Mete Atamel

💡 This post is part of a series on Antigravity configuration:

• Where does Antigravity look for Agent Skills?
• Where does Antigravity look for MCP Servers?
• Where does Antigravity look for Rules and Workflows?
• Where does Antigravity look for Hooks?

In today’s post, I’ll talk about Hooks in Antigravity, what they are, how to configure them, and some shortcomings.

Hooks

Hooks allow you to run custom scripts or programs at specific points in the Antigravity agent lifecycle, such as before or after model or tool calls. They are a way to extend the functionality of the agent. Hooks run synchronously as part of the agent loop and they can be useful for things like adding context, validating actions, enforce policies or logging interactions.

💡 This post is part of a series on Antigravity configuration:

• Where does Antigravity look for Agent Skills?
• Where does Antigravity look for MCP Servers?
• Where does Antigravity look for Rules and Workflows?

I’ve been exploring different ways of customizing Antigravity and where it saves its various configuration files. For Agent Skills, see Where does Antigravity look for Agent Skills? and for MCP Servers, see Where does Antigravity look for MCP Servers?.

💡 This post is part of a series on Antigravity configuration:

• Where does Antigravity look for Agent Skills?
• Where does Antigravity look for MCP Servers?
• Where does Antigravity look for Rules and Workflows?

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.

💡 This post is part of a series on Antigravity configuration:

• Where does Antigravity look for Agent Skills?
• Where does Antigravity look for MCP Servers?
• Where does Antigravity look for Rules and Workflows?

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:

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.