In this July 3rd episode of The Daily AI Show, the team breaks down “context engineering,” a concept advanced by Andrej Karpathy that is set to replace prompt engineering as the core skill for working with agentic AI systems. They explain why context engineering is different, how it impacts agent design, and what it means for future workflows, memory, orchestration, and AI productivity.

Key Points Discussed

Context engineering focuses on giving AI agents the right objectives and frameworks while allowing them to plan, search, and refine outputs autonomously.

Unlike static prompt engineering, context engineering leverages memory, tool use, and real-time data gathering during multi-turn workflows.

Beth noted that effective context engineering is as much about what you remove as what you provide, focusing attention where it matters.

Jyunmi outlined a practical six-step framework for context engineering: define the use case, identify data sources, plan orchestration, filter information, optimize for performance, and ensure privacy/compliance.

The team discussed context pruning to avoid overloading the context window, emphasizing right-sized context delivery at the right moment.

Agent orchestration layers (like LangChain, MCP) handle dynamic context injection and retrieval across multi-step processes.

The group highlighted challenges in agent consistency, memory prioritization, and human-in-the-loop refinement during complex tasks.

Analogies like improv vs. stage magic helped clarify how context is dynamically constructed or pre-planned.

Evaluation layers remain essential: agents need internal feedback loops while humans provide external prioritization and validation.

Latency and context window size constraints can still cause slowdowns in models like Claude and Gemini despite large token capacities.

The episode emphasized that context engineering will become a foundational literacy for those working with advanced AI agents, impacting everything from small business workflows to enterprise orchestration.