From Circuits to Policies: A Review and Taxonomy of Decision Making and Strategy Selection in LLMs and LLM Agents

Abstract

Large language models and the agents built on them can reach the same outcome through different internal computations, reasoning trajectories, and system-level policies. Existing reviews typically treat mechanistic interpretability, reasoning, agent architectures, and safety separately, leaving no common framework for analyzing how a system represents alternatives, evaluates them, commits to one, executes it, and revises that choice. This article presents a structured review and a faceted taxonomy of decision making and strategy selection in LLMs and LLM-based agents. The framework separates system locus—model, harness, or their tested interaction—from temporal scope—a single forward pass or call, a reasoning episode, or an agent trajectory—and decomposes decision making into six functional stages from state representation to update or termination. Five claim-specific evidential relations distinguish behavioral observation, representation decoding, causal influence, controlled manipulation, and mechanistic explanation, while robustness is assessed separately across prompts, tasks, models, harness configurations, and independent replication. Across the literature, evidence is strongest for local mechanisms within individual model calls and substantially weaker for persistent strategies across agent trajectories. The central unresolved gap is cross-scope continuity: few studies follow the same decision variable from internal representation through commitment, executed action, and subsequent feedback. A July 2026 agent-security incident is used to illustrate how the framework converts observed coordination and constraint circumvention into testable causal hypotheses.