Context is king in the agentic world. Pairing a performant reasoning model like Claude, DeepSeek or GPT-5 with the right context drives efficient planning and tool usage and improves multistep reasoning, leading to personalized conversations, higher task accuracy and relevant responses.
In this article, we present the need for context engineering and associated benefits, identify challenges developers face as they use it for developing AI agents, and propose a high-level architecture to help address them.
