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Architecting AI Agent Systems: A Strategic Framework for Production Deployment Building an AI agent demo is easy. Building an AI agent system that operates reliably in production is a completely different challenge. As organizations move beyond chatbots and copilots, they're discovering that successful agent deployments require much more than connecting an LLM to a few tools. The real challenge lies in architecture. In our latest article, we explore a practical framework for designing AI agent systems that can scale from experimentation to enterprise-grade production environments. Key topics include: 🔹 Agent orchestration and workflow design 🔹 Memory architectures and knowledge management 🔹 Tool integration strategies 🔹 Guardrails, governance, and observability 🔹 Multi-agent collaboration patterns 🔹 Infrastructure considerations for reliability and scale One of the most common mistakes we see? Teams focus heavily on model selection while underestimating the importance of orchestration, memory, monitoring, and system design. In production environments, these architectural decisions often have a greater impact on success than the underlying model itself. The future of AI isn't just about smarter models. It's about designing systems where agents can reason, collaborate, access knowledge, use tools safely, and operate within well-defined governance frameworks. For technology leaders, architects, and AI practitioners looking to move beyond prototypes and into production, this framework provides a practical blueprint for building robust agent ecosystems. Read the full article: https://lnkd.in/ecFZzWRn #AIAgents #AgenticAI #ArtificialIntelligence #LLMOps #AIArchitecture #MachineLearning #EnterpriseAI #GenerativeAI #MLOps #SoftwareArchitecture #AgentSystems #AIEngineering #DypsisAI

🚀 Running GPUStack with NVIDIA MIG: A Deep Dive into Multi-Instance GPU Orchestration NVIDIA's Multi-Instance GPU (MIG) technology has the potential to dramatically increase GPU utilization by partitioning a single GPU into multiple isolated instances. Sounds straightforward? In practice, combining MIG with modern AI orchestration platforms introduces a surprising amount of complexity. While deploying GPUStack on NVIDIA H200 NVL GPUs, we encountered a chain of 8 distinct issues spanning: ✅ CDI device configuration ✅ NVIDIA Container Runtime ✅ GPU enumeration ✅ CUDA_VISIBLE_DEVICES alignment ✅ vLLM compatibility ✅ Runtime device discovery Each fix revealed another layer of the stack that needed attention. In our latest technical deep dive, Frederico Vicente shares: 🔹 How we increased GPU utilization through MIG partitioning 🔹 The architecture behind GPUStack + MIG deployments 🔹 The root cause analysis of 8 production-blocking issues 🔹 Runtime patches applied to GPUStack and vLLM 🔹 Automation scripts used in production environments 🔹 Lessons learned for teams running multi-tenant AI inference infrastructure One of the biggest takeaways? When working with MIG, UUID-based device selection is significantly more reliable than index-based allocation. Small implementation details across CDI, CUDA, and orchestration layers can make the difference between a stable deployment and hours of debugging. If you're building AI infrastructure, operating inference clusters, or looking to maximize the ROI of expensive GPU hardware, this article may save you a considerable amount of troubleshooting time. Read the full article: https://lnkd.in/eVseKfUr #AIInfrastructure #MLOps #GPUStack #NVIDIA #MIG #vLLM #LLMOps #ArtificialIntelligence #MachineLearning #GPU #InfrastructureEngineering #DypsisAI

Previsão de fluxo de caixa, análise de desvios, menos trabalho manual. Tudo isto é possível quando a IA é aplicada corretamente ao ERP. Este e-book explica onde começar e como reduzir risco.

⚙️ Se está a planear levar IA para produção, ou melhorar sistemas existentes, este webinar é para si. Casos reais, decisões técnicas e lições aprendidas em ambientes de produção. 📅 27 de janeiro | 🕚 11h | 💻 Webinar gratuito

Manual processes, unreliable forecasts, and reactive decisions are still common in many ERP environments. The good news? AI can help, without replacing your ERP.

🚀 A maioria dos projetos de IA falha não por falta de escala, mas por excesso de complexidade. Neste webinar, mostramos como modelos de IA leves podem ser colocados em produção com custos previsíveis e resultados concretos. 📅 27 de janeiro | 🕚 11h | 💻 Webinar gratuito