82% of executives say they can keep up with governing AI agents. Only 26% are ready for when agents collide.

In June, CIO Dive surveyed 858 IT executives for a report with OutSystems and KPMG. 82% believe their organizations can keep pace with governing, integrating, and monitoring AI agents. Only 26% say they're well prepared to resolve conflicts across agents, systems, and automated workflows. That's the state of agentic AI in the enterprise right now. Confident about the mission, unprepared for the mechanics.

Oversight is mostly manual, too. Just 5% have automated controls, audit trails, and formal human review points in place. That leaves most companies governing agents with processes built for software that waits for a human to click submit. Agents don't wait. They act in parallel, on live systems, with no one in the loop unless someone put one there on purpose. OutSystems sells a platform for governing agents, so it has a stake in this gap. But CIO Dive ran the survey on its own reader base.

Camunda's 2026 survey points the same way. Coleman Parkes surveyed 1,150 IT and business decision makers at large companies for it. 73% admit a gap between their agentic AI vision and what's actually running. Half believe untamed agents risk "fanning the flames" of processes and automations that were poorly built in the first place.

I ran program management on GenAI data operations, QA at scale across training data pipelines. The lesson carries over directly. You can't bolt quality control onto a system after it's already running at volume. Review points and escalation paths get designed before the pipeline runs, because retrofitting them later is slower and more expensive.

Agent conflict is the same problem with a new name. When two agents touch the same account, the same inventory line, or the same support ticket, something has to arbitrate: a priority order, a lock on the resource, a defined trigger for human escalation. An unresolved conflict between two agents is a missing acceptance criterion nobody wrote down before launch.

The same report found 81% of executives rate testing and validating agent behavior as very or extremely important when picking an AI platform. That instinct is right, and often aimed at the wrong layer. Testing one agent's outputs tells you it's capable. It doesn't tell you what happens when two capable agents reach for the same resource at once, or when your own agents start stepping on each other as you add more.

So before an agent acts on anything outside its own sandbox, write down four answers. Who wins when two agents disagree? What triggers escalation to a human? How far can the agent go before someone sees what it did? How does it get shut off quickly? It's the multi-agent version of the blast radius I wrote about before. Treat those answers as acceptance criteria, test them like you test the agent's actual task, and revisit them every time you add an agent, because every addition changes who can collide with whom.

Confidence and readiness are two different numbers, and this survey put the gap between them in public. Ship an agent before you've decided what happens when it disagrees with another one, and the agents will decide for you.