This is the point.
AI agents need more than model intelligence. They need reliability, validation, and clear human handoffs.
Effect AI is building the human-in-the-loop task layer that helps make agentic workflows work in production.
First large-scale study of AI agents in production just dropped: 306 practitioners, 20 case studies, 26 domains.
(Pan et al, Berkeley collaborators, arXiv:2512.04123)
The findings that should reframe every "agentic AI" pitch you sit through this quarter:
• 68% of production agents execute ≤10 steps before human intervention
• 70% use prompting off-the-shelf models, NOT fine-tuning
• 74% rely primarily on human evaluation
• 66% are deployed in latency-tolerant workflows
The top challenge isn't model intelligence. It's reliability.
The vendors selling "fully autonomous, fine-tuned, instant-response" agents are pitching the opposite of what's working in production.
The winners are simple, controllable, human-in-the-loop systems with measured handoffs.