SRAL: A Framework for Evaluating Agentic AI Architectures

The rapid proliferation of LLM-based agents has produced systems with impressive capa- bilities but inconsistent reliability. Current discourse focuses on what agents can do—tool use, code generation, web browsing—while neglecting the architectural foundations that determine whether they can do these things reliably. This paper introduces SRAL (State- Reason-Act-Learn), a minimal evaluation framework for reasoning about agent architecture. Unlike perception-oriented loops such as Sense-Reason-Act-Learn or Perceive-Reason-Act, SRAL foregrounds State—the constructed, persistent world-model that agents must explic- itly maintain—as the foundational component upon which reasoning, action, and learning depend. We argue that most agent failures trace not to reasoning or action, but to un- managed state: context window overflow, lost constraints, and forgotten decisions. SRAL provides both a dependency model (State → Reason → Act → Learn) and an evaluation methodology (four architectural questions applied in sequence). We differentiate SRAL from related frameworks including OODA, ReAct, and enterprise agent architectures, and demonstrate its application as an analytical tool for agent system design.