Anchor-and-Frontier: Scheduling Tool-Schema Exposure Along Agent Trajectories

Tool-using language models repeatedly ingest executable schemas while acting, creating a trade-off between full-catalog exposure and compact-context efficiency. In multi-step trajectories, however, an omitted tool can alter subsequent calls, observations, and decisions, making schema exposure a trajectory-level scheduling problem rather than only a per-turn retrieval problem. We introduce Anchor-and-Frontier (A&F), a deterministic controller that restores broad schema context at observable user-turn and execution-error checkpoints and otherwise exposes a cardinality-capped, state-conditioned frontier, without additional generative-model calls. On the BFCL V4 primary split, an always-compact controller reduces model-token use by 19.66% but loses 21.57 task-success percentage points relative to full-schema exposure, motivating selective broad-context restoration. On a predeclared 394-example condition holdout, Qwen3.5-27B with A&F achieves 259/394 successes versus 258/394 for full schema while reducing model tokens by 15.85%; a later same-condition Qwen3.5-122B analysis shows a similar 15.55% reduction. Across additional analyses and transfer experiments, token savings are more consistent than success retention, indicating that the efficiency–reliability trade-off is setting-dependent.