Up to this point, we’ve been interacting with models directly. While models are the “brains” of the operation, turning them into Agents allows for even more control and connectivity to data and tools. Agents sit above deployed Models and orchestrate their behavior. An agent uses a model along with instructions, memory, tools, and knowledge to accomplish goals autonomously. It can decide what steps to take, call tools or APIs, manage context, and iterate toward an outcome.
In this chapter, we’ll learn how to deploy an Agent in Microsoft Foundry, connect it to various data sources and tools, and then manage its behavior through instructions and prompts.
Agents Versus Models?
In Foundry, agents are designed to go beyond single responses and actively carry out work. They can plan and reason across multiple steps, using tools and APIs to retrieve and ground answers in enterprise data. They can also maintain context or memory across interactions. Furthermore, agents can automate workflows, collaborate with other agents, and react to events or user input to adapt their behavior over time. This makes them well suited for scenarios like customer support automation, data analysis, code generation, business process orchestration, or AI copilots that operate with a clear goal rather than a single prompt. See Figure 3-1 for a diagram of the agentic flow between inputs, the model, tools, and outputs.
Figure 3-1. The agentic flow between inputs, the model, tools, ...
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