Context Engineering in 2026 — Louis-François Bouchard, Omar Solano & Samridhi Vaid, Towards AI

Context engineering for AI agents centers on balancing performance, cost, and memory recall within finite context windows. Contrary to common assumptions, aggressive compaction techniques like summarization often degrade performance and increase costs by invalidating prompt caching, which can reduce token expenses by up to 50 times. Maintaining full conversation history proves more effective, yielding higher recall accuracy and lower latency when leveraging modern caching APIs. For large-scale knowledge retrieval, hybrid search pipelines combining semantic similarity with keyword-based BM25 outperform pure semantic approaches, especially when dealing with facts buried in extensive documentation. While local models offer cost advantages, they currently struggle with hardware-constrained context windows, making cloud-based solutions with efficient caching strategies the most scalable and reliable approach for high-volume AI tutoring applications.