The Digital Press

The emerging focus in AI development is shifting from prompt engineering to "context engineering," which involves providing AI models with the right information and tools in the right format and at the right time to solve tasks effectively. This broader approach enables AI agents to perform more reliably by addressing context failures rather than model limitations.

Why it matters: AI agent success increasingly depends on the quality and completeness of context, not just prompt design or model sophistication.

The big picture: Context engineering is a dynamic, system-level discipline that integrates instructions, memory, external data, and tools to empower LLMs.

The stakes: Without proper context, AI agents produce robotic or incorrect outputs, limiting their usefulness beyond simple demos.

Commenters say: Many emphasize that context engineering mirrors human problem-solving and is crucial for practical AI, while some caution it’s still an integration challenge without a silver-bullet solution.