When AI Starts Acting: The Hidden Data Gaps That Break Enterprise Agents

Scaling agentic AI depends on closing the data gaps that show up when agents move from demos to day-to-day operations.

View the article for clear answers to the questions enterprise leaders are asking:

Q: Why do agents work in pilots, then fail in production?

A: Production adds messy inputs, inconsistent context, and higher stakes. Missing signals and unclear decision rules lead to wrong actions.

Q: What does “agent-ready data” mean?

A: Data that includes business context, freshness requirements, ownership, and clear boundaries for what an agent can and cannot do.

Q: What’s the most common hidden gap?

A: Undocumented process logic. Escalations, exceptions, and “how we actually decide” often live in people, not systems.

Q: What needs to be set before agents can execute actions?

A: Scoped permissions, approval gates for high-risk steps, audit trails, and rollback paths.

It also features insights from Vipin Kataria, Senior Lead Architect (Data/ML) at Picarro (and Agentic & Applied AI for the Enterprise Speaker), on what it takes to make enterprise data usable for agents at scale.

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