Composable AI: Building Next-Gen AI Agents with MCP скачать в хорошем качестве

Composable AI: Building Next-Gen AI Agents with MCP

🔊 Recorded at PyCon DE & PyData 2025, April 24, 2025 https://2025.pycon.de/program/7FLW7F/ 🎓 Martin Seeler reveals how to build composable, production-ready AI agents with the Model Context Protocol (MCP), bridging cutting-edge research and real-world engineering at scale. Speakers: Martin Seeler Description: Martin Seeler presents a technical deep-dive into building composable AI agents using the Model Context Protocol (MCP). As a Senior Staff Engineer at Blue Yonder, Seeler shares practical insights on developing AI solutions that bridge research innovations with real-world customer needs. The presentation explores the architectural principles and implementation strategies for creating modular, extensible AI systems that can be effectively deployed in production environments. Seeler examines key considerations including dependency management, configuration approaches, and the balance between flexibility and reliability in AI agent design. The talk addresses critical challenges in scaling AI solutions, including managing model contexts, handling complex interactions between components, and ensuring robust performance in enterprise settings. Drawing from hands-on experience, Seeler demonstrates how thoughtful system design and modern software engineering practices enable the development of AI agents that can adapt and evolve while maintaining operational stability. The presentation provides concrete examples of MCP implementation patterns and discusses strategies for avoiding common pitfalls in AI system architecture. The core message emphasizes the importance of building AI systems that are not just technically sophisticated but also practical and maintainable in real-world applications. ⭐️ About PyCon DE & PyData: The PyCon DE & PyData conference unite the Python, AI, and data science communities, offering a unique platform for collaboration and innovation. The PyCon DE & PyData 2025 conference, provided an exceptional experience, fostering deeper connections within the Python community while showcasing advancements in AI and data science. Attendees enjoyed a diverse and engaging program, solidifying the event as a highlight for Python and AI enthusiasts nationwide. Follow us: • LinkedIn:   / 28908640   • X: https://www.x.com/pyconde Links: • Conference website: http://pycon.de • Other sessions: https://2025.pycon.de/talks/ The conference is organized by • Python Softwareverband e.V.: http://pysv.org • NumFOCUS Inc.: http://numfocus.org • Pioneers Hub gemeinnützige GmbH: http://pioneershub.org If you enjoyed this session, please like, comment, and subscribe to our channel for more insightful talks and discussions. Share this video with your network to spread the knowledge! Hashtags: #Python #PyConDE #PyData #OpenSource #AI #DataScience #MachineLearning #SoftwareDevelopment #LLMs #Community Acknowledgements: Special thanks to all the volunteers and sponsors who made this event possible. About: Python Softwareverband e.V.: PySV is a non-profit that promotes the use and development of Python in Germany through events, education, and advocacy, fostering an open Python community. NumFOCUS Inc. supports open-source scientific computing by providing financial and logistical support to key projects like NumPy and Jupyter, promoting sustainable development and collaboration. Pioneers Hub gemeinnützige GmbH: is a non-profit fostering innovation in AI and tech by connecting experts and promoting knowledge exchange through events and collaborative initiatives. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.