Crucial Role of Data Foundation in AI Agent Success: Lessons from Xebia

The Importance of Data Foundation for AI Agents

When it comes to integrating AI agents into your organization to boost operational efficiency, it all starts with laying a strong foundation – which primarily involves ensuring that your data is readily available for AI consumption. Niels Zeilemaker, the global CTO at Xebia, emphasizes the critical role of data strength in the scalability of Agentic AI.

Zeilemaker highlights that neglecting to prepare your data for AI utilization can lead to significant issues. He explains, “If you don’t think about that, you can build the best agent, but it will never be able to find the correct data; maybe it will misinterpret the data, maybe it will join different fields together in your data which should never be connected. And these mistakes are not necessarily the fault of the agent. It’s the fault of your foundation, which is not ready for AI agents.”

One crucial aspect to consider, according to Zeilemaker, is data cataloguing. While not a new concept, the dynamics change significantly for AI agents. Zeilemaker points out, “If you’re setting up a data catalogue for an organisation only consisting of humans, there’s always a fallback. If there’s something not really well documented, you can pick up the phone, walk to a colleague, and have a sort of back door, in ‘how should I work with this particular set of data?’”

However, the scenario is different for AI agents. Zeilemaker stresses, “Agents don’t have such a back door. They have to rely on the data catalogue, what’s written there, and if the description is wrong, the agents will not perform.”

Xebia’s Approach to AI Strategy Implementation

Xebia is dedicated to assisting organizations in translating their AI strategies into practical, production-ready solutions that drive tangible transformation at an accelerated pace. The company’s core principles revolve around prioritizing people, maintaining uncompromised quality, and above all, fostering knowledge sharing – a practice exemplified at events like TechEx Global North America, where Xebia actively participates.

Zeilemaker underscores the significance of knowledge sharing within Xebia’s ethos, stating, “I think sharing knowledge is very important for us, and it also allows us to be a bit ahead of the curve, adopt quickly to new changes in the market, because everybody has this eagerness to find out new things, and to share what works, what doesn’t work. By pushing a lot into this sharing knowledge and innovation, we try to also pick a couple of domains where we want to be the authority.”

Data and AI emerge as focal points within Xebia’s expertise. At the AI & Big Data Expo, Zeilemaker provided insights on establishing a robust AI foundation and streamlining fragmented data landscapes. He emphasized how combining purpose-built AI agents with expert engineering can compress a 12- to 24-month timeline into a fixed-price, milestone-bound engagement.

“Agents have to rely on the data catalogue and what’s written there – and if the description is wrong, the agents will not perform”

Zeilemaker elaborates on Xebia’s innovative solution, the Agentic Data Foundation (ADF), which extends the data platform to accommodate agents, leveraging them for both customer-facing applications and internal processes. While there has been a longstanding interest in transitioning from legacy to modern platforms, Xebia is witnessing a growing demand for a swifter and more reliable approach to migrating data platforms. Zeilemaker emphasizes the collaborative nature of co-developing solutions with clients in this context.

Xebia’s Innovative Solutions

Drawing from their extensive experience, Xebia has developed Xebia Axis: Agentic Data Foundation, aimed at helping enterprises prepare their data for AI applications in a faster and more efficient manner compared to other alternatives.

Additionally, Xebia offers Xebia ACE: AI-Native Software Engineering, a framework that integrates AI throughout an organization’s software development lifecycle (SDLC). This approach can accelerate delivery by up to 40% and reduce legacy transformation costs by up to 70% when implemented correctly.

Zeilemaker highlights the utility of Xebia ACE for larger enterprises seeking to maintain specific governance standards while enhancing their SDLC processes. He exemplifies the concept of vibe coding and how adopting ACE can deliver accelerated results without compromising on quality.

Furthermore, Zeilemaker emphasizes the importance of maintaining control, particularly in the context of the AI-driven SDLC. With the rapid generation of code, security vulnerabilities can pose a significant risk. While the industry is still navigating this challenge, Zeilemaker acknowledges promising developments such as Anthropic’s pull request reviewer, which adds an additional layer of review to ensure code quality.

Conclusion

Regardless of where organizations stand in their AI journey, Xebia stands ready to assist in establishing a solid foundation for AI implementation and driving transformative outcomes. With a focus on sharing knowledge, innovation, and expertise in data and AI domains, Xebia continues to lead the way in helping enterprises leverage AI technologies effectively.

Photo by fabio on Unsplash

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