By AI agents deployed inside enterprises are producing inconsistent, unreliable answers, not because the underlying models are weak, but because the data those agents need is not available where it is needed. Without a coherent semantic layer and complete data integration, ten employees asking the same agent the same question may receive five or six different responses. For teams betting on agentic workflows to drive real business outcomes, this is a foundational infrastructure problem that no model upgrade will fix.
In this interview on TFiR, Mario Moscatiello, VP of Growth at Airbyte, covers why data connectivity has replaced model selection as the primary bottleneck for enterprise AI readiness, and how Airbyte is building a fully agentic data platform to close that gap.
Guest: Mario Moscatiello, VP of Growth at Airbyte
Show: TFiR
Here is what every data engineer and AI platform team needs to know.
Technical Deep Dive
Q: Is the AI model itself still the primary bottleneck for enterprise AI deployments?
Mario Moscatiello, VP of Growth at Airbyte, argues that the model problem has been largely solved. New models, both closed source and open source, general and specialized, are releasing continuously and improving rapidly. The constraint that is actually blocking enterprise AI deployments from delivering reliable results is not model capability but data availability and data connectivity.
“Every week and every month we have new models that are coming out and they’re better and better. I would say the model problem has been largely solved.” — Mario Moscatiello, VP of Growth, Airbyte
Q: What is the relationship between data maturity and AI readiness in enterprise organizations?
Moscatiello draws a direct line between data maturity and willingness to adopt AI. Companies that are highly data mature tend to be the first to embrace AI because they can trust their data. Organizations at lower data maturity levels struggle to deploy agents effectively regardless of which model they use, because the data those agents require simply does not exist in accessible, integrated form.
“Companies that are highly data mature are usually the first to want to be embracing AI because they can trust their data.” — Mario Moscatiello, VP of Growth, Airbyte
Q: Why are AI agents hallucinating inside large enterprises even when using strong models?
Moscatiello identifies the root cause as missing data integrations. The data that agents need to reason over is not available in the data warehouse or in the integration layer, so agents fill that gap with plausible-sounding but incorrect responses. Compounding this is the absence of a semantic layer: without one, the same question asked by ten different people inside a business can produce five or six different answers from the same agent.
“Data connectivity is what’s causing a lot of agents to hallucinate because they think that they’re giving the right answers to a problem. But there is no notion of semantic layer.” — Mario Moscatiello, VP of Growth, Airbyte
Q: Does the enterprise data connectivity problem only affect smaller or less mature companies?
Moscatiello is explicit that this problem spans the full enterprise spectrum. Working with organizations from early-stage startups to Fortune 500 companies, the Airbyte team consistently finds that even very large enterprises lack the data integrations their agents would need. Scale and resources do not automatically produce data readiness.
“Even in the case of very large enterprises, a lot of the data that these agents would need to work on is actually not available in the data integrations or in their warehouses or in their systems.” — Mario Moscatiello, VP of Growth, Airbyte
Q: What is Airbyte building to solve the enterprise data connectivity bottleneck for AI agents?
Moscatiello describes Airbyte as building a fully agentic platform on top of six years of data movement and data integration work. The platform is designed to ingest data from many sources, organize it in a way that makes contextual sense, and then route it back into the operational systems where work actually happens. The goal is to help organizations assemble context so that their agents can reason and act accurately.
“We’re building a fully agentic platform, building on top of six years of data movement and data integration to say, how do we help companies assemble context so that their agents can work in a smart way?” — Mario Moscatiello, VP of Growth, Airbyte
Resources & Documentation
- Airbyte, open-source and cloud data integration platform for building data pipelines and powering AI agent context
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👇 Click to Read Full Raw Transcript
Swapnil Bhartiya: AI or Genai or agentic AI, it has kind of become the only topic that we talk here at tfm. No matter who I talk to, AI is the only topic. And what we have realized so far is that almost every enterprise, they have no shortage of AI models where the struggle is to actually their own enterprise data, enterprise context. If I ask you, based on your interaction, do you also feel that data connectivity is becoming a real bottleneck for agentic models as compared to the model themselves? What are you seeing when it comes to model versus data?
Mario Moscatiello: I think every week and every month we have new models that are coming out and they’re better and better. And you go from general models to very specialized models, whether they’re closed source or open source. So I would say the model problem has been largely solved. I think what you’re seeing is that if you look at companies, whether they’re small companies or large companies and enterprise and organizations, they sort of have, you know, two things. You look at data maturity and you look at AI readiness. And yes, in a lot of the cases, companies that are highly data mature are usually the first to want to be embracing AI because they can trust their data. But in a lot of companies, and you know, at everybody we work from startups to Fortune 500, we see that even in the case of very large enterprises, a lot of the data that these agents would need to work on is actually not available in the data integrations or in their warehouses or in their systems. And so I think data connectivity is what’s causing a lot of agents also to hallucinate because they think that they’re giving the right answers to a problem. But there is no notion of semantic layer. If 10 people in the business were to ask an agent the same question, they would probably get five or six different answers. And so we’re really seeing that becoming a huge bottleneck for companies. And that’s definitely something that we’re here to help with.
Swapnil Bhartiya: Excellent. And what is Arbyte doing to address this problem?
Mario Moscatiello: So in a way what we’re seeing is that because organizations have different levels of data maturity, what we’re building is really a system that can take a lot of data from different sources for companies and sort of organize it in a way that makes sense and then helping those companies put that bit, put that data into action and put that data where it needs to work so back into the systems that they use every day. So we’re building a fully agentic platform in that sense, where we are building on top of six years of data movement and data integration to say, hey, how do we help companies assemble context so that their agents can work in a smart way?
