Four layers of context engineering for production AI agents: How Deep Insight applies them in working code | Amazon Web Services
When production AI agents handle tasks such as data analysis and report generation, context management can become a major bottleneck. A 200K-token window sounds generous until a real-world task burns through most of it in minutes. Data analysis and report generation leave the agent failing mid-execution, losing accuracy, or driving up costs. A body of published guidance already addresses this, but the individual techniques are easier to read about than to apply. Applied together, they make a measurable difference: in the reference implementation this post walks through, a single write-and-execute tool cuts the tokens each script adds to context by about 95 percent, and loading skills on demand shrinks the system prompt by about 97 percent.
This series organizes that guidance into practices you can apply directly. Anthropic’s engineering posts, together with features built into the Strands Agents SDK, converge on four layers of context engineering: isolate context at the...
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