Effective Context Engineering for AI Agents (why agents still fail in practice)

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⏱️ Timestamps
00:00 The Rise of AI Agents
01:14 Understanding Context Engineering
07:08 Tips for Effective System Prompts
10:38 Common Engineering Pitfalls
18:46 Balancing Control and Automation
20:39 Strategies for Managing Memory
23:41 The Creativity of Context Engineering

📌 Description
In this video, Dave Ebbelaar explores context engineering in AI agents, defining it as the selection of optimal information tokens during large language model (LLM) inference. He distinguishes context engineering from prompt engineering, emphasizing a balanced approach to avoid information overload. He shares practical strategies, including flexible system prompts and optimizing user interactions, highlighting that effective AI solutions often rely on structured workflows rather than complexity. He concludes by stressing the need for creativity and adaptability in context management to ensure AI agents can handle dynamic interactions and maintain quality control.

👋🏻 About Me
Hi! I'm Dave, AI Engineer and founder of Datalumina®. On this channel, I share practical tutorials that teach developers how to build production-ready AI systems that actually work in the real world. Beyond these tutorials, I also help people start successful freelancing careers. Check out the links above to learn more!