Three-quarters of enterprise leaders tell us they’re adopting agentic AI. However, only a small minority have it running in meaningful production beyond “agentish” chatbots — true scaled multiagent systems are rarer still. That’s the gap between the chase and the catch, and it’s the story of 2026. The technology is a runaway train — the enterprise is the heavy load it has to pull.
In our latest report, The State Of Agentic AI, 2026, my colleagues and I discuss the architects building agentic systems and dig through Forrester’s survey data to put meat on the bones of the story. Our read is that the technology has arrived and enterprise readiness hasn’t caught up. That shouldn’t surprise anybody. We’ve seen this story before. The harder question is whether readiness can ever catch a technology moving this fast.
Long-Horizon Agents Are No Longer On The Horizon
The capabilities are here, and they arrived faster than anybody expected. The vendor market is reorganizing itself in real time around agents. Agents now run for hours, days, even months. OpenAI has operated an internal software development workflow with minimal intervention for months. Cursor has deployed long-running coding agents. Anthropic has demonstrated multiday research agents. The proofs are in.
A long-running agent doesn’t behave like a chatbot: It behaves like a distributed system, and distributed systems demand orchestration, identity, and context discipline that most companies have never built. Scaling fails on task complexity — not agent count — and most teams aren’t managing that complexity at all. Stitch a dozen isolated agents together without shared registries or routing and coordination falls apart into duplication and drift.
The Chase Is Easy — The Catch Is Expensive
Interest is everywhere — scale is rare. The reasons are stubbornly consistent, and they start with money. ROI uncertainty traps enterprise ambition in pilot mode because most companies can’t justify production beyond narrow efficiency gains. Governance gaps drive agentic sprawl. More than half of enterprises report it even after adopting the NIST AI RMF because a policy document can’t control an autonomous, tool-invoking system. And platform confusion freezes commitment while teams argue over whether to bet on a SaaS agent, a systems integrator-built system, or a custom build.
Underneath all of it sits the trust tax. Every autonomous action has to be logged and defensible to an auditor, and right now that cost is too high. Even the leaders feel it. Bank of New York is about as far out front as a regulated enterprise gets, and it still hasn’t captured the full value of agentic promises. But Bank of New York has something most don’t: Its workforce is ready to manage highly autonomous agents inside a tightly regulated business. That readiness is gold.
Risk Management Is The Real Constraint
This is the part executives underestimate. Autonomous systems that operate beyond real-time human oversight are both promising and perilous. In Forrester’s Security Survey, 2026, 49% of security decision-makers named agentic AI as a concern. These threats are new in kind, not just degree. Agents can impersonate each other and escalate privileges because nonhuman identity is still a mess. Their populations grow faster than anyone can keep track of. And when coordination breaks, a small misjudgment becomes an outage.
You can’t govern that with quarterly reviews. You govern it with instrumentation that runs while the agent does, with identity and policy enforced as code rather than written down and hoped for.
How To Start Catching The Train
The companies pulling ahead aren’t the ones with the most agents. They’re the ones laying the track the train will run on. Three moves matter most:
Invest in orchestration before adding agents. Shared registries and hand-off patterns are critical for agents and conventional systems to work as one.
Redesign the work, not just the tooling. Agents bolted onto human-paced legacy workflows produce task savings, not step-change value. Pick a few high-friction workflows and rebuild the roles and approvals around autonomy.
Treat every agent as a governed identity. Give it unique credentials, least privilege, full logging, and a named owner who manages its lifecycle.
Following completion of these moves, scale in stages. Start with bounded tasks behind approval gates and rollback paths. Widen autonomy only when the controls earn it.
The train is moving, and fast. The only question now is whether it’s headed where you want it to go.
ReadThe State Of Agentic AI, 2026 for the full picture. It maps the six use-case categories where agents are actually delivering and lays out the control-plane playbook for closing the gap. Then schedule a session and we’ll help you sequence it.
