Xebia：適切なデータ基盤なしでは AI エージェントは失敗する理由

If your remit is to help your organisation add AI agents to accelerate its processes, you have to start at the foundation – and that means making your data available for AI consumption. Agentic AI scales on data strength, as Niels Zeilemaker, global CTO at Xebia, 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,” explains Zeilemaker. “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 area to particularly consider, Zeilemaker notes, is data cataloguing. It’s not a new concept, but the game changes for agents. “If you’re setting up a data catalogue for an organisation only consisting of humans, there’s always a fallback,” he says. “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?’

“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 focus is to help organisations turn AI strategy into production-ready solutions which drive real transformation faster. The company’s core values include being people first and quality without compromise, but perhaps the most important, as Zeilemaker sees it, is sharing knowledge – such as at events like TechEx Global North America, at which Xebia participated.

“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,” says Zeilemaker. “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 is evidently one of those areas. At AI & Big Data Expo, Zeilemaker told attendees how to build this AI foundation and unify their fragmented data landscapes. It was an honest account of how combining purpose-built AI agents with expert engineering compresses a 12- to 24-month timeline into a fixed-price, milestone-bound engagement.

The overarching thread for this is what Xebia calls Agentic Data Foundation (ADF), which extends the data platform to host agents, and then make use of them both in customer-facing use cases and internal processes. While there has always been a big appetite in migrating from legacy to modern platforms, Xebia is seeing more customers asking for an approach to more quickly – and reliably – migrate into data platforms. Zeilemaker says this is where consultant and customer are co-developing the solution.

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

“After doing migrations the old-fashioned way, and accelerating some with LLM coding, we are now integrating this into the data platform, making use of the additional context it can provide to accelerate migrations even further,” he says.

That accumulated experience is what shaped Xebia Axis: Agentic Data Foundation, Xebia’s answer to helping enterprises make their data AI-ready faster than any alternative.

Another weapon Xebia has in its arsenal is Xebia ACE: AI-Native Software Engineering, a framework which embeds AI across an organisation’s entire software development lifecycle (SDLC). Done right, delivery can be accelerated by up to 40%, while legacy transformation costs are cut by up to 70%.

Zeilemaker notes that Xebia ACE is particularly useful for larger enterprises who ‘maybe still want to stick to a particular governance or way of working while doing SDLC’. Yet there is a bigger picture here. Zeilemaker uses vibe coding as an example. “If you think about vibe coding, everybody can create an app, but nobody is daring to actually push these apps into production,” he says. “If you adopt ACE, you still get a lot of the benefits of the acceleration of LLMs, but you’re still having the same quality end results as you’re used to in the past.

“If you’re looking to make the switch to using LLMs in coding, Xebia ACE will give you a very nice framework to use, without the risk, or any drawbacks of doing dark factory LLM and hoping for the best – and losing a bit of control or governance in the process,” adds Zeilemaker.

For enterprises, that control is key. With so much code being generated, the AI-driven SDLC could become a security weakness through vulnerabilities. Zeilemaker argues it’s something the industry still needs to figure out to a degree, but notes with interest the recent move by Anthropic to release a pull request reviewer.

“It’s an interesting one, which we’ll probably see more of,” he says. “There will be very lengthy pull request reviews, which you apply whenever you go and try to do a new production release. And then you add a very senior team member in the form of an LLM to your process, which does a sort of third-party review.

“I think that’s an interesting angle with what we’re going to see more of in the future.”

Ultimately, wherever organisations are in their journey, from assessing their data readiness to being ready to build, Xebia is able to help get the foundations right – and create the transformations on top of it.

Photo by fabio on Unsplash

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