AI-native API lifecycle: Postman Git workflows and LLM-ready specs
Postman introduced AI-native, Git-based API workflows and a central API catalog while LLMs begin to consume and co-author API specs, pushing teams to make documentation machine-optimized and governed. Postman’s latest platform update brings Agent Mode into Git, where it understands collections, definitions, and underlying code to cut manual work in debugging, test writing, and keeping collections in sync, alongside native Git workflows for specs, tests, mocks, and environments and a new enterprise-wide API Catalog for visibility and ownership tracking ([InfoWorld](https://www.infoworld.com/article/4140102/postman-api-platform-adds-ai-native-git-based-workflows.html)). It can also coordinate multi-step changes using inputs from MCP servers tied to Atlassian, Amazon CloudWatch, GitHub, Linear, Sentry, and Webflow, and publish docs, sandboxes, and SDKs in one place. As agentic access to APIs grows, specs must be unambiguous for machines as well as humans, emphasizing well-structured descriptions, precise natural language, sample requests/responses, and consistent versioning to avoid drift and misuse ([Nordic APIs](https://nordicapis.com/how-llms-are-changing-the-way-we-build-api-specifications/)). This shifts API design from merely machine-readable to truly machine-optimized. For teams building research copilots or smarter portals, Perplexity’s APIs offer web-grounded answers (Sonar), agentic research workflows, and ranked search that can backstop doc Q&A, discovery, and RAG without maintaining your own crawl pipeline ([DataStudios overview](https://www.datastudios.org/post/perplexity-ai-api-access-and-developer-use-cases-overview-platform-structure-key-capabilities-and)).