arXiv:2606.13904v1 Announce Type: cross Abstract: Exploratory question answering (EQA) over data lakes requires an LLM agent to discover relevant sources, analyze retrieved data, and adapt its actions based on intermediate results. End-to-end accuracy alone cannot distinguish failures in search, planning, data analysis, or the agent's Action Policy: its decisions about what to do next and when to submit an answer. We present SANA (Search Agent Navigation Ablation framework), a diagnostic ablation framework that transforms EQA tasks into runtime profiles containing gold source sequence, sanitiz
The proliferation of advanced LLMs and the increasing complexity of data environments necessitate better diagnostic tools for autonomous AI agents.
Improving the diagnostic capabilities of AI agents is crucial for developing reliable and effective systems capable of handling real-world complexity.
The SANA framework provides a standardized method for evaluating and enhancing exploratory question-answering agents, enabling faster progress in agentic AI development.
- · AI Agent Developers
- · Data Infrastructure Providers
- · Enterprises Adopting AI Agents
- · Inefficient AI Agent Architectures
- · Organizations with Poor Data Governance
More robust and accurate AI agents emerge for complex data lake navigation.
Increased adoption of AI agents across industries due to improved reliability and explainability.
New business models and services centered around AI agent optimization and deployment arise.
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Read at arXiv cs.AI