AI research lab NeoCognition has secured $40 million in seed funding to develop a new generation of artificial intelligence agents designed to learn and adapt in a manner similar to humans. The funding marks one of the largest early-stage investments in the AI agent space, highlighting growing investor interest in more advanced and autonomous AI systems.
Founded by AI researcher Yu Su and a team of academics, the startup is focused on building agents that can continuously learn from their environment and evolve into domain experts over time. Unlike traditional AI models that rely heavily on pre-trained data, NeoCognition’s approach emphasizes adaptive learning, where systems improve through experience and develop a deeper understanding of specific tasks and workflows.
The company aims to address a key limitation in current AI systems—their lack of reliability and consistency in executing complex, real-world tasks. As noted by Su, existing AI agents are often only partially effective, successfully completing tasks roughly half the time, which limits their ability to function as fully autonomous tools.
NeoCognition’s technology is designed to create “expert agents” that build structured models of their working environment, allowing them to become faster, more efficient, and more reliable over time. This human-like learning process could significantly reduce the need for manual customization and constant supervision, making AI more practical for enterprise applications.
The startup’s approach reflects a broader shift in the AI industry, where the focus is moving beyond scaling large language models toward developing systems capable of reasoning, adapting, and specializing. By enabling AI to gain expertise in fields such as healthcare, finance, and operations, NeoCognition aims to unlock new levels of productivity and automation across industries.
