Data Agent: A Holistic Architecture for Orchestrating Data+AI Ecosystems

TL;DR

This paper introduces the 'Data Agent', a holistic architecture leveraging large language models to improve orchestration, reasoning, and planning in Data+AI ecosystems, addressing current limitations in semantic understanding and automation.

Contribution

It proposes a comprehensive data agent architecture that integrates LLMs for better understanding, reasoning, and automation in Data+AI system orchestration, with practical examples and open challenges.

Findings

Demonstrates the feasibility of data agents in various Data+AI tasks

Highlights the integration of LLMs enhances system reasoning and planning

Identifies key challenges in designing effective data agent systems

Abstract

Traditional Data+AI systems utilize data-driven techniques to optimize performance, but they rely heavily on human experts to orchestrate system pipelines, enabling them to adapt to changes in data, queries, tasks, and environments. For instance, while there are numerous data science tools available, developing a pipeline planning system to coordinate these tools remains challenging. This difficulty arises because existing Data+AI systems have limited capabilities in semantic understanding, reasoning, and planning. Fortunately, we have witnessed the success of large language models (LLMs) in enhancing semantic understanding, reasoning, and planning abilities. It is crucial to incorporate LLM techniques to revolutionize data systems for orchestrating Data+AI applications effectively. To achieve this, we propose the concept of a 'Data Agent' - a comprehensive architecture designed to orchestrate Data+AI ecosystems, which focuses on tackling data-related tasks by integrating knowledge comprehension, reasoning, and planning capabilities. We delve into the challenges involved in designing data agents, such as understanding data/queries/environments/tools, orchestrating pipelines/workflows, optimizing and executing pipelines, and fostering pipeline self-reflection. Furthermore, we present examples of data agent systems, including a data science agent, data analytics agents (such as unstructured data analytics agent, semantic structured data analytics agent, data lake analytics agent, and multi-modal data analytics agent), and a database administrator (DBA) agent. We also outline several open challenges associated with designing data agent systems.