Learning to Configure Agentic AI Systems
📰 ArXiv cs.AI
Learn to configure agentic AI systems using semi-Markov decision processes to optimize performance and reduce waste
Action Steps
- Formulate agent configuration as a semi-Markov decision process (SMDP) to model the configuration space
- Choose workflows, tools, token budgets, and prompts based on query difficulty to optimize performance
- Use reinforcement learning to learn optimal configuration policies that adapt to different query scenarios
- Test and evaluate the performance of the learned configuration policies using metrics such as compute efficiency and accuracy
- Apply the learned policies to real-world agentic AI systems to improve their overall performance and reduce waste
Who Needs to Know This
AI engineers and researchers can benefit from this approach to improve the efficiency and effectiveness of their agentic AI systems, while product managers can use this to inform design decisions
Key Insight
💡 Configuring agentic AI systems can be formulated as a semi-Markov decision process to optimize performance and reduce waste
Share This
🤖 Learn to configure agentic AI systems using SMDPs to optimize performance and reduce waste! 🚀
Key Takeaways
Learn to configure agentic AI systems using semi-Markov decision processes to optimize performance and reduce waste
Full Article
Title: Learning to Configure Agentic AI Systems
Abstract:
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 act
Abstract:
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 act
DeepCamp AI