Most agentic AI research studies control, memory, and verification in isolation. But in deployment, these demands don't arrive separately.

This paper introduces SCRAT (Stochastic Control with Retrieval and Auditable Trajectories): a comparative framework drawn from squirrel ecology—arboreal locomotion, scatter-hoarding, and audience-sensitive caching—to argue that robust agentic systems must couple fast feedback control, structured episodic memory, and embedded verification in a single loop. Three falsifiable hypotheses and a benchmark agenda follow from that coupling.

The implication: agentic systems succeed or fail by how well control, memory, and verification work together.

Download the paper to see how squirrel behavior sharpens the joint engineering problem, and what it means for how agentic systems should be built and evaluated.