Four layers of context engineering for production AI agents: How Deep Insight applies them in working code

Amazon's guide explains four layers of context engineering for production AI agents, using Deep Insight, an open source multi-agent data analysis solution, as the reference implementation.

• The layers are isolating context through multi-agent architecture, controlling it through prompts, externalizing it to the filesystem, and verifying it with independent agents and safeguards.
• The post says 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.
• It cites Anthropic's Claude Sonnet 4.5 on Amazon Bedrock, which offers a 200K context window, and notes that a sample sales-analysis task consumes over 150K tokens across 15 minutes.
• Deep Insight gives each specialized agent its own context window, keeping even the Coder under 25K tokens, and passes only compressed results through a shared_state dictionary holding messages, clues, full_plan and history.
• The post is the second in a three-part series; the third, on deploying agents in a private network on Amazon VPC, is coming soon.