Explore how to build and orchestrate production-ready, type-safe AI agents using Google's TypeScript Agent Development Kit (ADK). This guide provides practical scaffolding patterns, multi-agent coordination strategies, and seamless integration techniques for deploying remote subagents within the Gemini CLI ecosystem.
Kanshi TANAIKE tanaikech
This article explores integrating remote subagents built with Google Apps Script into the Gemini CLI using the Agent-to-Agent (A2A) protocol. It demonstrates how bypassing standard authentication via local agent cards enables seamless execution of complex workflows while effectively overcoming Tool Space Interference (TSI) for massive toolsets.
Google Sheets recently introduced the SHEET and SHEETS functions. Because they automatically recalculate upon structural changes, developers can utilize them as custom triggers. This article demonstrates how to leverage these functions to detect sheet insertions, deletions, renames, and movements without requiring cumbersome installable triggers in Google Apps Script.
On February 23, 2026, Google introduced two pivotal built-in functions to Google Sheets: SHEET and SHEETS Ref. The SHEET function returns the index (sheet number) of a specified sheet or reference Ref. Meanwhile, the SHEETS function provides the total count of sheets within a spreadsheet Ref.
Recursive Knowledge Crystallization: Enabling Persistent Evolution and Zero-Shot Transfer in AI Agents
This paper presents a self-evolving framework, Recursive Knowledge Crystallization (RKC), designed to overcome the "Catastrophic Forgetting" inherent in autonomous AI agents. By persisting evolved technical insights into a universally readable SKILL.md file based on the Agent skills specification, this approach establishes long-term memory and cross-platform portability. The framework was empirically validated through the development of gas-fakes, a highly complex Node.js-to-Google Apps Script (GAS) emulation library. The results demonstrate that agents can autonomously internalize project-specific architectural patterns and environmental nuances. Consequently, the framework achieves Zero-Shot Knowledge Transfer across distinct toolcha
Discover how to seamlessly integrate Google Workspace with GitHub Actions using the gas-fakes library. This guide demonstrates running Google Apps Script locally and within CI/CD pipelines without deploying Web Apps. Automate workflows, secure credentials, and effortlessly interact with Google Drive and Sheets directly from your repository.
In the development of autonomous agents using Large Language Models (LLMs), restrictions such as context window limits and session fragmentation pose significant barriers to the long-term accumulation of knowledge. This study proposes a "self-evolving framework" where an agent continuously records and refines its operational guidelines and technical knowledge—referred to as its SKILL—directly onto a local filesystem in a universally readable format (Markdown). By conducting experiments across two distinct environments featuring opaque constraints and complex legacy server rules using Google's Antigravity and Gemini CLI, we demonstrate the efficacy of this framework. Our findings reveal that the agent effectively evolves its SKILL through iterative cycles of trial and error, ultimately saturating its learning. Furthermore, by tr
This is an appendix for https://gist.github.com/tanaikech/da47643a4b59578092f0129a9eb0c9c5, Medium, and DEV.
A.1. Directory Structure
Directory Structure for Antigravity:
This article demonstrates how to build an adaptive learning agent using Agent-to-User Interface (A2UI), Gemini, and Google Apps Script. We explore a system that generates personalized quizzes, tracks performance in Google Sheets, and dynamically adjusts difficulty to maximize learning efficiency within the Google Workspace ecosystem.
This article explores A2UI (Agent-to-User Interface) using Google Apps Script and Gemini. By generating dynamic HTML via structured JSON, Gemini transforms Workspace into an "Agent Hub." This recursive UI loop enables complex workflows where the AI builds the specific functional tools required to execute tasks directly.
This article details the development of Smart Stowage Optimizer, a web-based digital twin for logistics that bridges the gap between physical safety and artificial intelligence. By integrating Gemini 3 Pro, the system solves the 3D Bin Packing Problem (3DBPP) using advanced spatial reasoning. Built with React 19 and Three.js, the application visualizes physics-aware load stability in real-time, offering a comparative analysis between traditional heuristic algorithms and modern generative AI agents.
NewerOlder