Productizing AI Agents: 8 Lessons to Successfully Deploy AI Agents

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Enterprise appetite for agentic AI is not the constraint. Autonomy is. Two-thirds of AI projects reach half of full production or less, and only 4% clear the three-quarter mark. Almost none of those failures come down to model quality. They come down to workflow integration, governance, data context, and metrics that were never tied to a business outcome.

 

Written by Somya Kapoor, CEO of IFS Loops, this guide sets out what it actually takes to get an agent into production, drawing on real deployments across manufacturing, energy, utilities and resources, field service, and construction and engineering. Every lesson ends with a question to put to a platform vendor before you sign.

 

Key areas covered include:

  • Why scoping to a workflow rather than a single agent separates a demo from a board-level result
  • Why context, not data cleanliness, is what actually blocks deployment
  • The governance, ownership, and exit criteria that must exist before anything goes live
  • Why the model is not the moat, and what buyers really select on
  • A deployment readiness checklist: eight lessons, eight questions