arXiv:2606.01185v1 Announce Type: new Abstract: Coding agents are becoming users of data infrastructure, but their success depends not only on model quality: it also depends on the skills and environment files that teach agents how to use a system. We study how to optimize these artifacts for agents operating on a branching lakehouse, Bauplan. In our setting, headless APIs and Git-like data primitives expose data workflows through code, branches, commits, and merges. Our central observation is that a branching lakehouse turns data-agent evaluation from an output-matching problem into a state-v
The proliferation of coding agents necessitates effective data infrastructure interaction, making optimization of their 'skills' and environment files a crucial, immediate challenge as these systems move from research to deployment.
Optimizing how AI agents interact with complex data systems directly impacts their efficiency and reliability, which is critical for scaling autonomous workflows and realizing the full potential of agentic AI.
The focus for AI agent performance expands beyond model quality to include the quality of their environmental configurations and learned 'skills', shifting optimization efforts towards data-centric approaches for agent operations.
- · AI agent developers
- · Data infrastructure providers
- · Companies adopting autonomous agents
- · Inefficient AI agent solutions
- · Organizations with siloed data systems
Increased efficiency and effectiveness of AI agents in data-intensive tasks.
Faster development and deployment cycles for complex agentic systems across various industries.
New paradigms for human-agent collaboration as agents become more adept at autonomous data manipulation.
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Read at arXiv cs.AI