Gists

Abstract

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.

Introduction

As the artificial intelligence landscape rapidly evolves, modern generative AI increasingly relies on autonomous agents equipped with sophisticated components, including system instructions, specialized skills, and Model Context Protocol (MCP) servers. To facilitate the development of such AI-driven applications, Google has released the Agent Development Kit (ADK) across multiple programming languages Ref. Among these, the ADK for TypeScript Ref offers distinct advantages for modern engineering paradigms:

Gists

Abstract

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.

Introduction

Recently, remote subagent support was introduced to the Gemini CLI. Ref With this feature, the Gemini CLI connects to remote subagents using the Agent-to-Agent (A2A) protocol, expanding its capabilities by delegating tasks to external services. I have previously published several articles discussing the A2A server architecture:

Gists

Abstract

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.

Introduction

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.

Gists

Abstract

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 toolchains (Google Antigravity and the Gemini CLI) while maintaining absolute 1:1 behavioral parity with the live GAS environment.

Gists

Abstract

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.

Introduction

Google Apps Script (GAS) is a powerful low-code platform that enables developers to integrate, automate, and extend Google Workspace with ease. Ref Typically, executing GAS requires the script to be hosted on Google’s servers via the Script Editor. While tools like clasp allow for local development and synchronization, running scripts from outside the Google ecosystem—such as from a local environment or a different cloud provider—often involves complex setups relying heavily on the Apps Script API or Web Apps. Ref

I got an awarded for publishing a top Google AI post for this article. Recursive Knowledge Crystallization: A Framework for Persistent Autonomous Agent Self-Evolution

Gists

Abstract

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 transferring this evolved SKILL to a completely clean environment, we verify that the agent can successfully implement complete, flawless client applications in a single attempt (zero-shot generation). This methodology not only circumvents the limitations of short-term memory dependency but also pioneers a new paradigm for cross-environment knowledge portability and automated system analysis.

Gists

Abstract

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.

Introduction

A2UI (Agent-to-User Interface) represents a paradigm shift in how users interact with generative AI. Originally open-sourced by Google and implemented in TypeScript and Python Ref, A2UI becomes even more powerful when integrated with Google Apps Script (GAS). This combination enables seamless access to the Google Workspace ecosystem, transforming static documents into intelligent, agentic applications.

Gists

Abstract

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.

Introduction: The Evolution of AI Interaction

The Official A2UI framework by Google marks a significant paradigm shift in how we interact with artificial intelligence. Short for Agent-to-User Interface, A2UI represents the evolution of Large Language Models (LLMs) from passive chatbots into active agents capable of designing their own functional interfaces. Building upon my previous research, A2UI for Google Apps Script and Bringing A2UI to Google Workspace with Gemini, I have refined this integration to support sophisticated, stateful workflows.

Gists

Abstract

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.