NeoCognition raises $40M to build self-learning AI agents that specialize like human experts

AI research lab NeoCognition lands $40M seed to build agents that learn like humans | TechCrunch

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. 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 process. "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 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 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.

NeoCognition's $40M bet on self-learning AI agents

The Palo Alto startup, spun out of Ohio State University by Yu Su, argues that current agents complete tasks as intended only half the time, a reliability gap it plans to close by giving agents a mechanism to build world models of the domains they operate in, learning on the job as a specialist rather than relying on fixed general training. NeoCognition, a Palo Alto AI research lab, has emerged from stealth with $40 million in seed funding. The oversubscribed round is co-led by Cambium Capital and Walden Catalyst Ventures, with participation from Vista Equity Partners. Angel investors and founding advisors include Lip-Bu Tan, CEO of Intel and founding managing partner of co-lead Walden Catalyst Ventures; Ion Stoica, co-founder and executive chairman of Databricks, and AI researchers Dawn Song, Ruslan Salakhutdinov, and Luke Zettlemoyer. Additional institutional participants include A&E Investments, Salience Capital Partners, Nepenthe Capital, and Frontiers Capital. The company was founded by Yu Su, Xiang Deng, and Yu Gu. Su is a professor at Ohio State University who has led one of the country's most established LLM-based agent research labs since well before the ChatGPT moment, and holds a Sloan Research Fellowship. He described himself as having initially resisted pressure from venture capital to commercialise his work, until he concluded that advances in foundation models had reached a point where genuinely personalised agents were feasible, at which point he spun the lab out last year. The problem NeoCognition is trying to solve is reliability. Su's claim, which the company has not independently substantiated in a published benchmark, is that current AI agents successfully complete tasks as intended only around 50% of the time. T hat figure is broadly consistent with widely-reported findings from AI coding agent evaluations, though specific numbers vary by task type, agent, and evaluation methodology. The consequence, Su argues, is that agents cannot be trusted as independent workers: every task is a gamble. NeoCognition's response is to give agents a mechanism for rapid specialisation through experience, specifically, by learning to build a "world model" of whatever micro-environment they operate in, capturing its rules, relationships, and constraints through use rather than through pre-training on general data. The conceptual model draws a direct analogy to human learning. Su's argument is that what makes human intelligence powerful is not its breadth but its plasticity, the capacity to enter a new professional environment and rapidly develop deep domain expertise by internalising how that specific world works. Current AI agents, optimised for generalism, lack this specialisation mechanism. NeoCognition's thesis is that building it in, as a learnable, autonomous process rather than a manually engineered one, is what separates reliable specialist agents from the current generation of capable-but-inconsistent generalists. The commercial strategy is primarily enterprise, focused on established SaaS companies rather than consumer end-users. The pitch to a software vendor is that NeoCognition's agent system can be embedded to create AI workers that improve over time within that vendor's specific operational context, or to power agentic upgrades to existing product offerings. Vista Equity Partners' participation is framed as a distribution lever: Vista manages one of the largest portfolios of enterprise software companies in private equity, giving NeoCognition potential direct access to software firms actively looking to embed AI at the application layer. The team has roughly 15 employees, most holding PhDs. The company's specific technical approach has not been disclosed in detail beyond the 'world model' framing, and no product is yet publicly available.

AI Startup NeoCognition Raises $40 Million to Build Agents that Learn Like Humans

The company also said a deeper understanding of a work environment could improve speed, cost efficiency, reliability, and safety in higher-stakes use cases. NeoCognition ties its commercial story to Su's academic record in AI agents. The company said Su's Ohio State lab produced work such as Mind2Web, MMMU, and SeeAct, which it describes as part of the foundation of the modern agent field. Walden Catalyst's Lip-Bu Tan said Su's team has worked across major parts of agent development, including perception, memory, planning, evaluation, and safety. Su serves as an associate professor and innovation scholar at Ohio State, where he works on language agents, planning, world models, memory, and evaluation. His profile states that he co-directs the OSU NLP group and leads research teams tied to foundational AI and machine learning. Ohio State has also noted that Su joined the university in 2020 and previously worked at Microsoft Semantic Machines. This funding round reflects strong that focus on the reliability layer of AI rather than only on larger foundation models. NeoCognition now enters that race with fresh capital, academic credibility, and an enterprise pitch built around specialized AI agents that keep learning after deployment.