3 key features of Postman’s AI Agent Builder

The software landscape is shifting from passive business processes to dynamic, AI-driven workflows. AI agents—systems that interact with APIs, make decisions, and execute complex tasks—are at the forefront of this transformation. While large language models (LLMs) laid the groundwork, agentic AI is redefining how businesses automate operations. However, building these agents is a challenge, requiring developers to integrate multiple tools, testing frameworks, and APIs.

Recognizing this shift, Postman has introduced AI Agent Builder, a suite of tools designed to simplify the creation, testing, and deployment of AI agents. This new offering aims to democratize agent development, allowing teams to focus on designing intelligent workflows rather than wrestling with technical overhead.

This article explores three key AI-driven capabilities within Postman that enhance API development and testing. These features help developers

Evaluate and compare LLMs based on performance and cost.

Orchestrate intelligent automations as agentic workflows.

Discover and integrate relevant APIs as agent tools, without coding complexity.

We’ll examine each capability in more detail and discuss how they facilitate AI-driven development for teams looking to harness AI in for intelligent business processes.

AI protocol: Extending Postman’s API testing to AI models

Postman’s new AI Protocol extends its existing API testing platform to handle AI model interactions. By treating large language models like powerful APIs, development teams can systematically test both system and user prompts, configure model properties for desired creativity or predictability, and benchmark performance based on response time, accuracy, and cost. Collections of prompts act as versioned assets, allowing teams to track prompt changes over time, refine parameters, and maintain consistent test suites as new models or updated versions are released.

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The recent debut of DeepSeek R1 illustrates how quickly organizations scramble to test newly available foundation models for potential performance gains or cost savings. Rather than setting up parallel machine learning pipelines or adopting additional tools, teams can leverage Postman’s existing interface, environment variables, and versioning features to integrate model testing immediately. This approach helps prevent fragmentation across multiple LLM providers by enabling side-by-side comparisons and centralized metrics. Shortly after DeepSeek R1’s release, Postman customers were already evaluating their current prompt collections against both R1 and OpenAI’s o1 model to determine which option delivered the best results for their specific use cases.

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Agent Builder: Creating agentic workflows with a visual low-code tool

Postman’s Agent Builder uses the platform’s Flows visual programming interface to create multi-step workflows that integrate both API requests and AI interactions—no extensive coding required. With full integration of the new Postman AI protocol, developers can embed LLMs into their automation sequences to enable dynamic, adaptive, and intelligence-driven processes. For example, AI requests can enrich workflows with real-time data, make context-aware decisions, and discover relevant tools to address business needs. Flows also includes low-code building blocks for conditional logic, scripting capabilities for custom scenarios, and built-in data visualization and reporting, enabling teams to quickly tailor workflows to specific business requirements, reduce development overhead, and deliver actionable insights faster.

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This Agent Builder approach supports rapid experimentation, local testing, and debugging, effectively fitting into a developer’s “inner loop.” Collaboration features allow teams to label and section workflows, making it easier to share and explain complex automations with colleagues or stakeholders. For multi-service workflows, developers can confirm each step under realistic conditions using scenarios to ensure consistency and reliability well before final deployment. Scenarios can be versioned and shared, streamlining the process of testing and evaluating agents built with Flows.

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API Discovery and Tool Generation: Easy access to verified APIs

Postman’s API Discovery and Tool Generation capabilities add the ability to find and integrate the right APIs to use with AI agents. By leveraging Postman’s network of more than 100,000 public APIs, developers can automatically generate “agent tools,” removing the need to manually write wrappers or boilerplate code for those APIs. This scaffolding step includes specifying which agent framework (e.g., Node.js, Python, Java) and which target LLM service or library the agent will use, even if official SDKs don’t exist yet. As a result, teams can focus on core workflow logic rather than wrestling with setup details.

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Moreover, verified partner APIs in the catalog help ensure agents are configured accurately for critical business tasks. Instead of researching and integrating each API from scratch, developers can rely on the Postman network to surface endpoints, request payloads, and authentication specifications suited to specific AI-driven use cases. By consolidating discovery, documentation, and testing in one place, teams can filter through a vast API collection, preview endpoints, run sample requests directly in their browser or the Postman client, and then generate ready-to-run code. This results in faster onboarding, more reliable integrations, and a broader range of capabilities for AI-powered applications. Without these built-in safeguards and automation, developers would need to manually verify each API’s reliability, usage patterns, and code compatibility—an error-prone and time-consuming process.

A unified approach to AI-driven automation

By combining AI model testing, low-code agent building, and tool discovery in one platform, Postman helps developers standardize how AI workflows and traditional APIs intersect. Teams can build on familiar API practices—such as versioning, environment variables, and collaboration—while extending them to AI-powered services. This unified approach fosters consistent testing, quality standards, and data management across both conventional APIs and AI-driven workflows.

For organizations looking to operationalize AI, these capabilities provide a smooth pathway from prompt engineering and multi-LLM evaluation to production-grade intelligent automation, without juggling multiple platforms, integrations, or tools.

For deeper technical details and documentation, visit the official Postman AI Agent Builder documentation. Whether you’re a newcomer experimenting with LLMs or a seasoned pro looking for enterprise-grade testing and integration, Postman’s latest features aim to simplify and unify your AI development workflow.

Rodric Rabbah is the head of product for Flows at Postman. An accomplished entrepreneur and technologist, Rabbah founded Nimbella, a serverless cloud company that was successfully acquired by DigitalOcean, where he led the launch of DigitalOcean Functions. He is the main creator and developer behind Apache OpenWhisk, the open-source platform for serverless computing. He created OpenWhisk while at IBM Research, where he also led the development and operations of IBM Cloud Functions.

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