AI Orchestration
Definition
AI Orchestration is the coordination of multiple AI components (models, agents, tools, data sources, guardrails) into a single coherent workflow. Orchestration handles routing, retries, fallbacks, and the control flow between AI and deterministic logic.
Why it matters
The business case for AI Orchestration.
A single model almost never solves a real business problem alone. Production AI is always a choreographed system of models, tools, and humans, and orchestration is the layer that makes that choreography reliable.
How SynthesisArc applies it
From concept to production.
PRISM's orchestration layer handles deterministic-to-generative handoffs, multi-agent coordination, and guardrail enforcement in a single runtime, proven in production operations.
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.
Multi-Agent System
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.