Agents of Data: AI Behind the Scenes

The technology landscape has shifted rapidly—from monolithic on-prem systems to the cloud, and now to the age of Artificial Intelligence. Yet, despite these leaps, the fundamental challenges of data silos, governance, and quality persist.

In a recent episode of the Agents of Data podcast, Angie Hastings, Senior Solution Architect at Matillion, sat down with Shashir Srivastava of TEK Systems Global Services. They discussed how organizations are navigating the transition from traditional big data to Agentic AI, and why the "garbage in, garbage out" principle matters now more than ever.

You can listen to the full conversation on the Agents of Data podcast here.

Data is the New Oil (But It Still Needs Refining)

Shashir notes that while the industry has moved from mainframes to the cloud, the cultural and technical friction of data silos remains a primary bottleneck. Departments often hoard data, leading to quality issues and governance nightmares.

The analogy of data as "the new oil" holds true, but only if you view it as a raw material that requires processing. For AI to function, it needs a clean foundation. If you feed an LLM poor-quality data, you get hallucinations and inaccurate insights.

To modernize your data strategy, you must prioritize:

• Integration: Breaking down silos between Salesforce, ServiceNow, and legacy systems.
• Quality: Ensuring data is accurate before it hits the model.
• Governance: Defining who accesses data and how it is used.

Real-World Use Cases: Handling Unstructured Data

The definition of "data" has expanded. It is no longer just rows and columns; it is PDFs, images, and emails. Shashir highlighted how organizations are using AI to tackle unstructured data challenges that were previously impossible to automate efficiently.

Inventory Management: A manufacturing client used AI to scan unstructured proofs of inventory (PDFs and images) to validate stock levels across North America. What used to take weeks of manual review is now processed in minutes using Cortex search engines and structured outputs.

Regulatory Compliance: Legal teams are using Large Language Models (LLMs) to digest thousands of pages of new government directives. The AI summarizes changes and recommends actions, allowing human experts to focus on strategy rather than reading documents.

The Rise of Agentic AI

We are moving past simple chatbots. The industry is entering the era of Agentic AI.

As Shashir explains, traditional AI is reactive—you ask a question, it gives an answer. Agentic AI is autonomous. It orchestrates a series of tasks to achieve an outcome.

Think of ordering a pizza. A simple bot might tell you the phone number. An Agentic system has:

• An agent to select your favorite order based on history.
• An agent to process the payment.
• An agent to coordinate delivery logistics.

These agents collaborate to complete the workflow without constant human intervention. In data engineering, this means agents that can autonomously validate data, identify outliers, and even self-heal pipelines.

Governance and the Human in the Loop

With great power comes the need for strict governance. You cannot let an autonomous agent run wild without guardrails. Shashir emphasizes the need for AI Governance Councils to monitor for bias, security risks, and hallucinations.

TEK Systems applies "reinforcement learning" to data quality. When an AI agent flags an outlier (like a special character in a name field), it asks the human operator for the correct action. Once you validate the decision, the agent learns and applies that logic automatically next time. This keeps the human in the loop while drastically reducing repetitive manual work.

The Matillion Advantage

As the industry shifts toward Agentic AI, your platform needs to support both high-code customization and autonomous capabilities. The Data Productivity Cloud is built to bridge this gap.

Matillion supports the ingestion and processing of unstructured data, allowing you to feed clean, governed data into LLMs. Furthermore, with Maia, your agentic data team, you can automate the heavy lifting of pipeline documentation, code generation, and error handling. 

Maia doesn't just suggest code; it acts as a force multiplier, allowing your team to focus on architecture and strategy rather than boilerplate scripting.

The future isn't about data engineers losing their jobs to AI. It is about evolving from building pipelines manually to orchestrating intelligent agents that build them for you.

Agentic AI represents a fundamental shift: from AI as a tool, to AI as a decision-maker within complex systems. 

For data leaders, the opportunity is clear: reduce bottlenecks, cut costs, and make data engineering a strategic advantage.

Looking to catch the full episode?

• Watch Agents of Data Episode 4 on YouTube here.
• Listen to Agents of Data Episode 4 on Amazon Music here.
• Listen to Agents of Data Episode 4 on Apple Podcasts here.
• Listen to Agents of Data Episode 4 on Deezer here.