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technologyreview.com·1h ago
AI Research
arXiv CS.AI·May 27·8 min read
Natural Language Query to Configuration for Retrieval Agents
TL;DR
Retrieval agents—systems that fetch relevant information to answer questions—require manual configuration of which tools to use and how to use them, creating a bottleneck for deployment. Researchers developed a method to automatically generate agent configurations directly from natural language queries, eliminating manual setup.
✦ Why It Matters
Engineers can deploy retrieval agents faster by automating configuration, reducing manual setup overhead and enabling query-specific optimization.
Key Takeaways
- Automate agent tool selection from natural language instead of manual specification.
- Generate query-specific configurations dynamically rather than using static predefined setups.
- Reduce deployment time and configuration errors for retrieval-based AI systems.
- Enable non-experts to configure multi-tool agents without deep technical knowledge.
Full Summary
Retrieval agents are AI systems that dynamically select and execute tools (like search engines or databases) to answer user questions. Traditionally, engineers manually specify which tools an agent can access and how to invoke them—a time-consuming, error-prone process that limits scalability.
This work introduces an automated approach that translates natural language queries into optimal agent configurations without human intervention. The method analyzes query characteristics to determine relevant tools, parameters, and execution strategies.
Evaluation across multiple retrieval benchmarks showed faster configuration generation and improved answer quality compared to static, manually-designed setups. This enables non-experts to deploy retrieval agents more easily and allows systems to adapt configurations dynamically per query.