Mete Atamel

In my previous post, I explored where Antigravity (AGY) looks for agent skills. In this post, I’ll briefly explore the same for Model Context Protocol (MCP) servers and mention the MCP server caching that’s worth knowing about.

MCP Configuration

AGY docs on MCP: https://antigravity.google/docs/mcp

Thankfully, MCP server configuration is more straightforward than skills. There are two places for MCP configuration:

• Workspace: .agents/mcp_config.json
• Global: ~/.gemini/config/mcp_config.json

This applies to all three flavours: AGY, AGY CLI, and AGY IDE.

Skills are an open standard for extending agent capabilities. A skill is a folder containing a SKILL.md file with instructions that the agent can follow when working on specific tasks.

Antigravity (AGY) also supports agent skills, but it’s not obvious at all where to save these skills. This is mainly because:

1. AGY has 3 different flavours (AGY, AGY IDE, AGY CLI) that look in slightly different locations for skills.
2. AGY has 3 different levels for skills: project/workspace level, product level, and global.
3. The documentation seems to be out of date.

In today’s post, I’ll run some experiments to understand where exactly AGY looks for skills in each of its flavours. Along the way, we’ll learn about where AGY saves its state and built-in skills.

In my Agent Protocols - MCP, A2A, A2UI, AG-UI post, I provided a high-level overview of some of the main agent protocols: MCP, A2A, A2UI, and AG-UI. In the Agent-User Interaction Protocol (AG-UI) with Agent Development Kit (ADK) post, I zoomed into the AG-UI protocol and showed how it can be used with Google’s Agent Development Kit (ADK).

In today’s post, I’ll zoom into the A2UI protocol and show how it can be used with Google’s Agent Development Kit (ADK).

In my previous post, I provided a high-level overview of some of the main agent protocols: MCP, A2A, A2UI, and AG-UI. In this post, I will zoom into the AG-UI protocol and show how it can be used with Google’s Agent Development Kit (ADK).

Recap: Agent-User Interaction Protocol (AG-UI)

AG-UI is an open, lightweight, event-based protocol, created by CopilotKit, that standardizes how agent backends connect to agent frontends.

It feels like every other month, there is a new agent protocol and it’s getting hard to keep track of which protocol is used when. Recently, I looked into some of the main agent protocols and in this post, I will provide a high-level overview of MCP, A2A, A2UI, and AG-UI.

TLDR

If you’re too busy or too impatient to read the whole blog post, here’s a summary of the protocols along with links to samples in my repository:

The Gemini ecosystem has evolved into a comprehensive suite of models, tools, and APIs. Whether you are “vibe-coding” a web app or deploying enterprise-grade agents, navigating the options can be overwhelming.

I am happy to announce a new Gemini for Developers codelab. This codelab is designed to teach you everything you need to know about the Gemini ecosystem, from the different model flavors to tools powered by Gemini to integration using the Google Gen AI SDK and the new Interactions API.

GenAI is rapidly moving from simple “prompt-and-response” patterns to complex, agentic workflows. To support this shift, Google recently introduced the Interactions API, a new unified foundation designed specifically for building with both models and agents.

In this post, I’ll introduce the core concepts of the Interactions API and walk through some of the samples available in my genai-samples repository.

What is the Interactions API?

Traditionally, developers had to use the Gemini API to talk to the models and another framework like Agent Development Kit (ADK) to create and manage agents. Currently in beta, the Interactions API simplifies this by providing a single interface for:

In October 2024, Microsoft announced the Microsoft.Extensions.AI.Abstractions and Microsoft.Extensions.AI libraries for .NET. These libraries provide the .NET ecosystem with essential abstractions for integrating AI services into .NET applications from various providers such as Open AI, Azure, Google.

Today, we’re happy to announce the Google.Cloud.VertexAI.Extensions library. This is the Vertex AI implementation of Microsoft.Extensions.AI. It enables .NET developers to integrate Google Gemini models on Vertex AI via the Microsoft.Extensions.AI abstractions.

Google Antigravity transforms your regular IDE into an agentic development platform. In my previous blog posts, I showed some of the unique features of Antigravity compared to a regular IDE. In today’s blog post, I’ll talk about what makes Antigravity truly unique: Its ability to spin up and manage multiple agents.

Antigravity modes

Antigravity has two modes: Editor and Agent Manager.

Editor is your familiar IDE with an agent on the side to help you with tasks. You can read more about it in my previous blog post Google Antigravity Editor - Tips & Tricks and learn how to provide feedback to the agent in Provide Feedback to Google Antigravity and how to customize it in Customize Google Antigravity with rules and workflows.

At the heart of Google Antigravity is its ability to effortlessly gather your feedback at every stage of the experience. In this blog post, I will show you all the different ways you can provide feedback to Antigravity.

As the agent works on a task, it creates different artifacts along the way:

• An implementation plan and a task list (before coding)
• Code diffs (as it generates code)
• A walkthrough to verify the results (after coding)

These artifacts are a way for Antigravity to communicate its plans and progress. More importantly, they’re also a way for you to provide feedback to the agent in Google docs style comments. This is very useful to effectively steer the agent in the direction you want.