevant information from a knowledge base and uses it to inform generated responses, grounding the AI in specific, up-to-date data.