AI agents are designed to execute tasks intelligently — but what happens when their goals don’t fully match yours? In this episode, Ben Kus, CTO of Box and Meena Ganesh, Sr. Product Marketing Manager for AI at Box, break down the concept of misalignment in AI agents: when the system tries to fulfill an objective but runs into missing permissions, broken integrations, or ambiguous instructions. From data retrieval failures (“user doesn’t have access”) to unintentional side effects (“system down for maintenance”), misalignment isn’t about AI going rogue — it’s about context gaps between human intent and system execution. You’ll learn: -What “agent misalignment” looks like in real-world enterprise workflows -Why even well-designed AI systems can misinterpret intent -How to build alignment through clear objectives, feedback loops, and human-in-the-loop review -The design principles that help agents know when to stop, clarify, or escalate Because in enterprise AI, success isn’t just what agents can do — it’s what they should do. Chapters: 00:00 Intro: example scenario 00:23 Why this episode matters 00:57 What is a misaligned agent? 03:14 How agents interpret instructions 05:06 Why misalignment is dangerous for businesses 07:49 Real incidents and research findings 10:12 Three ways to prevent misaligned agents 11:08 Wrap and subscribe

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