  Artificial Intelligence (AI) significantly influences many fields, largely
thanks to the vast amounts of high-quality data for machine learning models.
The emphasis is now on a data-centric AI strategy, prioritizing data
development over model design progress. Automating this process is crucial. In
this paper, we serve as the first work to introduce the automatic data-centric
development (AD^2) task and outline its core challenges, which require
domain-experts-like task scheduling and implementation capability, largely
unexplored by previous work.
  By leveraging the strong complex problem-solving capabilities of large
language models (LLMs), we propose an LLM-based autonomous agent, equipped with
a strategy named Collaborative Knowledge-STudying-Enhanced Evolution by
Retrieval (Co-STEER), to simultaneously address all the challenges.
Specifically, our proposed Co-STEER agent enriches its domain knowledge through
our proposed evolving strategy and develops both its scheduling and
implementation skills by accumulating and retrieving domain-specific practical
experience. With an improved schedule, the capability for implementation
accelerates. Simultaneously, as implementation feedback becomes more thorough,
the scheduling accuracy increases. These two capabilities evolve together
through practical feedback, enabling a collaborative evolution process.
  Extensive experimental results demonstrate that our Co-STEER agent breaks new
ground in AD^2 research, possesses strong evolvable schedule and implementation
ability, and demonstrates the significant effectiveness of its components. Our
Co-STEER paves the way for AD^2 advancements.
