In today’s fast-evolving technological landscape, organizations are increasingly looking to integrate AI agents to supercharge their processes. But here’s the often-overlooked truth: the success of these intelligent agents hinges entirely on the quality and accessibility of your data. As Niels Zeilemaker, global CTO at Xebia, aptly puts it, “Agentic AI scales on data strength.”
Without a robust data foundation, even the most sophisticated AI agent is bound to stumble. It might struggle to locate the correct information, misinterpret crucial details, or even mistakenly link disparate data fields. Zeilemaker emphasizes that these aren’t typically agent failures, but rather symptoms of an underlying foundation unprepared for AI’s demands.
The Crucial Role of Data Readiness for AI Agents
One area that demands particular attention, according to Zeilemaker, is data cataloguing. While not a new concept, its significance amplifies dramatically when dealing with AI agents. For human teams, an incomplete or poorly documented data catalogue often has a workaround; you can always “pick up the phone, walk to a colleague,” and clarify ambiguities.
However, AI agents lack this human fallback mechanism. They are entirely reliant on the documented data catalogue, and any inaccuracies or insufficient descriptions directly impede their performance. A flawed catalogue means flawed agent output, highlighting the absolute necessity of precise and comprehensive data documentation.
Xebia’s Approach: Building an Agentic Data Foundation
Xebia is at the forefront of helping organizations translate ambitious AI strategies into tangible, production-ready solutions that drive real transformation. The company prides itself on core values like being people-first and committed to quality without compromise. Crucially, Zeilemaker views knowledge sharing as their most vital principle, exemplified by their participation in industry events like TechEx Global North America.
This commitment to sharing insights allows Xebia to stay ahead of market changes, fostering an environment where innovation thrives. By actively pushing knowledge exchange, Xebia aims to establish authority in key domains, with data and AI being a clear priority. Their expertise was showcased at the AI & Big Data Expo, where Zeilemaker detailed how to unify fragmented data landscapes and build solid AI foundations.
This comprehensive approach is encapsulated in what Xebia calls the Agentic Data Foundation (ADF). ADF extends existing data platforms to seamlessly host AI agents, enabling their use in both customer-facing applications and internal operational processes. Xebia has observed a growing demand from customers for faster and more reliable migrations to modern data platforms, moving beyond traditional methods.
The ADF framework, often developed in close co-development with clients, leverages accumulated experience from past migrations. By integrating lessons learned from accelerating legacy transformations with LLM coding directly into the data platform, ADF provides additional context that further speeds up the migration process. This makes Xebia Axis: Agentic Data Foundation a powerful answer to enterprises seeking to make their data AI-ready faster than ever before.
Revolutionizing Software Development with AI-Native Engineering
Beyond data foundations, Xebia offers another transformative solution: Xebia ACE: AI-Native Software Engineering. This innovative framework integrates AI throughout an organization’s entire software development lifecycle (SDLC). When implemented effectively, ACE can accelerate delivery by an impressive up to 40%, while simultaneously cutting legacy transformation costs by up to 70%.
Xebia ACE is particularly beneficial for larger enterprises that need to maintain specific governance structures and established ways of working within their SDLC. Zeilemaker highlights the concept of “vibe coding” – where individuals can quickly create apps but are hesitant to push them to production due to quality concerns. ACE addresses this directly.
By adopting ACE, organizations gain the immense acceleration benefits of Large Language Models (LLMs) in coding, without compromising on the quality of the end results. This framework ensures the same high standards enterprises are accustomed to, mitigating the risks associated with unguided LLM usage. Xebia ACE provides a secure and structured path to integrate LLMs into coding, preventing a loss of control or governance in the process.
Navigating the Future of AI-Driven SDLC and Security
The rise of AI-generated code introduces new considerations, especially concerning security. With vast amounts of code being produced at speed, the AI-driven SDLC could potentially become a source of vulnerabilities. Zeilemaker acknowledges that the industry is still evolving in how it addresses these new challenges.
He notes with interest recent advancements, such as Anthropic’s release of an AI-powered pull request reviewer. Zeilemaker anticipates seeing more of these sophisticated tools in the future, where “a very senior team member in the form of an LLM” acts as a third-party reviewer for lengthy pull requests before a new production release. This represents an exciting development in maintaining code quality and security.
Ultimately, regardless of where organizations stand in their AI journey – whether they are assessing data readiness or poised for extensive AI solution building – Xebia is equipped to guide them. By establishing robust foundations, Xebia empowers enterprises to confidently build transformative AI solutions on top, ensuring both speed and reliability.
Source: AI News
