(untitled) (Journal of Advanced Artificial Intelligence Research, 2026)
Autonomous scientific discovery agents represent a frontier in artificial intelligence, aiming to accelerate the generation and validation of knowledge across disciplines. This paper introduces a hierarchical planning and execution framework grounded in the cognitive metaphor of fast and slow reasoning, originally articulated by Kahneman and subsequently adapted into computational architectures. The proposed system integrates a rapid, intuitive pattern-matching module for hypothesis generation and anomaly detection with a deliberate, resource-intensive reasoning module for causal inference, experimental design, and theory refinement. A hierarchical controller governs the interplay between these two modes, prioritizing tasks based on epistemic uncertainty, resource constraints, and long-term scientific goals. We examine the architectural trade-offs between speed and accuracy, the governance mechanisms required to ensure robustness and reproducibility, and the infrastructure demands of deploying such agents at scale. The discussion extends to sustainability concerns, including energy consumption and data stewardship, as well as fairness and policy implications arising from automated science. By situating the framework within the broader history of AI and cognitive science, we argue that fast–slow architectures offer a principled path toward trustworthy and efficient autonomous discovery, provided that careful attention is paid to system-level design, oversight, and alignment with human values. This paper contributes a systems perspective that bridges cognitive modeling, engineering, and socio-technical governance.