Do Agents Think Deeper? A Mechanistic Investigation of Layer-Wise Dynamics in Sequential Planning

Researchers conducted a mechanistic analysis of how large language models allocate computational depth when operating as autonomous agents performing multi-turn planning and tool use. The study reveals that agents progressively recruit deeper layers as task complexity increases, contrasting with prior findings that LLMs underutilize depth in single-turn tasks, suggesting adaptive depth allocation emerges in sequential reasoning scenarios.

This mechanistic study challenges the prevailing assumption that large language models waste their architectural depth on standard tasks by demonstrating that autonomous agent behavior exhibits fundamentally different layer utilization patterns. The research, which analyzed complete agent trajectories across Deep Research, Code Generation, and Tabular Processing domains, employed residual stream probes and causal interventions to map how computational depth scales with reasoning complexity. The findings suggest that as multi-turn agent interactions unfold, models strategically activate increasingly deeper layers, indicating depth serves a critical function in iterative planning and correction.

The distinction between construction and refinement phases reveals mechanistic insights about how agents operate. Early layers establish semantic directions relatively quickly, while deeper layers contribute essential stabilization of final outputs, particularly in the later turns of agent trajectories. This construction-refinement gap varies across model architectures—pronounced in Qwen and Minimax but more domain-dependent in GLM—suggesting different training approaches yield divergent depth allocation strategies. The shift from feature accumulation toward correction-dominant residual updates indicates agents fundamentally operate through iterative refinement rather than linear feature building.

These findings carry implications for AI system design and optimization. Understanding that agents require genuine depth-dependent computation challenges efficiency assumptions and suggests pruning or distillation strategies optimized for single tasks may be misaligned with agentic workflows. Developers building production agent systems should recognize that depth allocation directly impacts planning quality and multi-step reasoning reliability. Future work should explore whether computational depth correlates with agent performance metrics and whether training procedures can optimize depth utilization specifically for agentic tasks.

• →Autonomous LLM agents progressively recruit deeper layers as trajectories unfold, unlike single-turn tasks where depth utilization remains relatively flat.
• →A substantial gap exists between early semantic construction and deep-layer refinement, with deeper layers essential for stabilizing outputs across multiple reasoning steps.
• →Correction-dominant residual updates in later agent turns indicate agents operate through iterative recalibration rather than accumulated feature building.
• →Depth allocation patterns vary significantly across model families, with Qwen and Minimax showing pronounced construction-refinement gaps while GLM exhibits domain-dependent behavior.
• →These mechanistic findings suggest depth-aware optimization and pruning strategies designed for single tasks may degrade multi-turn agent performance.