The Hidden Cost of Multi-Agent Chaos Hitting Enterprise Budgets | Mario Moscatiello, Airbyte

Of course, if you look at Airbyte, and I have been covering Airbyte from the very early on. So, I have seen your journey. Now, you folks are giving agents multiple ways to interact with enterprise systems, NCP, SDK, now CLI, and of course availability in OpenAI app marketplace, and it will be available in other places as well. How do you see these interfaces evolving? The AI market is changing very fast, so it's hard to say that. But, just, you know, based on your interaction, based on your own usage, because today everybody is using AI either way, will you Do you see one will win out as the standard, or there will be a fragmentation, there will be a mess where enterprises will have to deal with all of them, depending on the kind of agents they need? Also, a lot of players, even Anthropic, they have launched managed agents, because they all understand that agents are becoming a their big part of the problem for enterprises to manage. >> I I think you you hit the nail on the head, right? I think it's different people within the organization will will interface, will use different interfaces for different types of work. I think if you're in a marketing team and you're using Claude or OpenAI, and you want to use an MCP because that's the interface that's your interface for work, that's great. If you're an agent If you're a developer and you're building an agent, and you need the data SDK, and you're using the SDK, that's great. You're building a custom agent for a specific thing, or you're building an application that has agents in the back, that's great. And, you know, if you're again an an engineer working out of your terminal, and you want to have access to a command line interface to manage everything you do in this Is a Gentic application, that's okay, too. And, I think that what's the interesting thing is that something like we do with Airbyte agents is we can say, "Look, we know that enterprises need access to different interfaces for different classes of agents. But what shouldn't change is the data infrastructure behind them. So that if your marketing team is using the MCP and you know, they're querying data or your developers are using the SDK and the CLI, they should read from the same data. Like if you know, at the end of the day the marketing team is marketing person is building an agent that should answer how much revenue did we do yesterday and an engineer in doing it with the MCP and an engineer is trying to do the same with the SDK because they're building a revenue agent to that question like the agent whether it's you know, question is asked from an NCP or from an SDK like the answer should be the same. And so like what we can say is like look, it doesn't matter whether your teams are querying from our MCP or building something custom with the SDK or the CLI, we can we want to make sure that they're reading and and writing from the same data so that everybody in the business speaks the same language. >> How much how much say developers have when it comes to AI versus how much is the top one because that also dictates the kind of framework you're building, the kind of tools you're using, the kind of AI you're allowed to use or not use. >> I think in smaller companies developers have the freedom to experiment. Usually you know, smaller companies are more flexible and they tend to have you know, more usage-based pricing when it comes to like yeah, you can you can experiment with a bunch of different providers and so on and so forth. I think when when the companies like start scaling, you have you know, companies like Uber saying wait on a second like it's I don't remember the exact month but they were saying hey, it's March or April and we already used all of our token budget for a year. We need to put some governance in and so in that sense if you have you know, the board and the executives, they probably go and negotiate with one of the providers and say hey, like if we were to deploy your models across the entire companies, what is the pricing? And so, I think from a cost perspective, larger companies will tend to have more top-down, uh you know, in in that sense because these models are expensive and these providers like are are are expensive. And so, they need to put in in place again, we go back to governance. Like it's not only governance for the data side, but it's also governance for how much you're spending and how much many tokens are using and are they getting ROI. But, I think what's happening right now is that a lot of these companies are realizing that they also need to stay open uh to be working with different providers and uh building all of their stock in what we call like a model agnostic way um because, you know, for a company, if in 3 months, if you're working with one provider and another provider comes up with a better better model in 3 months that is way better for you, you should be able to in theory swap everything you're doing and just use that model because it's going to give you a competitive advantage. And so, like I think it's it's kind of like a fine balance in between cost uh in between cost and sort of like flexibility. Um and that's what we're also seeing a lot of the open source models really really taking shape and you you have some great open source models that are being developed. I I certainly hope that the US will start leading the charge with open source models because I think that we're we're lagging behind um especially compared to China. They have some amazing labs there coming up with amazing models and I think that companies are going to look at uh more and more are going to look at sovereignty um even even with models. It's going to be an interesting uh an interesting like 5 10 years ahead.