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Autonomous agents promise efficiency, but without visibility and control, they risk costly mistakes before anyone can intervene. In this episode, we explore how to transform AI agents from opaque black boxes into steerable, inspectable systems using LangGraph’s human-in-the-loop capabilities. You’ll learn:

* How streaming exposes an agent’s reasoning in real-time, from token generation to state transitions, building trust through transparency

* How breakpoints enable surgical intervention at critical decision points, allowing humans to approve, reject, or correct actions mid-execution

* How time travel lets you rewind to any prior state, fork alternative reasoning paths, and explore “what-if” scenarios without restarting from scratch

If you’d rather read than listen, the full article (with diagrams, code examples, and implementation details) is available on Substack:

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