Agentic AI Is Here, But True Autonomy Remains Out of Reach

Agentic AI, the promise of systems that act with reasoning, adaptability, and independence, is a reality. In FeatureByte’s new report with Databricks and CIO.com, Unlocking autonomous agents: Why smarter data science is the missing link,  57% of enterprises report deploying agentic AI in production, and most plan to expand use cases in the next 12 months.

But here’s the catch: only 14% have achieved even semi-autonomous decision-making. The rest still depend heavily on human oversight and task automation.

Why Autonomy Still Eludes Most Enterprises

Agentic AI needs decision context to make intelligent, sound decisions. It needs the ability to understand patterns, anticipate outcomes, and act with predictive insight, which LLMs alone can’t do effectively. Most organizations haven’t yet operationalized predictive AI at the scale needed to give agents that “intelligence.” Instead, data remains fragmented, workflows are manual, and teams struggle to keep pace with the complexity of real-world decision environments.

Bridging the Gap

To move beyond rule-based assistance, organizations must embed predictive intelligence directly into agentic workflows. That means automating the entire data science lifecycle, so that data science can happen at scale and agents have a wealth of models to use in their decision-making.

FeatureByte’s Data Science Agent automates data science with speed and scale, giving both humans and agents the models they need to make business-critical decisions. Agentic AI is proliferating, but autonomy depends on predictive context and automation. Without solutions like FeatureByte, AI agents remain assistants, not decision-makers.

Download the report