Good morning, afternoon everyone, and thank you for being here. My name is Yan Pra. Today we are going to talk about a critical evaluation in artificial intelligence infrastructure. Moving from monolithic to micro mind, we'll explore how building a t AI platform with Multigenic. Architecture that can unlock the unprecedented flexibility and performance that traditional system simply cannot match. Let's deep down the landscape of AI platform engineering at an. Inflection point. Organizations still lying on our infrastructure and finding themself constantly constrained by the system will be this problem. The path forward is to transformational, not just on technology, but also on the philosophy. It is about embracing a new model built on modularity. Specialization and intelligence coordination over the complex and monolithic. The organization that mastered their shift will be one that define the next ticket. AI powered business performance and operation. The today's bottleneck about the monolithic, I will not say crisis, but it's going to be definitely crisis. First, we see a significant development slowdown. It's core base become a more complex and simple update. That one took a week. Now sketch into month because everyone want to consume the same architecture of the place or the resource. This leads to a heavy testing burden. A minor feature Edison can trigger need for extensive regression testing across completely and on the related part of the system as well. And finally, the risk of catastrophic failure. When there is a tightly coupled system failed, then they tend to fail completely on every area, and it'll bring down your system. The technical depth in the system has become a barrier to innovate with monolithic architecture. Let's deep down how its impact the AI and the organizational impact on because of this monolithic ai, the technical issues creates a profound organizational problem. Different business unit have different AI need, but centralized, monolithic process you and slows down your development. The result of growing disconnect day by day. Within the organization also create a problem on your current architecture and it create a problem on your delivery. It's impact your delivery. It is also difficult to maintain a centralized AI system while eliminating their limitation. So going with the monolithic ai, every organization is going to face this problem. Let's talk about a multi-agent architecture. Fundamental and how this could help us. The biggest answer is yes, and it lies in multi-agent architecture. The core idea is simple. A complex intelligent doesn't require a single complex system. Instead, we engineered a multiple focused as in it optimized for a specific problem or a data drive. This approach guided by a key. Principle specialized through a, through decomposition flexible communication protocol. We can. Established an NCP protocol, which is standard for AI for agent and work together intelligent coordination layer. We can orchestrate a coordination layer among the agent and a natural fault tolerance so that any failure, one part of the system should not bring down the whole system and evolutionary capability and allowing system to grow and adapt and launch easily. The poor layer of agent deployment framework. So to structure this approach we can use four different layer of the multi-agent framework. The first layer is the prescription layer. And this is the layer where responsible for your data ingestion. Processing and initial interpretation. Then you have a cognition layer. Contains agent mostly responsible for analysis, reasoning, decision making sentiment analysis, and these agents are applied and domain specific intelligence to generate insight prediction or recommendation anon layer where translate your insight. Decision into a concrete operation, that external system or business process can be achieved. Then there is a coordination layer. It's an orchestration of the multi-agent, where multiple agent can work across all of the layer, and it facilitated the dynamic collaboration pattern, that optimized system performance based on the current condition of the system. So let's talk about the performance engineering and distributed AI system. Performance in a multigenic system is fundamentally different from a monolithic, while monolithic focuses on on a centralized. Efficiency and distributed architecture, Excela, parallel processing and adaptive source. So big parallel processing agent can handle multiple tasks simultaneously, a different resource. Dynamic resource utilization means the system also can scale based on the actual demand inactive agent, the time they don't need. It can also. Low power more while other can work when it's required. This is all enabled by a optimized communication using an advanced messaging queue and compression to ensure that communication supports rather than hindrance and performance. So ability to do everything what you want with the less resource and. Less latency and more performance. The platform reliability and the pulse tolerance, how the multi-gen distributed system can help you on reliability and cultural tolerance. The reliability is another area where. This architecture excels by distributing risk across independent component. We create a system with a sophisticated fault tolerance. We can implement key mechanism, like a graceful degradation and circuit breaker pattern to contain a failure, continuous health monitoring and self feeling capability to automatically recover. Unlike in a monolithic, that requires a full restart up your whole system. A multi-agent distributed system can use a granular recovery strategy and restoring functionality in incrementally and maintaining downtime. If you have a multiple agent distributor across different, and they're consuming different resources and they are distributed, then you will have that ability. To incrementally and minimizing the downtime and, even restart them individually. The legacy system, integration strategy, of course a full transition doesn't happen overnight. A smooth transition with legacy system is essential. There are four strategies to achieve on this. Adapter pattern implementation creates a translation layer so the modern agenda and legacy systems can communicate. Then data synchronous mechanism ensure information flows sim seamlessly between old and new component. API Gateway Strategies provided a centralized point to manage interaction handling, security and protocol transition, and an event driven integration. You just a messaging to loosely coupled system, allowing them to coordinate asynchronously. Let's talk about the operational excellency and monitoring of the system. Monitoring a distributed system requires definitely more advanced approach. Instead of a simple logging, and just disaster recovery or any kind of mechanism. We need capability like distributed pressing to follow a request entire journey across multiple agent. This is vital for. Diagnosing performance issues. We also use a correlation based alerting where the system can distinguish with between a minor problem, it can handle itself, and a significant issue that requires a human intervention. Let's talk about security and governance. Nothing in this world is possible without talking about security and governance. In the current world at least, security in a distributor network must also evolve beyond traditional perimeters. We rely on the modern strategies like zero trust, security model, where every interaction between agent is authenticated and authorized independently. Identity and access management is to provide agent. With the secure identity and dynamic permission distributed audit trials for regularly to check compliance. And the last one is the behavioral based threat detection, where machine learning establishes a baseline abnormal activity to quickly identify the anomalies. Change management and team adaption? Yes, of course. When you moving out of monolithic architecture into a multi-agent distributed platform the change management is a key and. Without this, nothing is going to work. The transition is as much as about people as it also about technology. It requires a cultural transit transformation shifting team from a mindset of centralized control to a distributed collaboration. To succeed, we must focus on three thing, skill development program to engineer. For designing and managing disorder system process evaluation to adapt our development cycle for interdependency pilot program approaches to gain expenses and demonstrate value while limiting the risk. Let's talk about a future proofing AI platform architecture by mastering this architecture today. We are future proofing our platform. We position ourself to leverage emerging technologies like edge computing, integration to distribute intelligence closer to data source, self optimizing architecture. Where AI agent can lend to automatically allocate resources and predict bottleneck, at least like Autoscale Auto, identify. Further down to the line, quantum computing integration could be enable new classes of agent per currently impossible in computation, sustainable computing with energy efficient. Design and carbon error. Resource location. Embracing the multi-agent features in conclusion. This move from a monolithic to a multi-agent AI is more than a technological upgrade. It's a fundamental REMA imaging re reimagining of how intelligence system are bolded evolves. The evidence is clear. This architecture delivers a superior platform performance, reliability, and adaptability. The framework and tools are available today. The multi-agent avail. The multi-agent revolution has begun and organization that act decisively will save the future of AI business. Thank you very much.