Context Engineering Emerges as AI's Next Key Discipline Beyond Prompt Engineering

At the AI Engineer World's Fair in San Francisco, context engineering — the practice of managing an AI model's working memory — drew massive developer interest, with hundreds lining up for a dedicated workshop. The discipline addresses a core limitation identified by HumanLayer's Dex Horthy, who observed that AI agents degrade in performance after consuming roughly 100,000 tokens, about 10% of their available context window. Unlike humans, who deepen understanding through extended conversation, AI models lose focus as their context windows fill, making careful memory management a practical necessity for developers. MLH co-founder Ben Halpern described context engineering as the latest optimization frontier, helping developers improve latency, reduce costs, and treat models less like black boxes. However, Codex developer experience lead Dominik Kundel cautioned that overly strict context management can limit an agent's range, advocating instead for giving agents broad, unfiltered access to sources like Slack and Notion so they can navigate complexity independently.