AI Agents Are Failing and It's Almost Never the Model's Fault | Alberto Pan, Denodo
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Why are enterprise AI agents failing and why isn’t it the model’s fault?
The adoption by enterprise of both generative AI and agentic AI has been much slower than a lot of people anticipated. They don't trust the AI.
Most companies, most organizations, as you know, during the last two years they have been doing pilots around the AI, right? How long
are gonna be before all large enterprises are using agenda KI in all relevant processes.
I think it will take significant time because at the end of the day there are bottlenecks that in many cases are related to organization or even legal reasons in some cases.
Can you talk about the trust problem, uh what you guys I think call the trust gap? Why don't you start by introducing yourself, how you came to Denodo, what Donodo does. Yeah, absolutely.
Well, first of all, thank you. Thank you for having me, Craig. So yeah, I I am uh Alberto Pan, I am chief technology officer of Denodo. I'm also a member of the of the founding team. And Denodo is a is a global company today. We are headquartered in Palo Alto and we have offices in more than twenty five countries. But our story actually began in Acoruna, which is a a small city in the in the northwest of Spain. I'm actually I'm still based there today. I I am talking from From Akorunia today, and and uh here basically I'm leading uh our RD team. The majority of our RD team is is based here. And about my journey, my journey actually started in academia. I was for many years. I was for many years doing research on data management and actually the core technology that powers Denodo today uh grew out from that original research that me and and other members of the founding team of Denodo were doing.
And Denodo, uh describe w the the problem that you guys set out to solve and then the solution that you've come up with.
Denodo, first of all, is a data management company um that uh enables organizations, typically big organizations, to create a unified real-time uh access layer, data access layer across all their data sources, right? We typically call this a universal semantic layer because it provides the data in the language of the business. So it makes it easy for people, and of course, also today for AI agents, not only to get access to the data that they need, but also to understand how this data should be used in different business contexts, right? And and and a big difference between the nodo and traditional uh data management architectures is that the nodo does not force you to consolidate everything upfront uh in a central system, like right, like in traditional data warehouse or lighthouse architectures.
With the nodo, you can query the data where it lives. And this has a number of benefits. Uh first it it removes the the button. That are typically associated with centralization. Also allows accessing the data in real time where it lives, as I mentioned. and also allows users and agents to get access to all the data. Um because typically in in many organizations what you have in the central systems and data warehouses or really houses is only a small percentage of the data. So with the nodes you can actually get access to. To all the data. Uh and we call this uh typically um a logical data management. Because well you don't need total physical data replication, right? And the and the underlying technology that uh allows this is is is called in the market typically uh data virtualization.
And uh the semantic layer uh that allows uh both uh humans and AI agents uh to make queries in natural language or or w w how does that work?
Well um uh natural language is one of the of the interfaces supported and obviously we are using uh Gen AI for that. But actually you can also consume the data with more traditional interfaces. For instance, actually what we try to do is to deliver the data so it can be consumed with any tool. So for instance if you are accessing um the data products that you create with the nodo with tools like Power BI or Tableau, you you will probably access them using technologies like JDBC or ODBC, right? Technologies oriented to SQL. But if you are a data scientist and you want to access the nodo data with a notebook, maybe you could use a technology like Arrow.
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Chapters
8 chapters
1
Why are enterprise AI agents failing and why isn’t it the model’s fault?
0:00–5:51
2
What is the “trust gap” and how did the AI Trust Gap Report uncover it?
5:51–10:41
3
How does Denodo’s logical data‑management (data virtualization) differ from traditional warehouses?
10:41–16:26
4
Why is real‑time, fresh data essential for trustworthy AI decision‑making?
16:26–21:04
5
What are the two common traps (over‑centralization and ad‑hoc layers) that organizations fall into when scaling AI?
21:04–26:00
6
How does a universal semantic layer solve inconsistent semantics across hundreds of data sources?
26:00–31:39
7
What deployment options does Denodo offer to enforce consistent security and governance for multi‑agent AI?
31:39–36:03
8
What roadmap changes and future priorities did the Trust Gap findings trigger at Denodo?
36:03–41:34
Speakers
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