This episode explore the evolving landscape of AI development, particularly focusing on the transition from prompt engineering to context engineering. This shift highlights the importance of curating, optimizing, and providing comprehensive information to large language models (LLMs) to enhance their performance and reliability, especially for complex tasks. The sources also introduce agent engineering as the next logical step, emphasizing the design of autonomous, goal-driven AI systems that incorporate sophisticated context management, adaptive planning, and persistent memory to operate effectively and safely. Ultimately, the discussions suggest that successful AI application development hinges on expertly managing the information and environment within which LLMs and agents operate.Blog on Context Engineering:https://www.linkedin.com/pulse/context-engineering-path-towards-better-agent-superagenticai-hnyqeContext Engineering thread on Twitter (X) Langchain blog on Context EngineeringSuperagentic AI blog on Context Engineering

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