
What happened
Postman and AWS described the architecture behind Agent Mode, which runs on Amazon Bedrock and serves 40 million developers. In Postman's testing, tool-selection errors increased once the visible toolset exceeded approximately 40 tools.
Why it matters
The team said missing or incomplete context caused more failures than missing capabilities, so production agents appear to hinge on managing what the model sees, not just adding more tools.
WHO IT HITSEnterprise IT and platform teams running customer-facing AI agents, along with product managers at mature software companies, can use Postman's patterns to plan tool scoping, data access, and context budgets when moving agents from demo to production.
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Postman's Agent Mode is a portal for working across API testing, documentation, discovery, and implementation in an AI-native way. Postman has evolved over 11 years, and developers and users learned to locate information by expanding sidebars, checking tabs, and opening requests. The team built dedicated context handlers that distilled each entity into what the agent needed to know, because serializing the existing interface data model did not produce useful context.
Two other patterns came out of that work. The architecture selects tools based on need and context and isolates individual execution threads, so the model sees only the tools relevant to the current task. For products such as the API Catalog, Postman consolidated multiple narrow views into a single query tool that can generate complex queries with joins and WHERE clauses, shifting the engineering job from building one tool per question to modeling the data well once.
On Amazon Bedrock, Postman gains model flexibility across supported Anthropic Claude models, cross-Region inference for throughput, model-dependent zero data retention, and multi-tier prompt caching. Postman's production implementation is proprietary and is not available as a public sample repository.
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