Agentic AI

We'll now talk about architecture before jumping in code examples and more technical examples with LG a little bit later. If you have questions throughout that presentation, please drop them in the chat. We'll make sure to answer them at the end. So in terms of, technological landscape, what we see here is that these giant platforms have built some very cool, agentic framework to run your code in and build some agents. The key here to select the right one is to take the one that matches your needs. So if you're in Salesforce, obviously, Agent Force is a good choice. If you're in Microsoft Shop, Azure is gonna be a very good, selection as well. They all offer, I'd say, similar functionalities. Depending on the level of maturity of these platforms, you may opt for one or the other. One key, observation here is that they have all different, I'd say, protocols or APIs, but the key lesson is that they are all text based, JSON based. So it's basically just a schema that defines, the different tools that you're using. One observation we made by looking at them and building these agents is that you can have a single retrieval platform, in this case, Coveo, that will power all of these. So no matter if you're building in one agentic framework or the other, you can have one set of information that is coherent across all these different experiences, and we think it's strongly beneficial, obviously. If you try to build these different agents, you'll see that there are different stages of maturity. You cannot start by having a set of intelligent agents that are all together speaking and be happy together. You're gonna need to do these different steps to get there. The first one we see mostly in PMEs and and small enterprises are basically just connecting GPT, trying to ground it with some prompts, but you know what it can do. It's obviously gonna hallucinate. It's not perfect. It it's a good start, but it's it's not grounded, and we don't think it's an enterprise solution at all. The second thing you're gonna see is that you're gonna ground your your bot or your your LLM on some information. To ground that information, you can use a vector database or a retrieval engine as sophisticated as Coveo if you want. This is the first step. You're gonna have a generation machine like Coveo RGA, which you are probably aware of. The next phase is gonna be to have a conversational or an agentic integration at this point. So this agentic will start to execute on itself some different tasks and will start to basically retrieve content and do a little bit more. And the last one, a search agent, is basically a fully autonomous, solution that will take care, of all your search needs. So it's really gonna be either a standalone application for your agents that needs to to search or a part of a more complex suite, of applications. What we see on the market is that there are some missing strategies. Before GPT, we had these chatbots that everybody disliked that were basically scripted bots where you had some path that were hard coded dependings on business rules. So if you wanted to talk to support, they were listening to specific keywords and then just redirecting you to a set of predefined answers. After GPT, what we see is that we have LLM chatbots, and while they look intelligent, sometimes they are not always grounded. And this is the main thing. Even if you build a vector database, it's it's not gonna be necessarily up to date connected to all the different tools you have out there, not necessarily connected not just to, text retrieval, but also structured data, for instance. So what we decided to bring on the market is the gap to bridge that whole thing, which is a, a real relevance generative answering, the first stage into that maturity model. So if you want to have something that is accurate and bring good result and get a good return on investment quickly, you need to have something like this. This is a simple query that's gonna give you some answers. And in the middle, you're gonna have the Kubernetes suite that's gonna, at a enterprise grade, get your data in and extract all these good text chunks and with good relevance, give you back some generated answers, follow-up questions, etcetera. We are now evolving that solution to an agent, so an agentic rag as we call it. So now it's the same kind of magic sauce, but now in a conversational, aspect. So we're gonna have instead of having a simple query, you're gonna have some long texts, some advanced queries. Then Then we're gonna go in the middle. That search agent will resonate and do all sorts of different tasks, always, sit on top of all these good documents. And then it's gonna just throw back that answer, and then you're gonna have the context of the whole conversation. And what we want to, promote and what we see on the market that is important is basically to pipe that search agent to other agents, which is the final stage of that maturity model, where you're gonna have that search agent that is gonna be able to feed others. At this point, it's becoming a little bit meta, but you're gonna have these bots that will talk to other bots. And then at one point, they're gonna be autonomous in, realizing some some specific tasks for your enterprise.