arXiv:2602.11574v3 Announce Type: replace Abstract: 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-Markov decision process (SMDP) where each configuration acts as a temporally extended option that determines how an agent system processes a query, and introdu
The proliferation of LLM-based agent systems is highlighting limitations of current configuration methods, driving research into more dynamic and efficient approaches.
Improving agent configuration efficiency and adaptability is crucial for scaling autonomous AI operations and reducing computational waste, impacting resource allocation and long-term cost structures.
The shift from fixed templates to adaptive, dynamic configuration for AI agents will enable more robust and efficient automated workflows.
- · AI software developers
- · Cloud computing providers (through efficient usage)
- · Enterprises adopting AI agents
- · Companies with suboptimal AI agent deployments
- · Manual AI configuration specialists
More sophisticated and reliable AI agents become feasible and widely deployable across various industries.
Reduced operational costs and increased productivity for businesses leveraging AI automation.
Accelerated development of fully autonomous systems capable of handling complex, variable tasks without constant human oversight.
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Read at arXiv cs.AI