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Dive deep into advanced AI agent development in this comprehensive technical podcast episode featuring Philipp Schmid from Google DeepMind, where you'll tackle the most challenging questions developers face when building production-ready AI agents. Explore critical decision-making frameworks for model and framework selection, comparing Gemini versus open models like Gemma and evaluating when to use frameworks such as LangGraph, CrewAI, or the ADK. Learn from a detailed case study of the Deep Research Agent's development lifecycle, covering everything from initial concept and prompt engineering to implementing multi-agent patterns. Master advanced techniques with the Gemini CLI through live demonstrations and pro-tips for agentic workflows, while discovering strategies for writing model-agnostic prompts that maintain performance across different model families including Gemini, GPT, and Claude. Gain insights into practical agent architecture design, development methodologies, and evaluation techniques that go beyond basic AI/ML concepts to focus on real-world implementation challenges. Access extensive resources including technical blog posts on context engineering, effective prompt engineering, and hands-on demos showing Code Sandbox MCP integration with Gemini CLI, plus documentation and custom slash commands for enhanced development workflows.
