Transcript: Arun Joseph on Reliable AI — Interview with Alexy

Yeah, I'm Arun Joseph. Uh I'm the co-founder of Mosaic Agentic Systems. Role of course uh is building in a startup right along with my co-founders Jasper and Amad. Yeah, that's what my and my startup is all about large scale decisioning engines uh for operational intelligence as we call it. Yeah, that's pretty much what we do. Previously the head of AI engineering for Dodge Telecom in Europe. I would I would say that the accomplishment was we developed a few things which might have been something like a small mini Xerox spark moment. We built potentially one of the first agentic platforms and put it live early late 2023 and uh it it powers it used to power multiple countries in Europe open source mood and Eclipse Foundation early or mid 2024 we gave a talk about it in our production agents when most of the enterprise a AI was failing. So we gave a talk and it was only a week later OpenAI even released swamp. So we started building our own protocols. So we had a small great team um who who aspired to build the foundations of what we call as a H&D computing and we put it in production and there were so many learnings. So both the business outcomes as well as having had the privilege of leading such an elite team reliable AI. Yeah, this is excellent. So I'm one of the original contributors to the small group that u wrote reliable AI. Reliable AI is something I would define as um if the outputs from an AI system or any system, right? U if they are verifiable and reproducible and consistent under real operational constraints. I would I would say that's the definition. So this is interesting. So of late I have started to notice a shift in also treating this as a true engineering discipline. and not only uh model research. So reliability engineering loves constraints. So engineering shines in constraints, right? So AI the pure model, how do you constrain it to produce verifiable uh consistent results is an engineering discipline which has started to pick up and more effort should also start to go in there. uh and more such use cases will allow push the boundaries of computing as we know it and right now a lot of this u is definitely there on the model side but I'm absolutely imagining new operating systems new ways to have makes deterministic and nondeterministic computing as a foundational paradigm there are new line and straw walls to emerge I'm looking for where they are going to come from that's all I would say yeah the next five years, five years is uh light here in in the AI space. Um I would say that there is a lot of hype in the AI world. We started to see at least AI systems as computational primitives which is different from expecting unicorns and rainbows, right? So the next five years I would imagine at least personal computing would have definitely uh definitely changed into a mix of traditional deterministic and uh and agentic computing as I would call it in every personal device I would imagine models also to be running such that the ease with which you are interacting with the computer to do things for you is radically going to change. You can call it AI, you can call it whatever, but this is the fundamental construct. Other than that, I would imagine the large scale enterprises to completely shift in how they build their IT information systems are very prone to change. I would imagine companies to be run purely on agentic systems at least some divisions which is going to produce massive significant results. This is essentially why I quit my job to build such systems thinking that this is a future that it holds large scale operational intelligence companies or divisions which can self-optimize on information systems right and not AGI I would rather say how do you selfoptimize a system to increase the revenue or reduce cost leakage because information system can self- adapt with a very small nimble team of experts