This document contains system prompt instructions for an AI model designed to function as a Gmail Assistant. It details specific protocols for managing email threads, including deciding between providing single or multiple reply options based on user input complexity. The instructions cover tone maintenance, strict prohibitions against hallucinating information not present in context, and precise formatting rules for greetings and sign-offs.
* Decision logic for generating one vs three replies
* Guidelines for maintaining professional email etiquette
* Constraints to prevent making up non-existent information
* Rules for extracting and listing action items from threads
* Decision logic for generating one vs three replies
* Guidelines for maintaining professional email etiquette
* Constraints to prevent making up non-existent information
* Rules for extracting and listing action items from threads
Google explores the transition from traditional deterministic automation to agentic AI within Site Reliability Engineering. As system complexity grows due to microservices, cloud scale, and increased code generation, Google is implementing SRE AI across the entire software development lifecycle to enhance reliability. The approach includes using agents for automated runbook improvement, advanced anomaly detection, incident management orchestration, and autonomous investigation utilizing observability data.
- Moving from deterministic automation to agentic AI models
- Integration of AI in reliability design and documentation
- Using anomaly detection rather than static thresholds for alerting
- Orchestrating incident response via communication monitoring and automated summaries
- Leveraging historical data through AI Insights for risk management
- Adhering to principles of transparency, security, and agent identity
- Moving from deterministic automation to agentic AI models
- Integration of AI in reliability design and documentation
- Using anomaly detection rather than static thresholds for alerting
- Orchestrating incident response via communication monitoring and automated summaries
- Leveraging historical data through AI Insights for risk management
- Adhering to principles of transparency, security, and agent identity
Google announces several new AI-powered features designed to enhance productivity within the Google Workspace ecosystem and apps.
- Conversational voice features in Gmail Live, Docs Live, and Keep
- Google Pics image generation and precise editing tool
- Enhanced AI Inbox for streamlined task management
- Gemini Spark 24/7 personal AI agent integration
- Conversational voice features in Gmail Live, Docs Live, and Keep
- Google Pics image generation and precise editing tool
- Enhanced AI Inbox for streamlined task management
- Gemini Spark 24/7 personal AI agent integration
The author explores how Gemini Scheduled Actions represents a significant shift in Android automation by moving from rigid, trigger-based logic like Tasker to an intent-first architecture powered by Large Language Models. Unlike traditional tools that require programming knowledge and are prone to breaking when UI changes occur, Gemini understands natural language requests and manages complex workflows across devices via the cloud.
Key points:
* Comparison between brittle IFTTT engines and flexible LLM-based automation.
* The benefit of cross-device synchronization through Google accounts.
* Using the desktop web interface for easier setup and access to an Inspiration Gallery.
* Practical use cases including automated SEO idea generation, sports updates, grocery list creation in Google Keep, and email summaries.
* Current limitation of up to 10 active scheduled actions at a time.
Key points:
* Comparison between brittle IFTTT engines and flexible LLM-based automation.
* The benefit of cross-device synchronization through Google accounts.
* Using the desktop web interface for easier setup and access to an Inspiration Gallery.
* Practical use cases including automated SEO idea generation, sports updates, grocery list creation in Google Keep, and email summaries.
* Current limitation of up to 10 active scheduled actions at a time.
An exploration of the Google Agent Development Kit (ADK), a modular open-source framework designed to streamline the creation, deployment, and orchestration of AI agents. While optimized for Gemini and the Google Cloud ecosystem via Vertex AI, the kit remains model-agnostic and supports multiple programming languages including Python, Go, Java, and TypeScript. The review highlights the toolkit's ability to handle multi-agent architectures, long-term memory, and tool integration through agent skills.
Key points:
* Support for diverse programming environments (Python, Go, Java, TypeScript).
* Integration with Vertex AI Agent Engine and Google Cloud Run.
* Built-in developer UI (ADK Web) for debugging, tracing, and evaluation.
* Use of the open agent skills format for expanding agent capabilities.
* Comparison against competitors like Amazon Bedrock AgentCore and LangChain.
Key points:
* Support for diverse programming environments (Python, Go, Java, TypeScript).
* Integration with Vertex AI Agent Engine and Google Cloud Run.
* Built-in developer UI (ADK Web) for debugging, tracing, and evaluation.
* Use of the open agent skills format for expanding agent capabilities.
* Comparison against competitors like Amazon Bedrock AgentCore and LangChain.
A Python package designed to provide production-ready templates for Generative AI agents on Google Cloud. It allows developers to focus on agent logic by automating the surrounding infrastructure, including CI/CD pipelines, observability, security, and deployment via Cloud Run or Agent Engine.
Key features and offerings include:
- Pre-built agent templates such as ReAct, RAG (Retrieval-Augmented Generation), multi-agent systems, and real-time multimodal agents using Gemini.
- Automated CI/CD integration with Google Cloud Build and GitHub Actions.
- Data pipelines for RAG using Terraform, supporting Vertex AI Search and Vector Search.
- Support for various frameworks including Google's Agent Development Kit (ADK) and LangGraph.
- Integration with the Gemini CLI for architectural guidance directly in the terminal.
Key features and offerings include:
- Pre-built agent templates such as ReAct, RAG (Retrieval-Augmented Generation), multi-agent systems, and real-time multimodal agents using Gemini.
- Automated CI/CD integration with Google Cloud Build and GitHub Actions.
- Data pipelines for RAG using Terraform, supporting Vertex AI Search and Vector Search.
- Support for various frameworks including Google's Agent Development Kit (ADK) and LangGraph.
- Integration with the Gemini CLI for architectural guidance directly in the terminal.
Google's recent Pixel Drop introduces a groundbreaking, albeit unusual, screen automation feature for Gemini. Unlike previous assistants limited by strict APIs, Gemini uses visual reasoning to interact with third-party applications directly. By reading on-screen elements like menus and text fields, the AI can perform complex tasks such as ordering food or booking rides within a secure sandbox. While this offers significant benefits for multitasking and accessibility, it also raises critical questions regarding privacy, the stability of automation when app UIs change, and the potential disruption of the ad-supported economy. Currently, this beta feature is limited to high-end devices like the Pixel 10 and Galaxy S26 series in select regions.
Gemini is an AI assistant integrated into Pixel phones to boost productivity. It streamlines daily life by automating tasks (like food and ride orders), managing Gmail and Calendar, planning travel via Maps, and organizing schedules through task apps and Pixel Screenshots. Advanced features are available on the Pixel 10 Pro via the Google One AI Premium Plan.
Google has released a new command-line interface for Google Workspace apps, designed to make it easier for AI agents like OpenClaw to interface with Google apps like Docs, Drive, and Gmail. The tool offers over 100 Agent Skills to simplify agent actions and supports integrations with other AI agents beyond OpenClaw. While published by Google, it's not an officially supported product, so use it at your own risk.
This article discusses how to effectively utilize Large Language Models (LLMs) by acknowledging their superior processing capabilities and adapting prompting techniques. It emphasizes the importance of brevity, directness, and providing relevant context (through RAG and MCP servers) to maximize LLM performance. The article also highlights the need to treat LLM responses as drafts and use Socratic prompting for refinement, while acknowledging their potential for "hallucinations." It suggests formatting output expectations (JSON, Markdown) and utilizing role-playing to guide the LLM towards desired results. Ultimately, the author argues that LLMs, while not inherently "smarter" in a human sense, possess vast knowledge and can be incredibly powerful tools when approached strategically.
