"As agentic AI moves from experimentation into real-world systems, one foundational dependency is being stress-tested more than any other: APIs. Autonomous agents can reason, plan, and chain actions, but they can only act through the interfaces we give them. And many of those interfaces were never designed for machines that operate without constant human supervision, creating a need for agent-ready APIs."
"What's becoming increasingly clear is that agentic AI doesn't just consume APIs - it exposes their weaknesses. APIs that worked well for years suddenly fail when placed inside autonomous workflows. Error handling breaks down. Context is lost. Agents get stuck or behave unpredictably. In short, the agent doesn't fail - the interface does. This theme surfaced repeatedly in a recent ODSC podcast conversation with Sterling Chin, Senior Developer Advocate at Postman, whose work sits at the intersection of APIs, AI tooling, and agentic workflows."
Agentic AI moves from experimentation into real-world systems and places unprecedented demands on APIs. Autonomous agents can reason, plan, and chain actions, but they can only act through available interfaces. Many APIs were designed for human-driven workflows and lack robust error semantics, clear schemas, and machine-readable context. Ambiguous errors or unclear responses cause agents to fail silently, retry endlessly, or hallucinate fixes. Multi-step autonomous workflows amplify these failures because each step depends on prior success. Reliable agent operation requires APIs to be agent-ready, with precise contracts, deterministic error handling, and contextual responses that enable automated recovery.
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