Enterprises winning with AI agents are limiting how much the agents can do alone

The useful part

For much of the past two years, the general belief in enterprise AI has been that more autonomy equals better performance. Build agents that can plan, decide, and act across multi-step workflows, and give them as much room to run as possible. That assumption is now being tested at scale, in real production environments — and in a lot of deployments it's failing.

How it works

• McKinsey's 2026 AI Trust Maturity Survey fits right alongside that prediction:
• They're the ones who create AI agents with specific responsibilities and make sure they operate within clear rules.
• Two numbers tell you almost everything about where agentic AI stands in mid-2026.
• By Gartner's own forecast, more than 40% of the agentic AI projects running today won't survive to see 2028.
• Agentic AI deployment is accelerating across every industry, but average responsible-AI maturity sits at just 2.3 out of 4.

What to take from it

Only about 30% of organizations have reached a maturity level of three or higher in governance and agentic AI controls specifically. The 2024-to-2025 race was about who could deploy the most autonomous agent the fastest. It's not about who can build the <a href="https://venturebeat.com/orchestration/an-eval-harness-found-what-...

Details worth keeping

Put those two numbers side by side, and the story tells itself. That shift is changing the competitive framing, too.