Core Problem with AI Agents - AI Agents relying on scraping websites and inferring meaning from HTML can lead to unpredictable behavior due to DOM shifts and pop-ups [1][2] - Prompt-engineering alone is insufficient for ensuring the reliable operation of AI Agents [2] - Agents require predictable contracts, which APIs provide through clear rules for requests, responses, and failure expression [2] Solution: Standardized API Access and Management - Standardizing API access, exposing APIs to Agents, and maintaining inspectability are crucial for debugging and monitoring [3] - Unified management, testing, and observability of raw API access are necessary to prevent chaos in the backend [3] - Teams building production-grade AI agents are converging on setting up infrastructure for API management [3] Postman's Solution for AI Agents - Postman offers a centralized hub for documenting, versioning, and accessing APIs [4] - Postman's MCP server exposes APIs directly to Agents, enabling them to call APIs without manual wiring [5] - Postman logs every request made by Agents with full history for debugging purposes [5] Key Takeaway - Agents understand interfaces, not intent; explicit interfaces lead to reliable behavior, while implicit interfaces lead to unpredictable behavior [5] - APIs, when built with the right infrastructure, provide the necessary context for AI [6]

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/ocO8UZkuFaAvi Chawla (@_avichawla):Devs often treat Agent context as an access problem.The thinking goes like this:"If there's no API, just scrape the website. And if scraping breaks, let the Agent use the browser like a human."This sounds reasonable until you try to run it in production.To see why, https://t.co/mq7APfuOH6 ...

Core Problem & Solution - Agents struggle with unpredictable behavior due to implicit interfaces derived from HTML and UI, leading to unreliable performance in production [1][2] - The industry needs predictable contracts for Agents, which APIs provide through clear rules for requests, responses, and error handling [2] - Standardizing API access, exposing APIs to Agents, and ensuring inspectability are crucial for debugging and monitoring, preventing chaos in the backend [3] Postman's Solution for AI Agents - Postman offers a centralized hub for documented, versioned, and accessible APIs, replacing ad-hoc API discovery [4] - Postman's MCP server exposes APIs directly to Agents, eliminating the need for custom integration code and manual wiring [5] - Postman logs every Agent request with full history, enabling precise debugging by showing what was sent, what was returned, and where failures occurred [5] Key Takeaway - Agents require explicit interfaces (APIs) for reliable behavior, as they understand interfaces, not intent [5] - APIs, when built with the right infrastructure, provide the necessary context for AI Agents to function effectively [6]