Why Data Teams Are Best-Positioned For Agentic AI Success With Data Integration and MCPs

Building AI agents is the first step, and it’s positive to see enterprises exploring this avenue. But it’s only the first step. For true enterprise value, these agents must seamlessly connect to your data ecosystem through robust integration, standardized protocols, and be guided by knowledgeable data teams.

The need to give AI agents access to data and connect them to the necessary tools and functions has led to the creation of the Model Context Protocol (MCP). MCP allows agents to interact with tools and data sources using a common language, taking AI from generation to execution (more on that later).

And the people best positioned to build and manage these agents? Often, it’s those who already understand the company’s data, systems, and workflows. Their knowledge of how everything fits together, from backend processes to business rules, gives them a head start in designing agents that are competent and dependable.

In this article, we explore how data teams can leverage data integration and MCP’s execution abilities to achieve agentic AI success.

Data Integration for Agentic AI: The Secret Ingredient

Your BI or analytics platform delivers value when it gets high-quality data to work with. The same holds true for your AI agents.

The data integration solutions we’ve built here at Astera have helped enterprises integrate disparate data sources, intuitively pulling data from native apps, cloud services, databases, and more. This has allowed our customers to power their business intelligence (BI) and analytics initiatives while automating manual workflows using technologies like ETL (Extract, Transform, Load), OCR (Optical Character Recognition), and Data Warehousing.

As a result, enterprises that have streamlined their data management are poised for agentic AI success. These same technologies can be used to give AI agents real-time access to enterprise data so they can reason, decide, and act in meaningful ways.

“Organizations face challenges in integrating AI data due to information silos, uneven data quality, and ineffective procedures. To address these issues, strategic changes like implementing advanced data management technologies, promoting cross-departmental cooperation, and adopting a data-centric approach are needed.”

-Mariyono Dwi & Akmal Nur Alif Akmal in “Strategic Overhaul: Reframing Data Integration for Optimal AI Utilization”

Model Context Protocol (MCP): The Common Language

So far, we’ve talked about how the real value of agentic AI lies in how well it can interact and integrate with external tools and data sources. Until recently, this was done through Application Programming Interfaces (APIs). However, Anthropic introduced a new open standard protocol late last year that standardizes how language models interact with external data and tools.

Martin Keen describes it best as MCP being a USB-C port for your AI agents. Just as USB-C lets you seamlessly connect different types of peripherals to your computer, MCP does the same for your agents. It standardizes connections between your agents, LLMs, external data sources, and systems.

While MCPs and APIs share several similarities, certain fundamental differences make MCPs the better choice for all things AI. For instance, MCPs are purpose-built for LLMs and agents, whereas APIs are more of a general-purpose interface. This leads to MCPs having capabilities such as runtime dynamic discovery, which lets agents find, integrate, and leverage new capabilities in real-time.

Plus, MCPs standardize the interface, which means that every MCP server speaks the same protocol (as opposed to APIs, which are unique, and their protocols vary between services).

MCP becoming the standard protocol for all agent interactions opens up a whole new world of possibilities for how much your agents can do and how easily they can scale. The only real challenge, then, is how well you can integrate your data sources and applications into your agentic workflows.

Build AI with Data Engineers: The Right People

Finally, let’s discuss who in an organization is best equipped to build these AI agents. Our first instinct might be to point to the team of developers who can write the necessary code.

However, this approach undermines the value that can be derived from AI because our technical resources aren’t necessarily our data experts. Instead, if we empower data experts from different domains to build agents, we make AI a core part of the company ethos, encourage organization-wide adoption, and facilitate value-adding AI ideation and execution.

Since the success of AI agent development hugely depends on data integration and MCPs, the onus is on data engineers to craft agentic workflows that can deliver consistently and reliably.

Final Thoughts

To summarize our discussion so far, the strategic advantage of agentic AI lies in:

• Integrating internal data sources, systems, applications, and more,
• leveraging MCPs to facilitate connectivity with external tools and functions, and
• empowering data experts to build AI agents and applications that drive real ROI.

To extract this advantage, a new approach to building AI agents and applications was needed, which led us to Astera AI Agent Builder.

With Astera, enterprises can do all of this through a unified, visual platform that lets technical and non-technical users build, test, and deploy AI applications in hours.

However, what sets Astera AI Agent Builder apart is our organization’s data integration foundation. By leveraging the capabilities of Astera Data Stack, the Agent Builder lets enterprises effortlessly integrate their AI applications with internal data sources, systems, cloud resources, and external tools and applications.

Join the waitlist for early access or view our product’s first look here.