82 end-to-end with no human touching it. That is the number. Seats deployed, prompts entered, agents built – those are vanity metrics, and they tell you nothing. Pair it with the cost per resolved unit of work and track both over time. A demo proves an agent can do the task once. Production means it does it 10,000 times, and you know your exception rate. Measure the exceptions as hard as you measure the wins. The failures tell you where the process is broken, and that is usually more valuable than the deflection number. What does an enterprise architecture look like when you have hundreds of AI agents operating across it? It stops looking like a stack and starts looking like an org chart. You still have your systems of record and your models underneath, but the interesting part is above them. You need a registry, so you know what exists. An orchestration layer so agents can hand work to each other instead of every one of them being a dead end. A permission layer scoped per agent. Observability into how an agent reasoned, not just what it output. And a kill switch. The workflow platform becomes the substrate. Agents are the workers. The platform is their management system. Companies that treat agents as features bolted onto individual apps will hit a wall around a few dozen. How does the AI Control Tower help organisations govern an increasingly complex ecosystem of agents? It gives you one place to see every agent in the enterprise, ours and everyone else’s, who owns it, what it is allowed to touch, what it did, and what it delivered. The pieces that matter most to customers right now are risk frameworks mapped to the National Institute of Standards and Technology (NIST) and the EU AI Act out Scheller (left) with the Microsoft and ServiceNow partner team at Knowledge 2026 INTERVIEW
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