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Postman limits tool sprawl and treats context as Agent Mode’s bottleneck

aws.amazon.com by Srinivas Kini 12 min read Editor score 8/10

Postman’s Agent Mode uses schema-based reads, multi-tier prompt caching, and model-dependent data controls; the team identifies context—not capability—as the key bottleneck. The case offers practical architecture patterns for integrating agents into a mature product.

What: Postman Agent Mode brings API testing, documentation, discovery, and implementation into an AI-native product interface.
How: Built on Amazon Bedrock, it uses model flexibility, cross-Region inference, model-dependent zero data retention, and multi-tier prompt caching.
Why it matters: Task-scoped tools, purpose-built context, user approval for state changes, and PII-redacting Guardrails are production controls for agents.

The production architecture combines scoped tools and context with agent orchestration and Bedrock inference. Sketched from the article's own text.

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