Agentic AI systems increasingly operate in interac- tive, multi-step environments where correct behavior demands not merely generating responses, but disciplining how and when to act, solicit clarification, or refrain altogether. Frameworks such as ReAct [1], Toolformer [2], and Reflexion [3] have substantially advanced reasoning–action integration and self- refinement, yet they rely on emergent prompt heuristics or uncalibrated confidence signals for behavioral control. This structural weakness produces redundant tool calls, elevated latency, and avoidable error propagation in cost-sensitive, long- horizon tasks. This paper proposes Calibrated Act–Ask–Abstain Gating (CAAG), a behavior-level policy layer that treats agent action selection as a budgeted selective-decision problem under uncertainty. CAAG couples a lightweight calibration head with an expected-utility gating rule and a memory-triggered reflection mechanism, enabling resource-efficient deployment on a frozen backbone model. The policy is formulated under formal action- cost and bounded-risk constraints, allowing graceful degradation on resource-constrained systems without sacrificing task fidelity. Simulated analysis across seven public benchmarks—including WebArena, Mind2Web, SWE-bench, and ALFWorld—indicates that CAAG achieves substantial reductions in tool-call overhead (20–35%), false action rate (15–30%), and end-to-end latency (15–25%) while preserving or slightly improving task success rates. CAAG positions basic behavioral calibration as a first-class optimization target in agentic AI, a missing design principle in contemporary agent architectures.

Index Terms—Agentic AI, uncertainty calibration, selective prediction, tool use, resource-efficient agents, act–ask–abstain policy, behavioral gating.