Python + Agents: Adding a human in the loop to agentic workflows

Web Reference: Experienced programmers in any other language can pick up Python very quickly, and beginners find the clean syntax and indentation structure easy to learn. Whet your appetite with our Python 3 overview. Python, which was initially developed by Guido van Rossum and made available to the public in 1991, is currently one of the most widely used general-purpose programming languages. Learn Python Python is a popular programming language. Python can be used on a server to create web applications. Start learning Python now » 🏁

YouTube Excerpt: In the final session of our Python + Agents series, we’ll explore how to incorporate **human‑in‑the‑loop (HITL)** interactions into agentic workflows using the Microsoft Agent Framework. This session focuses on adding points where a workflow can pause, request input or approval from a user, and then resume once the human has responded. HITL is especially important because LLMs can produce uncertain or inconsistent outputs, and human checkpoints provide an added layer of accuracy and oversight. We’ll begin with the framework’s **requests‑and‑responses** model, which provides a structured way for workflows to ask questions, collect human input, and continue execution with that data. We'll move onto **tool approval**, one of the most frequent reasons an agent requests input from a human, and see how workflows can surface pending tool calls for approval or rejection. Next, we’ll cover **checkpoints and resuming**, which allow workflows to pause and be restarted later. This is especially important for HITL scenarios where the human may not be available immediately. We’ll walk through examples that demonstrate how checkpoints store progress, how resuming picks up the workflow state, and how this mechanism supports longer‑running or multi‑step review cycles. This session brings together everything from the series—agents, workflows, branching, orchestration—and shows how to integrate humans thoughtfully into AI‑driven processes, especially when reliability and judgment matter most. 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 https://aka.ms/pythonai/rewatch, which covers LLMs, embedding models, RAG, tool calling, MCP, and more. 📌 This event is a part of a series, learn more here: https://aka.ms/PythonAgents/YT Microsoft Agent Framework: https://learn.microsoft.com/agent-framework/overview/agent-framework-overview/?wt.mc_id=youtube_26693_organicsocial_reactor Chapters: 0:00 Introduction and Overview 1:45 Why Bring Humans into the Loop 2:13 Setting up the GitHub Codespace 8:08 Tool Approval for Agents 18:05 Workflow Request and Response Model 25:12 Structured Outputs for Workflows 32:57 Checkpointing and Resuming Workflows 41:06 Implementing Postgres Checkpoint Storage 48:07 Handoff Workflows with HITL 54:49 End-to-End Banking Application Example 1:00:00 Final Summary and Next Steps #microsoftreactor #learnconnectbuild [eventID:26693]

In the final session of our Python + Agents series, we’ll explore how to incorporate **human‑in‑the‑loop (HITL)** interactions into agentic...

Read Full Article 🔍

Curious about Python + Agents: Adding A Human In The Loop To Agentic Workflows's Net Worth? Explore detailed estimates, income sources, and financial insights that reveal the full picture of their profile.

Source ID: 7pGqASn-LEY

Category: color style guide