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AI agent integration is transforming GenAI development, but standardization remains the missing piece. 🧩 In this talk, Maks Operlejn, Senior ML Engineer, explores how Model Context Protocol (MCP) and modern frameworks are reshaping how we build production-grade AI systems. You’ll learn about: 👉 Pydantic AI essentials - building type-safe, production-grade agents with structured outputs, dependency injection, and powerful evaluation tools, 👉 MCP fundamentals - Anthropic's open standard for connecting AI models to external data sources and tools, 👉 Multi-agent system architecture – structuring data on demand with distributed, collaborative AI agents, 👉 10 lessons learned – mistakes and breakthroughs from building scalable agent systems, 👉 Practical recommendations – best practices for creating secure, maintainable AI integrations. If you're building LLM-powered applications or exploring agentic AI workflows, this talk is your field guide to standardizing agent integration. 🚀 Check out our blogposts: https://deepsense.ai/blog/ Linkedin: https://www.linkedin.com/showcase/applied-ai-insider 00:00 Intro & agenda 02:45 Pydantic AI essentials 07:25 Agentic frameworks comarison 08:35 MCP essentials – what & how 13:20 Case study – structuring data on demand 16:41 Lessons learned: APIs & token budget 20:42 Lessons learned: tools, models & observability 25:01 Lessons learned: testing, guardrails & graphs 29:15 Lessons learned: security 30:35 Summary & key takeaways #MCP #AIagents #MultiAgentSystems #PydanticAI #Anthropic #LLM