Python + Agents: Building your first AI-driven workflows
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About this event
In Session 4 of our Python + Agents series, we’ll explore the foundations of building AI‑driven workflows using the Microsoft Agent Framework: defining workflow steps, connecting them, passing data between them, and introducing simple ways to guide the path a workflow takes.
We’ll begin with a conceptual overview of workflows and walk through their core components: executors, edges, and events. You’ll learn how workflows can be composed of simple Python functions or powered by full AI agents when a step requires model‑driven behavior.
From there, we’ll dig into conditional branching, showing how workflows can follow different paths depending on model outputs, intermediate results, or lightweight decision functions.
We’ll introduce structured outputs as a way to make branching more reliable and easier to maintain—avoiding vague string checks and ensuring that workflow decisions are based on clear, typed data.
We'll discover how the DevUI interface makes it easier to develop workflows by visualizing the workflow graph and surfacing the streaming events during a workflow's execution.
Finally, we'll dive into an E2E demo application that uses workflows inside a user-facing application with a frontend and backend.
Prerequisites: To follow along with the live examples, sign up for a free GitHub account. If you are brand new to generative AI with Python, start with our 9-part Python + AI series, which covers LLMs, embedding models, RAG, tool calling, MCP, and more.
Pre-requisites:
To follow along with the live examples, sign up for a free GitHub account. If you are brand new to generative AI with Python, start with our our 9-part Python + AI series, which covers LLMs, embedding models, RAG, tool calling, MCP, and more.
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