Why AI agents need an integrations platform

AI agents are rapidly advancing and are expected to handle increasingly complex workflows, but many fail when interacting with real-world APIs due to intricate issues like authentication, schemas, rate limits, and edge cases. To overcome these challenges, AI agents require a robust integrations platform that acts as a translation layer between human-like intents and API-specific executions, handles authentication complexities, and provides observability for debugging. Such platforms ensure security and robustness by managing token lifecycles and scopes, offering logs and monitoring capabilities, and establishing constraints on API interactions. They also introduce clear "contracts" to define expected inputs and outputs, enhancing reliability and enabling agents to move beyond mere demonstrations to production-ready systems. While Model Context Protocols (MCPs) offer a standardized approach for agent-API interaction, they fall short without the underlying infrastructure to manage the complexities of real-world APIs, underscoring the necessity of a comprehensive integrations platform.