​​The Path to Agentic intelligent Data Engineering​

Written by Siddharth Mohanty, Director, Data Management

Evolving Agentic AI Landscape 

• ​Artificial intelligence (AI) is developing quickly, moving from basic assistants to self-governing systems that can act and make decisions with little human supervision. 

• ​Generative AI, AI agents, agentic AI, and now the Model Context Protocol (MCP), a ground-breaking framework that standardizes how AI communicates with outside tools and data sources, have all been steps in this journey. 

• ​Conventional generative AI was proficient at producing text, graphics, and code, but it lacked autonomy at its core. This gap is filled by agentic AI, which uses generative capabilities to allow for truly autonomous action.​

With a keen focus on the rapidly evolving field of artificial intelligence, we, at Fresh Gravity, have created a novel approach to using AI to create dependable and scalable data engineering solutions. Our vision of a future–ready data engineering solution represents a next-generation approach to building, automating, and optimizing data workflows by leveraging LLM and AI agents to act intelligently and autonomously throughout the data lifecycle.  This blog is a deep dive into one such proprietary solution built by Fresh Gravity – Agentic intelligent Data Engineering (AiDE) solution.

​AI Helping Make More Data Become Better Data

​​With key characteristics such as autonomy, goal-oriented execution, adaptability, multi-agent collaboration and integration with tools, AI agents come power-packed to alleviate complexity and manual overhead. Key capabilities of AI Agents include:

• ​​Operational Efficiency: Automation of repetitive processes like data entry, purification, and validation, agentic AI improves operational efficiency.  Batch and live ingestion operations are coordinated through data pipeline optimization.  Schemas are dynamically modified in response to patterns found.  Agents maintain data integrity across systems, optimize queries for speed and cost effectiveness, and keep an eye on warehouse performance 

• ​Increased Accuracy: By fusing the accuracy of traditional programming with the flexibility of large language models (LLMs), agentic AI can increase accuracy and precision and make better judgments depending on context and real-time data. Agents can examine large datasets, find inefficiencies in workflows, and spot anomalies without human involvement thanks to live analytics.  

• ​Improved data quality: Via intelligent monitoring and validation. Fresh Gravity’s AiDE comes powered with Data Quality, Data Reconciliation, and Data Observability agents designed to monitor data and pipelines to detect data discrepancies. AiDE has Data Quality and Observability Dashboards OOTB, providing real-time insights 

• ​Real-time Insights: AI agents can be designed to continuously profile incoming data, analyze patterns, offer real-time insights, and identify trends  

• ​Optimized Performance: Agentic AI effectively manages large-scale data environments, adapting to increasing volumes and complexities Agentic AI effectively manages large-scale data environments, adapting to increasing volumes and complexities. 

• ​Enhanced Compliance and Risk Management: This is achieved by developing models and agents to deploy policies and rules into execution. For example, we have built agents to take specific actions while processing PII data elements. 

​AI Agents Powering Data Engineering

​​Fresh Gravity’s AiDE solution accelerates the data engineering ecosystem by intelligently automating repetitive operations and providing AI-powered tools with the ability to effectively build, maintain, and improve data pipelines, thereby reducing the responsibilities of data professionals. These improvements are achieved by using AI Agents to bring efficiency gains, as listed in the earlier section, across the data engineering life cycle. These models are already proficient in debugging, optimization, and generating Python and SQL code, and their capabilities are expanding daily.  
From making raw data understandable to integrating, coordinating, and automating heterogeneous data into AI operations, data engineers are now at the vanguard of the AI movement. 

​Fresh Gravity’s Agentic Data Engineering Solution 

AI-driven Data Engineering is quickly shifting from trend to necessity, driven by the need for adaptability, speed, operational excellence, and insights at scale. By embedding intelligence throughout the data pipeline, organizations can innovate more quickly, reduce costs, mitigate risks, and lay the groundwork for the future of data-driven decision-making. 

​We have developed a one-of-a-kind AI-powered Data Engineering packaged solution to address all facets of a data solution (data pipelines development, data discovery, audit & control framework, data quality and reconciliation, orchestration and workflow management, data observability), Agentic intelligent Data Engineering (AiDE), an AI-powered smart and intelligent solution packaging a suite of agents performing data discovery, data ingestion, orchestration, execution, data validation, monitoring and observability. 

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​AiDE comes prepacked with code templates and coding standards with audit features enabled Out-of-The-Box (OOTB). Data Engineers can define the interfacing of source data systems with AiDE and, through a smart prompt, provide the AI model with context to discover data, develop pipelines, create audit table entries, execute pipelines, perform QC on data executions, and build operational dashboards for observability of the workload. 

​AiDE also comes with a Human-In-The-Loop (HITL) framework to incorporate business/domain-specific data processing logic into the pipelines. All data pipelines created with AiDE follow our in-house core design principle of a Metadata-Driven Framework (MDF), ensuring extensibility and flexibility by design for every artifact that gets delivered. 

​To know more about building performant data products and solutions on-premises or on cloud, please write to us at info@freshgravity.com.