REST in Peace? GraphQL vs REST in the Age of Agentic AI

As Large Language Models (LLMs) transition from static chatbots to autonomous agents, the interface through which they interact with the world (APIs or tools) becomes the primary bottleneck for performance. While the industry has spent decades optimizing APIs for human developers and web browsers, the “agentic consumer” introduces a radical new set of requirements: semantic density, schema discoverability, and context window economy.

This talk presents a systematic, data-driven analysis comparing REST and GraphQL as the backbone for agentic tool calling. We move beyond the “REST is standard” versus “GraphQL is flexible” debate to measure what truly matters for an AI-driven workflow: Token Efficiency and Reasoning Accuracy.

Through a series of tiered experiments, ranging from atomic data retrieval to complex relational discovery, we quantify how REST and GraphQL perform in Agentic tool calling use-cases to help you determine which one you should use.

Why this matters: In an era where every token has a financial and computational cost, choosing the wrong API architecture will slow down your app and make your agent “dimmer” by burying signals in noise. Attendees will walk away with a rubric for selecting the right architecture based on their agent’s “Reasoning Depth” and a blueprint for building“LLM-friendly” interfaces that maximize the intelligence of their autonomous systems.