Enterprise data management is undergoing a fundamental transformation. The traditional data stack built on rigid pipelines, static workflows, and human-led interventions is reaching its breaking point. As data volume, velocity, and variety continue to explode, a new approach is taking shape: agentic data management.

In this episode of Tech Transformed, EM360Tech’s Trisha Pillay sits down with Jay Mishra, Chief Product and Technology Officer at Astera, to explore why agentic systems powered by autonomous AI agents, Large Language Models (LLMs), and semantic search are rapidly being recognised as the next generation of enterprise data architecture.

The conversation explores the drivers behind this shift, real-world applications, the impact on data professionals, challenges faced by agentic platforms, and the future of data stacks. Jay emphasises the importance of starting small and measuring ROI to successfully implement agentic solutions.

What is Agentic Data Management?

At its core, agentic data management is the application of intelligent, autonomous agents that can perceive, decide, and act across complex data environments. Unlike traditional automation, which follows predefined scripts, agentic AI is adaptive and self-directed. These agents are capable of learning from user behaviour, integrating with different systems, and adjusting to changes in context, all without human prompts.

As Jay explains, "An agentic system is one that has the agency to make decisions, solve problems, and orchestrate actions based on real-time data and context, not just on training data.

Takeaways

• Agentic data management is the next evolutionary step in data architecture.
• Agents are autonomous and can make decisions on the fly.
• The demand for agentic solutions is increasing due to data volume and AI strategy needs.
• Maturity of foundation models enables near-human reasoning capabilities.
• Real-world applications of agentic AI include insurance claim processing.
• Data engineers will focus on policy and guardrail creation rather than coding.
• Governance, debt and hallucinations are significant challenges in agentic platforms.
• The future of data stacks will include declarative control plans and enhanced memory layers.
• Analysts will play a crucial role in defining policies for agentic systems.
• Starting small and demonstrating ROI is key to successful agentic implementation.

Chapters

00:00 Introduction to Agentic Data Management

02:58 Understanding Agentic Data Management

06:58 Drivers of Change in Data Management

10:03 Real-World Applications of Agentic AI

14:15 Impact on Data Engineers and Analysts

16:43 Challenges and Limitations of Agentic Data Platforms

20:03 Future of Data Stacks

23:31 Final Thoughts on Agentic Data Management

About Jay Mishra

Jay Mishra is the Chief Product and Technology Officer at Astera Software, with over two decades of experience in data architecture and data-centric software innovation. He has led the design and development of transformative solutions for major enterprises, including Wells Fargo, Raymond James, and Farmers Mutual. 

Known for his strategic insight, technical leadership, and passion for empowering organisations, Jay has consistently delivered intelligent, scalable solutions that drive operational excellence and financial success.

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