The world of conversational AI is evolving at lightning speed, and with tools like Copilot Studio, building intelligent agents is more accessible than ever. But as you craft your AI assistant, you’ll encounter a fundamental choice in how it understands and responds to users: Classic Orchestration versus Generative Orchestration. Understanding the difference is key to building truly dynamic and capable copilots.

So let’s dive in on the differences and what you should choose

The Traditional Approach: Classic Orchestration

Imagine a detailed planned flowchart. That’s essentially classic orchestration. In this model, you, the developer, explicitly define every possible path a conversation can take.

How it works:

• Explicit Intent Recognition: You pre-define specific «intents» (e.g., «Order Status,» «Account Balance,» «Book Appointment»).
• Topic-Based Design: Each intent maps directly to a «topic» or «dialog flow» that you design step-by-step.
• Rigid Pathways: The copilot follows a pre-programmed script. If a user’s query doesn’t exactly match a defined intent, the bot might struggle or default to a fallback message.
• Manual Fallback: You manually configure how the bot should handle unrecognized queries.
• Best For: Structured, repetitive tasks with clear, predictable user inputs. Think FAQs, simple form filling, or straightforward transactional processes.

Think of it as a highly trained specialist who only knows how to do a few things, but does them perfectly according to the script.

The Modern Frontier: Generative Orchestration

Now, imagine a brilliant, highly adaptable generalist who can learn on the fly and connect seemingly disparate pieces of information. That’s generative orchestration, powered by large language models (LLMs).

How it works:

• Dynamic Intent Understanding: Instead of strict, pre-defined intents, the LLM dynamically interprets the user’s natural language query. It understands context, nuances, and even unstated intentions.
• Flexible Information Retrieval: The LLM can intelligently decide which internal topics, external plugins (actions), or knowledge sources (like websites, documents, or your internal data) are most relevant to answer a query.
• On-the-Fly Response Generation: It can synthesize information from multiple sources and generate a coherent, natural-sounding response, even for novel or complex questions it hasn’t been explicitly trained on.
• Proactive Fallback: The LLM can often attempt to rephrase, ask clarifying questions, or direct the user to relevant areas even if a direct answer isn’t immediately available, making the conversation feel more natural.
• Best For: Complex queries, information retrieval from diverse sources, handling unexpected inputs, and providing a more human-like, flexible conversational experience.

Think of it as an incredibly intelligent assistant who can connect dots, pull information from various places, and give you a comprehensive answer, even if they haven’t seen that exact question before and also use information from the fetch itself for the final result.

Visualizing the Difference:

Here’s an image that displays the core difference:

Practical Examples of Generative Orchestration

Using generative orchistration you could ask the following question: Who is the CEO of the company that owns github, where did he go to university and what are the temperature in that city.

Using generative orchistration this would actually give you a respons while classic orchistration would here enter the fallback

If you have configured some kind of knowledge search etc Wikipedia and a weather action generative orchistartion would then be able to solve this be separating the different questions/promts and start fetching data and use that data in the next promt.

Why Generative Orchestration is a Game-Changer

Generative orchestration drastically reduces the amount of manual design work for developers. Instead of building endless flowcharts, you can focus on:

• Providing quality content: Ensure your knowledge bases, topics, and plugins are accurate and robust.
• Defining capabilities: Clearly outline what actions your copilot can take.
• Fine-tuning the LLM: Guide the LLM’s behavior and responses.

It allows your copilot to handle a much wider range of conversations, offering a more intuitive and satisfying experience for users. While classic orchestration still has its place for highly structured tasks, generative orchestration is paving the way for truly intelligent and adaptable AI assistants.

Have you started experimenting with generative AI in your copilots? Share your experiences in the comments below!