Context Engineering Strategies for Large Language Model Agents

As large language model (LLM) agents gain traction, context engineering emerges as a crucial aspect of building efficient agents. This post summarizes four key context engineering strategies: writing (saving context outside the context window, such as using scratchpads or memories), selecting (choosing relevant context from external storage), compressing (summarizing or trimming context), and isolating (splitting context across multiple agents or environments). These strategies aim to address the limitations of LLM context windows, improve agent performance, and reduce costs. The post uses examples from companies like Anthropic and Cognition to detail the specific methods and challenges of each strategy, including memory selection, context summarization, and multi-agent coordination.