The Hard Part of Shipping an AI Agent Wasn't the Model
Postman's team, describing their work with AWS, expected model quality and prompt design to be the hardest problems when building Agent Mode, the AI-native way to work across API testing, documentation, discovery, and implementation. The deeper challenges came from somewhere else entirely: integrating an agent into a mature product with years of interface-driven assumptions, a wide surface area, and specialized concepts.
That distinction matters. A product built for humans teaches people to find things by opening tabs and expanding sidebars; an agent reasons over data instead. The architectural patterns Postman and AWS describe emerging from that gap include controlling tool sprawl, exposing schema-based reads, and treating context rather than raw capability as the primary bottleneck.
AI-generated discussion · Different perspectives
If context is the bottleneck rather than capability, then a lot of enterprise agent failures should look like retrieval and interface problems, not reasoning problems.
Tool sprawl and old interface assumptions are engineering debt with a new name; calling it context doesn't make it easier to pay down.