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 architecture level, control it through prompts, externalize it to the filesystem, and verify it with independent agents and safeguards. In this post (Part 2), you will learn how each layer works and what it looks like in working code. We applied these techniques in Deep Insight, an open source multi-agent data analysis solution built on the Strands Agents SDK and Amazon Bedrock AgentCore, which serves as the reference implementation throughout.

The first post defined five core challenges that production multi-agent systems must solve. It introduced how Strands Agents SDK , Amazon Bedrock and Bedrock AgentCore , and AWS Fargate each address different layers of the problem, with code examples.

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