More than 3700 large language models have been developed globally over the past five years, including OpenAI, Google Gemini, Meta Llama, France’s Mistral, Canada’s Claude, and more than a thousand other LLMs from China. These models have captured the imaginations and dollars of thousands of individuals, but using these in your business is a different matter. What is the optimal model for my tasks?  What about accuracy? Cost?  And no one wants to share their private data with these AI companies, nor should your business.

An AI Agent operates with a similar function to how the human body processes information. The brain monitors the sensory field and acts through nervous system actions. AI Agents operate under the same concepts, taking input from spreadsheets, PDFs, databases, phone calls, and videos, and orchestrating many “brains”, the models,  to reason, to take action, and to make decisions, over time.

This act of orchestration is also what differentiates AI language models from AI Agents; Chat is not a system of action. In AI Agents, orchestration helps you keep track of your context so it can “remember” over time and over many sources of input. Agents orchestrate the decision of what model to use based on cost, performance, jurisdiction, or application domain. They manage the policies and guidelines of the business, and Agents orchestrate the decision of when to bring in other tools to communicate with humans or different machines.

And that’s why orchestration matters. It manages inputs, frames, and shapes your queries to the model, chooses how everything links together, and curates outputs. Understanding how AI Agents leverage AI models through orchestration is the difference between making a tool and making a system.