Google Antigravity: Orchestrating the Next Phase of Agency

Full disclosure: I work at Google. Antigravity ships under our logo. Everything here comes through that lens, so calibrate accordingly.

I’m going to be candid about what works, what doesn’t, and where this fits among the growing pile of agentic dev tools. The worst thing I could do — for Google’s credibility and my own — would be writing a product puff piece. Nobody needs another one of those.

What Antigravity Actually Is #

Google announced Antigravity alongside Gemini 3 back in November 2025. The short version: an agent-first IDE. Not an IDE with agent features bolted on (that describes Cursor and Copilot, fundamentally). An IDE designed from scratch around developers defining tasks while agents execute them.

Talk: Architecting Multi-Agent Developer Workflows

I gave a talk recently on multi-agent developer workflows. The prep forced me to organize a lot of scattered thinking into something presentable, and honestly that exercise alone justified the talk. This post distills the core arguments and architectural patterns I covered — not a transcript, more like the cleaned-up version of my speaker notes plus some refinements I’ve made since.

The punchline, for those who want it upfront: multi-agent systems work when the problem genuinely decomposes into specialized roles. But the instinct to split everything into agents? Same instinct that over-decomposed monoliths into too many microservices circa 2016. The pattern helps when it helps; it hurts when applied out of habit.

MCP: Using Streamable HTTP for Real-Time AI Tool Use

If you’ve been building MCP integrations, you probably noticed the transport layer changed underneath you. The March 2025 spec update—version 2025-03-26—deprecated the HTTP+SSE transport in favor of Streamable HTTP. The change sounds minor. Swapping one HTTP-based approach for another. But the implications ripple through everything from auth middleware to deployment topology.

Let me walk through what actually changed, why it matters, and what you need to know if you’re building or maintaining MCP servers.

Panel: Infrastructure for AI Agents in Messaging

I moderated a panel last week on infrastructure for AI agents in messaging, and one observation kept coming up: the chatbot era taught us almost nothing useful about what’s coming next.

Those early bots — the “type 1 for billing, type 2 for support” kind — were basically IVR menus in a text box. Rigid decision trees. No real state management. No tool orchestration. No fallback beyond “I didn’t understand that, please try again.” You know the drill.

Human-on-the-Loop AI Control

I was in a meeting last month where someone from a financial services team walked us through their AI oversight process. Every automated trading decision above a certain threshold gets routed to a human reviewer. The reviewer checks the decision, approves or rejects, and the system proceeds.

“How many decisions per day?” I asked.

“About forty thousand.”

“And how many reviewers?”

“Three.”

Nobody laughed. I think because everyone in the room already knew the answer before I asked. The math doesn’t work. It never did, really, but we’re only now being honest about it.

Panel: M2M Trust and the Security Gap in Agentic AI

I moderated a panel last week on machine-to-machine trust in agentic AI systems, and the conversation went sideways almost immediately. The plan was to discuss identity management frameworks. What we ended up talking about—for the better part of an hour—was how fundamentally unprepared enterprise security is for a world where AI agents act autonomously on behalf of organizations.

The core problem is simple to state and terrifying to think through: every AI agent needs an identity. Every identity is an attack surface. And we’re scaling agents faster than we’re scaling the governance around them.

Mastra: Empowering JS Devs to Build Agents in TS

If you’re a TypeScript developer who’s tried to build AI agents, you’ve probably felt the gravitational pull toward Python. LangChain, CrewAI, AutoGen—the mature agent frameworks all live in Python land. The JavaScript side has LangChain.js (a port, not a native design) and the Vercel AI SDK (great for streaming, thin on agent orchestration). The gap is real, and honestly? It’s been frustrating to watch.

Mastra is a new framework that aims to close it. Built by Abhi Aiyer, Sam Bhagwat, and Shane Thomas—the team behind Gatsby.js—it’s a TypeScript-first agent framework that just joined Y Combinator this month. I’ve been poking at it for the past week, and I want to share what I’ve found.

Agentic Interfaces vs Copilots

Every pitch deck I’ve seen this quarter mentions “agentic AI” somewhere in the first three slides. It’s the new magic word. Investors love it. Founders love it. Gartner named it their top technology trend for 2025, predicting that 15% of daily work decisions will be made autonomously by 2028.

I’m building AI products right now at DreamFlare, and I think most companies are getting this wrong. Not because agentic AI isn’t real — it is — but because they’re skipping the step that actually works.

AI Agents: The Transition from Chatbots to Actors

Last week one of our engineers at DreamFlare asked me a question that stuck: “When does a chatbot become an agent?” My first instinct — say something about autonomy — felt too clean. The real answer runs messier than that.

A chatbot reads. An agent reads and writes.

The distinction sounds simple, but software architecture reshapes entirely around it. And we’re right in the middle of that shift, watching it happen in real time, building pieces of it ourselves.