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Building Human-In-The-Loop Agentic Workflows
Understanding how to set up human-in-the-loop (HITL) agentic workflows in LangGraph
Kenneth Leung9 min read·Just now--
Recent LLMs like OpenAI’s GPT-5.4 and Anthropic’s Opus 4.6 have demonstrated outstanding capabilities in executing long-running agentic tasks.
As a result, we see an increased use of LLM agents across individual and enterprise settings to accomplish complex tasks, such as running financial analyses, building apps, and conducting extensive research.
These agents, whether part of a highly autonomous setup or a pre-defined workflow, can execute multi-step tasks using tools to achieve goals with minimal human oversight.
However, ‘minimal’ does not mean zero human oversight.
On the contrary, human review remains important because of LLMs’ inherent probabilistic nature and the potential for errors.
These errors can propagate and compound along the workflow, especially when we string numerous agentic components together.
You would have noticed the impressive progress agents have made in the coding domain. The reason is that code is relatively easy to verify (i.e., it either runs or fails, and feedback is visible immediately).
