Abstract
Integrating autonomous AI agents into enterprise architectures exposes critical security and latency vulnerabilities. The Autonomous Google API Agent (AGAA) solves this by enforcing a deterministic, zero-trust execution framework directly within Google Apps Script (GAS). By merging GASADK, dynamic REST endpoint resolution via GoogleApiApp, and the Developer Knowledge API through the Model Context Protocol (MCP), AGAA executes complex cross-domain workflows exclusively via natural language. It autonomously researches API schemas, mitigates server-side formula latencies, handles recursive pagination, and mathematically enforces local Role-Based Access Control (RBAC). AGAA enables true "Vibe Coding" across all Google APIs—including Workspace, Analytics, and YouTube—without bloated client libraries.
1. Introduction: The Evolution of Agentic Orchestration
Deploying generative AI directly against enterprise infrastructure requires more than clever system prompts; it requires an uncompromising orchestration layer. Blindly trusting a Large Language Model (LLM) with broad OAuth scopes is a recipe for catastrophic data loss.
Recently, I published GASADK (Agent Development Kit for Google Apps Script) [Ref 1, Ref 2], a runtime framework designed to bring autonomous agent capabilities to the stringent memory and execution limits of the GAS ecosystem. However, invoking the vast universe of Google APIs within an agentic loop immediately presents a scaling problem. Hardcoding static client libraries and custom tool wrappers for hundreds of dynamic REST endpoints bloats the LLM's context window, exhausts token limits, and cripples inference latency.
