X @Avi Chawla

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]