Think or Act? State-Conditioned Representation Steering for Strategic Tool Use in Language Agents

Large language model agents increasingly rely on external tools to access information beyond internal reasoning, yet tool use is not uniformly beneficial: a tool call can correct failed internal reasoning, but it can also introduce noise, increase cost, or corrupt an otherwise correct answer. We study this problem as a Think-or-Act decision: whether an agent should rely on internal reasoning or invoke an external tool for a given input. To evaluate this decision, we introduce ToA-Eval, a model-specific protocol that contrasts no-tool and forced-tool executions to identify when tool use helps or harms a particular agent. To control the decision, we propose ToA-Steer, a dynamic cluster-routed steering controller that extracts a shared THINK/ACT direction from contrastive completions and dynamically selects input-specific steering strengths in latent space. Across four reasoning benchmarks and four open-weight LLMs, ToA-Steer raises mean accuracy from 0.585 to 0.723, gains +16pp accuracy on ACT-needed instances and +9pp on THINK-preferred instances simultaneously, and is the only method we evaluate that moves the tool-call rate in opposite directions on these two classes, outperforming prompt-based, training-based, and static-steering baselines. Further analysis shows that THINK/ACT signals concentrate in specific layers and that optimal steering is cluster-dependent, supporting instance-conditioned rather than globally fixed tool-use control.