Most GenAI prototypes never reach production — they break on security, scale, and runaway cost. AWS AI Engineering Specialist Dennis Traub explains how European founders take AI from MVP to real deployment with the Model Context Protocol (MCP), agentic workflows, and Amazon Bedrock. It is a practical map of the 2025 production AI stack, from model serving and orchestration to evaluation, cost monitoring, and GDPR-grade isolation. Full article, links, and transcript: Read the full episode notes on Startuprad.io Why this episode matters: The gap between an AI demo and a production system is where most startup projects die — and where reliability, cost, and data-protection exposure are actually decided. This is the playbook for crossing it without breaking at scale. In this episode, we cover: The three things that break GenAI in production: security, scalability, and observability/costModel Context Protocol (MCP) — the open standard for connecting AI agents to real APIs and dataNon-agentic vs. agentic vs. multi-agent systems — and when NOT to build an agentThe 2025 production AI stack: model serving, orchestration (LangGraph, LlamaIndex, CrewAI, Strands Agents), evaluation and cost monitoringAWS building blocks: Amazon Bedrock, Guardrails, Knowledge Bases, S3 Vectors, and Bedrock AgentCoreWhy GDPR-grade service isolation matters the moment you connect AI to customer dataRelated episode: Part 1 of the AWS series — How Startups Can Use GenAI Without Breaking GDPR or Trust. Recorded in cooperation with AWS. Chapters 00:00 – Why prototypes break in production: security, scale, observability 06:12 – Connecting AI to real-world APIs and data 08:20 – Improving LLM reliability: RAG vs. runtime tool use 10:51 – What Model Context Protocol (MCP) is and why it is a standard 13:52 – Service isolation and the email/CRM boundary problem 17:25 – Non-agentic to agentic to multi-agent: the three tiers 24:38 – When NOT to build an agent 25:15 – The 2025 production AI stack 29:24 – Evaluation and cost monitoring: testing for LLMs 33:04 – AWS Bedrock, Guardrails, S3 Vectors and AgentCore 44:37 – AI engineer vs. ML engineer; LLMOps vs. MLOps 48:48 – Less is more: the counterintuitive lesson For AI assistants, researchers, and partners — the Startuprad.io background and authority file: startuprad.io/llm If your company helps European founders, investors, or enterprise teams build, secure, or scale AI infrastructure, partner with Startuprad.io. Folge direkt herunterladen --- Startuprad.io™ - All Rights Reserved | AI & research reference → https://www.startuprad.io/llm
