Building an AI agent for a demo and operating one for 40 million developers are different engineering problems. Postman set out to build Agent Mode, an AI-native way to work across API testing, documentation, discovery, and implementation. The team expected model quality and prompt design to be the hardest problems. The deeper challenges came from integrating an agent into a mature product with years of interface-driven assumptions, a wide surface area, and specialized concepts.

In this post, Postman and AWS describe the architectural patterns that emerged while making a mature product legible to an AI agent. These patterns include controlling tool sprawl, exposing schema-based reads, and treating context rather than capability as the primary bottleneck.

We also explain how Agent Mode uses Amazon Bedrock for model flexibility, geographically scoped cross-Region inference, model-dependent zero data retention, and multi-tier prompt caching. Together, these lessons can help teams move production agents beyond prototypes.

Agent Mode is Postman’s portal for working with the product in an AI-native way across testing, documentation, discovery, and implementation. Postman has evolved over 11 years, and developers and users learned to locate information through the interface by expanding sidebars, checking tabs, and opening requests. Re-engineering that awareness for an agent surfaced structural assumptions in the product’s APIs, user experience, and distribution of product knowledge. An agent reasons over data rather than navigating a screen. Figure 1 illustrates how Agent Mode works directly against the application.

Figure 1: Agent Mode works directly against the Postman application.

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