Context Engineering
Last updated: 2025-07-03
In Generative AI, context is the information a language model uses to guide
its outputs, such as instructions, memory, and external data. Context
engineering is the practice of designing, preparing, and managing this
information to shape model behavior and results.
Context engineering is especially critical in multi-agent systems, where multiple agents collaborate to solve complex tasks. In these environments, every improvement in context quality and data volume is amplified: even small gains from an agent can have a significant impact as the number of agents increases. Well-optimized context not only enhances individual agent effectiveness but also drives overall system efficiency and scalability.
Crafting and delivering the most relevant context to each agent is essential for achieving high-quality, efficient, and cost-effective results. This chapter introduces strategies for optimizing context, focusing on two main objectives:
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Minimizing irrelevant content: Filter out outdated, redundant, or noisy information to reduce misleading results, token usage, costs, and latency.
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Maximizing usefulness for each agent: Curate and structure context to prioritize relevant, actionable, and timely information—such as recent or high-confidence data, summaries, or dynamically adapted content—empowering agents to make better decisions.
In some cases, further optimization can be achieved by delegating tasks to simpler or more efficient components instead of language models.
Effective context optimization improves response quality, reduces computational overhead, and enables scalable, collaborative multi-agent systems.