Philipp Schmid
Beyond Chatbots: How to build Agentic AI systems
#1about 4 minutes
Tracing the evolution from language models to AI agents
The history of AI agents is traced from simple text completion models to complex, multi-step reasoning systems using frameworks like ReAct.
#2about 2 minutes
Defining the core components of an AI agent
An AI agent is a system where an LLM decides the application's control flow using reasoning, memory, and tools.
#3about 4 minutes
Exploring common design patterns for building AI agents
Several composable patterns like reflection, tool use, planning, and multi-agent collaboration provide structured approaches for building agentic systems.
#4about 1 minute
Why design patterns are crucial for scalable agents
Agentic design patterns are essential building blocks that provide structure, enable composition, and improve the testability of complex AI applications.
#5about 4 minutes
Evaluating agent reliability over model capability
Agent evaluation shifts focus from single-attempt model capability to multi-attempt reliability, requiring custom benchmarks to measure real-world task success.
#6about 3 minutes
Moving from prompt engineering to context engineering
Context engineering expands on prompt engineering by systematically providing the right information, tools, and memory to the LLM at the right time.
#7about 4 minutes
Building agents with Google Gemini and open source tools
You can build agents using Google Gemini through AI Studio, the native SDK, or integrations with open-source libraries like LangChain and CrewAI.
#8about 2 minutes
Predictions on the future impact of AI agents
Future agentic systems are predicted to enable zero-cost software creation, personalized multimodal interfaces, and accelerated adoption in robotics.
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Tracing the evolution from LLMs to agentic AI
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Understanding the shift to the agentic era
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The evolution from rule-based RPA to agentic AI
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Understanding the evolution and autonomy of agentic AI
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The evolution of AI from tool to assistant to agent
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