Learning to Configure Agentic AI Systems
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 large templates or hand-tuned heuristics. This leads to brittle behavior and unnecessary compute, since the same cumbersome configuration is often applied to both easy and hard input queries. We formulate agent configuration as a query-wise decision problem and introduce ARC (Agentic Resource & Configuration learner), which learns a light-weight hierarchical policy using reinforcement learning to dynamically tailor these configurations. Across multiple benchmarks spanning reasoning and tool-augmented question answering, the learned policy consistently outperforms strong hand-designed and other baselines, achieving up to 25% higher task accuracy while also reducing token and runtime costs. These results demonstrate that learning per-query agent configurations is a powerful alternative to "one size fits all" designs.
698f24193ae80e6a12af8e20
Aditya Taparia
claimed_verified
2026-02-17T15:51:35.020Z
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Aditya Taparia
aditya-taparia
user
aditya-taparia
698f24193ae80e6a12af8e21
Som Sagar
claimed_verified
2026-02-18T09:06:50.937Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6584c10444b9961f765a776d/KDvYnmKk6mQFJha3otq17.png
Som Sagar
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user
sssagar
698f24193ae80e6a12af8e22
Ransalu Senanayake
Learning to Configure Agentic AI Systems
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 large templates or hand-tuned heuristics. This leads to brittle behavior and unnecessary compute, since the same cumbersome configuration is often applied to both easy and hard input queries. We formulate agent configuration as a query-wise decision problem and introduce ARC (Agentic Resource & Configuration learner), which learns a light-weight hierarchical policy using reinforcement learning to dynamically tailor these configurations. Across multiple benchmarks spanning reasoning and tool-augmented question answering, the learned policy consistently outperforms strong hand-designed and other baselines, achieving up to 25% higher task accuracy while also reducing token and runtime costs. These results demonstrate that learning per-query agent configurations is a powerful alternative to "one size fits all" designs.
Learning per-query agent configurations through reinforcement learning improves task accuracy while reducing computational costs compared to fixed templates and hand-tuned heuristics.
LLM-based agent systems reinforcement learning hierarchical policy query-wise decision problem agent configuration token budget prompt engineering tool-augmented question answering reasoning tasks task accuracy computational efficiency