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Microsoft Reactor

Microsoft Reactor에 가입하고 개발자와 라이브 참여

AI 및 최신 기술을 시작할 준비가 되셨나요? Microsoft Reactor는 개발자, 기업가 및 신생 기업이 AI 기술 등을 기반으로 구축하는 데 도움이 되는 이벤트, 교육 및 커뮤니티 리소스를 제공합니다. 참여하세요.

LEARN, CONNECT, BUILD

Microsoft Reactor

Microsoft Reactor에 가입하고 개발자와 라이브 참여

돌아가기

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

5 3월, 2026 | 6:30 오후 - 7:30 오후 (UTC) 협정 세계시

서식:
Livestream

항목: 에이전트

언어: 영어

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.

필수 조건

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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