Abstract Human expertise emerges through iterative cycles of interaction, reflection, and internal model updating, which are central to cognitive theories such as Kolb’s experiential learning and Vygotsky’s zone of proximal development. In contrast, current AI systems, particularly large language models (LLMs) agents, rely on static pretraining or rigid workflows and lack mechanisms for continual adaptation. Recent studies have identified early cognitive traits in LLM agents, including reflection, revision, and self-correction, which suggest foundational elements of human-like experiential learning. This leads to a key question: Canwe design LLM agents capable of structured, cognitively grounded learning similar to human processes? To address this,we propose a computational framework ofKolb’s learning cycle with Vygotsky’s ZPD for autonomous agents. Our architecture separates extrinsic functions (environmentinteraction) fromintrinsic functions (internal reflection and abstraction), enabling cognitively grounded scaffolded learning, where the agent initially learns within structured, supportive environments, followed by open-ended generalisation. This approach empowers agents to master complex, many-step tasks ; domains that traditional fine-tuning or simple reflective methods could not tackle effectively. Its potential is powerfully demonstrated through direct competition with humans in real-world Kaggle data science challenges. Learning fully automated, end-to-end data science code generation across 81 tasks, our system, Agent K, demonstrated the ability to perform the entire workflow without human intervention, achieving an Elo-MMR score of 1694, placing it beyond median performance of the Kaggle Masters (the top 2% among over 200,000 users) included in our study. With 9 gold, 8 silver, and 12 bronze medals level performance – including 4 gold and 4 silver on prize-awarding competitions – Agent K is the first AI system to successfully integrate Kolb- and Vygotsky-inspired human cognitive learning, marking a major step toward generalist AI.
Bou-Ammar et al. (Wed,) studied this question.