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Abstract illustrated network of interconnected nodes overlaid on human silhouettes representing agentic AI governance and multi-agent systems

Agentic AI Governance: How Do We Govern What We Didn’t Design?

Agentic AI systems build responses nobody specified in advance, and traditional governance models weren’t built for that. Jon Stevens-Hall draws on complexity thinking, from Cynefin to LLM-as-judge testing, to ask what it takes to govern behavior that develops rather than behavior that’s designed, and why loosening control in the right places might be the only way to keep it.
Abstract illustrated stacked data layers in blue and red representing the AI agent data layer foundation for ITSM trust

Before You Trust an AI Agent, Trust Your Data Layer

Salil Kulkarni, CSIO at Virima, argues that when an AI agent gets overridden, the issue is rarely the model. It’s a data layer that’s stale, incomplete, or missing the context a human would have caught. Drawing on decades leading technology at Caesars, Las Vegas Sands, and in hospitality revenue management, he sets out four conditions a data layer must meet before an AI agent can be trusted to act.
Illustrated figure standing at a crossroads between a stormy chaotic landscape and a calm structured one representing ESM adoption choices

Want Better ESM Adoption? Stop Suggesting ESM

Matt Beran’s advice is blunt: never suggest ESM. He argues that IT teams jump straight to the framework and the acronym before another department has finished describing its actual problem, and that this is what turns a useful idea into an initiative people resist. His case is to help departments examine their own work first, let them own the solution, and let ESM show up without ever needing the name.
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