The Rise of Self-Managing Data Pipelines

Author: Denis Avetisyan

New research details a platform that automatically converts raw data into formats ready for artificial intelligence, minimizing the need for human intervention.

The Dataforge system embodies a framework built not to resist entropy, but to channel it, structuring information as a mutable landscape rather than a static monolith.

Dataforge introduces an autonomous data agent platform utilizing large language models and hierarchical routing with feedback loops for automated data transformation and preparation.

Despite the increasing demand for AI-driven solutions in data-intensive fields, preparing raw data remains a significant bottleneck, requiring substantial manual effort and specialized expertise. This work introduces Dataforge: A Data Agent Platform for Autonomous Data Engineering, a fully autonomous system designed to address this challenge for tabular data. By leveraging large language models and a novel hierarchical routing system with dual feedback loops, Dataforge automatically cleans, transforms, and optimizes data into AI-ready formats—all without human intervention. Could such an approach unlock new levels of efficiency and accessibility in data science, enabling broader innovation across diverse domains?

The Inevitable Erosion of Manual Pipelines

Traditional data pipelines rely heavily on manual intervention for crucial data cleaning, alignment, and preparation. This demands significant labor and expertise to ensure quality and compatibility. Manual processes introduce opportunities for error and inconsistencies, fundamentally limiting the reliability of derived insights. The increasing volume and complexity of tabular data exacerbate these challenges, necessitating automated solutions capable of scaling to meet contemporary demands. Like ancient structures, pipelines built on manual effort will inevitably yield to data’s relentless advance.

An agentic workflow offers a conceptual alternative to traditional manual processes.

Without automation, organizations risk being overwhelmed, unable to unlock data’s potential value.

Dataforge: An Autonomous Agent for Data’s Evolution

Dataforge is an autonomous agent designed for end-to-end transformation of tabular data, automating the entire process – from data understanding to final output – without extensive manual intervention. Its architecture is founded on Perception-Planning-Grounding-Execution principles, enabling intelligent navigation and adaptation to diverse data types and user requirements. Dataforge aims to provide a safe, user-friendly experience, validating transformations and minimizing expert intervention, democratizing data science by making powerful tools accessible to a wider range of users.

Dataforge presents a user interface designed for data interaction and analysis.

This agent prioritizes accessibility and safety in data manipulation.

Hierarchical Routing: Decomposing Complexity into Manageable Steps

Dataforge employs Hierarchical Routing to decompose complex transformation tasks into manageable steps, automating data engineering by breaking down intricate processes. The system handles diverse data formats and transformation requirements without extensive manual configuration. It utilizes both a Rule-Based Router for initial task identification and an LLM-Based Planner for refining feature-level actions. The Rule-Based Router efficiently categorizes data, while the LLM-Based Planner leverages large language models to generate optimal sequences of feature engineering steps, adapting to each dataset’s characteristics.

Dataforge effectively addresses the challenge of heart-disease detection through its analytical capabilities.

Dataforge achieved an average predictive performance of 0.783 across nine datasets, comparable to or exceeding baseline models, demonstrating effective automated data engineering and a robust, generalizable approach.

Dual Feedback Loops: Architecting Resilience Against Entropy

Dataforge employs Dual Feedback Loops, creating a data workflow capable of adaptation and self-correction. This differentiates it from static pipelines by continuously monitoring data integrity and dynamically adjusting transformation processes. The system assesses both incoming data validity and output consistency, enabling proactive error mitigation. The Action Validation Loop grounds all data transformations through rigorous schema alignment and consistency checks, preventing data corruption.

Evaluation across nine datasets demonstrated Dataforge’s exceptional stability, maintaining a 0% failure rate throughout testing – a significant improvement over comparable models, which exhibited failure rates ranging from 3-5%. This underscores Dataforge’s capacity to manage complex data streams with a level of robustness often lacking in conventional systems.

Impact Demonstrated: Streamlining Heart Disease Prediction

Dataforge successfully automated feature engineering and data preparation tasks when applied to the SPECTF Heart Dataset, streamlining the manual process of transforming raw data. Employing Dataforge resulted in a measurable improvement in diagnostic accuracy, increasing model performance from 0.772 to 0.840, while reducing input data dimensionality from 44 to 20 variables.

Dataforge’s workflow completion time averaged 3.9 seconds, requiring only 2 API calls – a substantial efficiency gain compared to reinforcement learning-based baseline methods, which necessitated between 13 and 22 calls. This reduced computational burden suggests potential for real-time or near-real-time diagnostic applications.

Dataforge, in its pursuit of autonomous data engineering, embodies a fascinating tension between creation and entropy. The platform’s hierarchical routing and dual feedback loops strive for perpetual refinement, yet acknowledge the inherent decay within any complex system. This resonates with Donald Knuth’s observation: “Premature optimization is the root of all evil.” Dataforge doesn’t seek to eliminate the need for adaptation—the feedback loops are adaptation. Instead, it embraces the inevitability of change, building a system designed to gracefully manage technical debt and respond to evolving data landscapes. The system isn’t static; it’s a continuous negotiation with the passage of time, ensuring that even as data shifts, the core functionality remains robust and relevant.

What’s Next?

Dataforge, as presented, addresses a transient state. The automation of data transformation, while currently efficient, merely postpones the inevitable drift toward entropy. Each successful pipeline is a local reduction in disorder, yet the universe favors increasing complexity and, ultimately, decay. The platform’s hierarchical routing and feedback loops, while ingenious, are not immune to the accumulating latency inherent in any complex system. The question is not if these agents will require recalibration, but when, and at what cost to maintain the illusion of seamless flow.

Future work must confront the limitations of the underlying language models. The capacity for these agents to generalize beyond their training data—to adapt to novel data structures and unforeseen anomalies—remains a critical vulnerability. The pursuit of “AI-ready data” is itself a Sisyphean task; data is never truly ready, only temporarily suitable for a specific purpose, before requiring further refinement or, eventually, obsolescence.

Perhaps the most pressing challenge lies in understanding the emergent properties of these autonomous agents. As Dataforge scales—as more agents operate concurrently, interacting with increasingly diverse datasets—unforeseen conflicts and unintended consequences will arise. Stability is an illusion cached by time, and the long-term behavior of such systems will likely reveal unforeseen vulnerabilities—a reminder that even the most elegant architecture is ultimately subject to the relentless march of disorder.

Original article: https://arxiv.org/pdf/2511.06185.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/