Why Software Engineers Struggle With AI Systems скачать в хорошем качестве

Why Software Engineers Struggle With AI Systems

AI agents, LLM systems, and production-grade AI engineering require a different mindset than traditional software development. This video breaks down how to design, evaluate, and scale reliable AI systems beyond prompt tuning. Most senior engineers don’t fail at building AI systems because they lack skill. They fail because they apply deterministic thinking to a probabilistic system. In this video, I break down why traditional software engineering instincts collapse when you start building AI agents. We’ll go beyond prompt tuning and into systems design, evaluation pipelines, and real-world architecture trade-offs that separate demo projects from production systems. You’ll learn: → Why “same input, same output” is a myth in agentic AI → How vibe-driven development quietly destroys reliability → The hidden latency tax of multi-step agent workflows → Why the best AI systems are often shockingly simple → How to shift from prompting to real orchestration If you’re building AI agents that work in demos but fall apart in production, this video gives you a mental model to engineer for stability, speed, and scale. 👉 Subscribe for more deep dives into AI engineering, system design, and production ML:    / @cesarsotovalero   ⏰ TIMESTAMPS 00:00 – Intro 01:16 – Agentic Non-Determinism 04:06 – The Evaluation Crisis (Why Vibe Checks Fail) 05:51 – The Architecture Trap and the Hidden Latency Tax 07:43 – From Prompting to Orchestration 09:13 – Takeaways #AIEngineering #AIAgents #SoftwareEngineering #LLM #MLOps #SystemDesign #ArtificialIntelligence #Developers #EngineeringMindset #TechLeadership