Context engineering is more than just the latest AI buzzword—it’s shaping up to be the future of how we build truly intelligent systems. In this episode of In The Loop, I explore the shift from prompt engineering to context engineering, why this evolution matters, and how it impacts the effectiveness of AI agents. If you’ve ever wondered why some AI tools feel magical while others fall flat, context engineering holds the answer.

I unpack what "context" actually means in the world of AI—from prompts and system instructions to long-term memory and external data. You’ll hear how building multi-agent systems without proper context coordination often fails, and why simpler, sequenced workflows are currently more effective. Whether you’re building AI products or simply using tools like ChatGPT, this episode will change how you think about how they work.

⏩ Episode Highlights

(01:00) - What is context engineering—and how is it different from prompt engineering?

(09:20) - What role are orchestration and multi-agent systems help maintain context and improve p

(16:05) - The impact of context engineering: personalization, privacy, and better UX

(16:00) - Closing thoughts: what this means for the future of AI

🔗 Links & Resources:

📖 Episode transcript with more resources on the Mindset AI blog

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Jack Houghton

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