Parsing Is Not Executing: Decentralized Compliance for Agentic Query Plan Routing

TRIAGE: Three-level Routing and Intelligent Agent Guidance for Efficient Execution

Large Language Model (LLM) agents based on the ReAct paradigm have demonstrated remarkable capabilities in tool use and task execution. However, ReAct suffers from a fundamental efficiency problem: every query triggers a complete reasoning loop from scratch, and similar queries repeat identical steps without leveraging historical experience. We propose TRIAGE,a three-level routing framework that reduces token consumption by reusing historical execution trajectories. Its core innovation is TaaS (Trajectory-as-a-Skill), which abstracts historical execution trajectories into reusable skills, realizing'experience as a service'. TRIAGE classifies queries into three levels: (1) Direct Reuse-identical queries, 0 tokens; (2) Skill Substitution-similar queries, 0 tokens via deterministic parameter substitution; (3) Full ReAct-novel queries, automatically stored for future reuse. In large-scale experiments on 1,007 security monitoring queries, TRIAGE achieves 62.3% token savings, with 56.0% of queries at Level 2 and 5.5% at Level 1, both executing at zero cost. Cross-domain validation on ToolBench (15 domains, 345 queries) achieves 76.3% token reduction, confirming the generalizability of semantic routing. An online learning experiment demonstrates cold-start-to-mature evolution: the L2 hit rate rises from 0% to 57% within the first 100 queries, and the average token cost drops from 198 to 74.7. We also propose an automatic Skill extraction mechanism that distills high-frequency trajectory patterns into deterministic Skills, creating a positive feedback loop of'the more you use it, the more efficient it becomes'.

Trust at the Seams: Differential, Decentralized Compliance for Composable Query Engines

MetaRoute-Bench: Evaluating Meta-Decision Policies for Agentic Workflow Routing

This work presents MetaRoute-Bench, an open, inspectable framework for comparing meta-decision policies under a shared execution model, and releases task generation, policies, traces, tests, and analysis artifacts to support live-system validation.

Learning Compositional Meta-Routing for Agentic Workflows: An Executable Benchmark

This work introduces an executable benchmark and a budget-aware meta-router that composes heterogeneous operations from raw task text that establishes a reproducible testbed and a bounded proof of concept, not evidence of live-LLM performance.

TransForm: Dynamic Format Selection for MCP Tool Outputs in Agentic Data Systems

AgentWeave: Routing Before Reasoning for Efficient Function Calling in Tool-Rich Language Models

AgentWeave is introduced, a deterministic pre-inference routing layer that constructs a bounded model-visible action space using eligibility, requirement, capability, and routing signals and shows lower mean local-model latency.