Four layers of context engineering for production AI agents: How Deep Insight applies them in working code
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This article outlines a four-layer context engineering framework for production AI agents, demonstrated through the Deep Insight multi-agent data analysis system built on AWS. The first layer isolates context by assigning independent windows to specialized agents, passing only compressed summaries via a shared state dictionary. The second layer controls context inflow using strict prompt-based output token budgets and enforcing a self-contained code philosophy where scripts do not rely on previous variables. The third layer externalizes heavy content to the filesystem, utilizing a write-and-execute tool that reduces context tokens by approximately 95 percent and loading skills on demand to shrink system prompts by 97 percent. The fourth layer ensures quality through verification agents like Validators and Auditors, alongside safeguards such as a SummarizingConversationManager to handle context overflow. These techniques collectively prevent context window exhaustion, reduce costs, and maintain accuracy in complex, long-horizon tasks. The approach leverages features from the Strands Agents SDK and Amazon Bedrock AgentCore to implement these practices efficiently.
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