Learning to Configure Agentic AI Systems

ArXi:2602.11574v3 Announce Type: replace Configuring LLM-based agent systems involves choosing workflows, tools, token budgets, and prompts from a large combinatorial design space, and is typically handled today by fixed templates or hand-tuned heuristics that apply the same configuration regardless of query difficulty, leading to brittle behavior and wasted compute. To address this, we formulate agent configuration as a semi-Marko decision process (SMDP) where each configuration acts as a temporally extended option that determines how an agent system processes a query, and.