Data Agent: A Holistic Architecture for Orchestrating Data+AI Ecosystems (2507.01599v1)

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 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.

• The paper demonstrates a novel LLM-embedded architecture that enhances autonomous orchestration of data pipelines by improving semantic understanding and planning.
• It integrates components such as perception, reasoning, tool invocation, and continuous learning to manage heterogeneous data sources effectively.
• The framework's validation via iDataScience and DBA Agent benchmarks highlights its ability to optimize latency, cost, and operational efficiency.

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

The paper discusses a novel approach to integrating LLMs into Data+AI systems through a concept called "Data Agent." This architecture is designed to enhance the autonomous orchestration of data pipelines by improving the systems’ semantic understanding, reasoning, and planning abilities. The advent of LLMs offers a promising solution to address these challenges, allowing for more efficient data processing and management strategies.

Introduction

The primary challenge in traditional Data+AI systems is their heavy reliance on human expertise for orchestrating system pipelines and adapting to dynamic environments and requirements. While various data science tools exist, the orchestration of these tools within an optimized and efficient pipeline remains a significant hurdle due to the system's limited semantic reasoning capabilities. The proposed Data Agent framework addresses these challenges by embedding LLM capabilities within the architecture, enhancing semantic understanding, reasoning, and planning.

Figure 1: Challenges of Data+AI Systems.

Data Agent Architecture

The proposed architecture of the Data Agent integrates several components, including perception, reasoning and planning, tool invocation, memory, continuous learning, and multiple agent collaboration. This composite structure is designed to manage data-related tasks autonomously, with a focus on knowledge comprehension, automatic planning, and adaptive improvement.

Figure 2: Key Factors of Data Agents.

Data Understanding and Exploration

The Data Agent architecture necessitates efficient data understanding and exploration to facilitate seamless discovery and accessibility. Using a structured semantic catalog and data fabric, heterogeneous data is unified to support advanced querying capabilities. Additionally, the architecture supports various tools for data preparation and integration, essential for efficient pipeline execution.

Figure 3: Data Agents Framework.

Data Engine Understanding and Scheduling

Different data processing engines, such as Spark and DBMSs, are understood and coordinated by the Data Agent to execute complex tasks effectively. Profiling specific engine capabilities and coordinating them for task execution is crucial to maximize system efficiency.

Figure 4: Data Agents Architecture.

Pipeline Orchestration

The pipeline orchestration agent component plays a pivotal role in generating, optimizing, and executing pipelines based on user-input natural language queries. By leveraging LLMs’ understanding and reasoning capabilities, the Data Agent refines these pipelines to improve latency, cost, and accuracy, invoking engine agents for efficient execution.

iDataScience: Multi-Agent System for Data Science

iDataScience serves as a practical manifestation of the Data Agent framework, focusing on adaptively handling data science tasks by harmonizing diverse data agent capabilities.

Figure 5: Overview of iDataScience.

Offline Data Agent Benchmarking

This stage constructs a data agent benchmark that represents various data science scenarios. Essential data skills are extracted from data science examples, forming a hierarchical structure, which, coupled with probabilistic sampling and LLM-generated test cases, underpins comprehensive data agent evaluation.

Online Multi-Agent Pipeline Orchestration

Upon receiving a data science task, this stage exploits benchmark results and task embeddings to select optimal agents for various sub-tasks. Using LLMs, tasks are decomposed, refined, and executed in parallel, with dynamic adjustments based on intermediate results to achieve optimal outcomes.

Figure 6: Example of Data Skill-based Benchmark Construction.

Data Analytics Agents

The paper postulates the development of specialized data analytics agents for different data types, including structured, unstructured, and multi-modal data. Each agent type leverages the semantic understanding and reasoning capabilities of LLMs to enhance data processing, optimally orchestrating and executing complex analytics tasks.

DBA Agent

The DBA Agent focuses on automating database diagnosis via LLMs to manage multiple databases effectively. It autonomously acquires knowledge and generates diagnostic reports to accurately determine the root causes of database anomalies, thereby significantly enhancing operational efficiency.

Conclusion

In conclusion, the Data Agent provides a holistic framework to advance the orchestration of Data+AI systems by embedding LLM capabilities. By introducing modular components and specialized agents, it addresses core challenges, paving the way for more adaptable and efficient data processing ecosystems. The proposed architecture offers numerous opportunities for further research and application across diverse domains in AI and data management.