Investors are aggressively courting AI researchers to build startups that can make AI more reliable and efficient.

Yu Su, an Ohio State professor leading an AI agent lab, said he initially resisted the pressure from VCs to commercialize his work. He finally took the leap last year and spun out his work into a startup when he saw that foundational model advances could make agents truly personalized.

NeoCognition, a startup Su describes as a research lab developing self-learning AI agents, has just emerged from stealth with $40 million in seed funding. The round was co-led by Cambium Capital and Walden Catalyst Ventures, with participation from Vista Equity Partners and angels, including Intel CEO Lip-Bu Tan and Databricks co-founder Ion Stoica.

“Today’s agents are generalists,” Su (pictured left) told TechCrunch. “Every time you ask them to do a task, you take a leap of faith.”

According to Su, the issue lies in a lack of consistency. Current agents, whether from Claude Code, OpenClaw or Perplexity’s computer tools, successfully complete tasks as intended only about 50% of the time, he said.

Since agents are still so unreliable, they are not ready to be trusted, independent workers, Su told TechCrunch. NeoCognition intends to change that by developing an agent system that can self-learn to become an expert in any domain, similar to how humans learn.

Su argues that while human intelligence is broad, its real power is our ability to specialize. When we enter a new environment or profession, we can rapidly master its unique rules, relationships, and consequences.

NeoCognition is building agents to mirror this exact approach.

“For humans, our continued learning process is essentially the process of building a world model for any profession, any environment,” Su said. “We believe for agents to become experts, they need to learn autonomously to build a model of any given micro world.”

Su views this capacity for rapid specialization as the critical missing link to getting AI to work reliably on its own.

While it is possible to train agents for autonomous tasks, they must be custom-engineered for a specific vertical. NeoCognition is different because it’s building agents that are generalists capable of self-learning and specializing in any domain.

NeoCognition intends to sell its agent systems primarily to enterprises, including established SaaS companies, which can use them to build agent-workers or to enhance existing product offerings.

Su highlighted that an investment from Vista Equity Partners is especially valuable for this reason. As one of the largest private equity firms in the software space, Vista can provide NeoCognition with direct access to a vast portfolio of companies looking to modernize their products with AI.

NeoCognition currently has about 15 employees, the majority of whom hold PhDs.