What I Learned at a Google x Postman Meetup About the State of AI Development Tooling

I went to a meetup co-hosted by Google and Postman today. I was expecting a product demo. What I walked away with was a much clearer picture of where AI development tooling is heading in 2026, and how one company is approaching the problem of connecting all the pieces together.

This is not a review or a recommendation. I just want to share what was presented and what I found interesting as a backend engineer trying to make sense of this space.

Stitch: Designing UIs With Prompts

The first tool they showed was Stitch, a design tool from Google Labs. You describe what you want in plain English and it generates UI mockups on an infinite canvas. Since the March 2026 update, it comes with a design agent that keeps track of the full project context, voice input for quick iterations, and something called DESIGN.md, a markdown file that captures your design system so you can reuse it across projects.

What caught my attention was the prototyping. You connect screens together, hit Play, and click through the app flow before writing any code. You can export to Figma, grab the HTML/CSS with Tailwind, or send the design to other tools through MCP.

AI Studio: Turning Designs Into Code

Next up was Google AI Studio and its Build mode. The idea is simple: take a Stitch design or a text description, feed it to Gemini, and get a working React app out. A built-in preview lets you test and iterate by prompting.

They claimed the first pass covers 70 to 90 percent of what you need. I have not tested this myself on a real project so I cannot confirm that, but the live demo was convincing enough.

Antigravity: An Agent-First IDE

This one was new to me. Antigravity is a VS Code fork built around the idea that AI agents should be first-class citizens in the IDE, not just a sidebar chat. It has two views: a classic editor for hands-on coding, and a Manager View where you can run multiple agents in parallel on different tasks. One agent works on the backend while another handles the frontend. Each one shows you what it did through artifacts like plans, diffs, and screenshots before anything gets merged.

It runs on Gemini 3 Pro and also supports Claude Opus 4.6 and GPT-OSS. Currently free during public preview.

Jules: Async Coding via Pull Requests

Jules is a coding agent that works asynchronously. You assign it a task, it clones your repo into a cloud VM, does the work, and sends back a pull request on GitHub. You do not need to watch it work. Bug fixes, tests, dependency upgrades, feature builds. They mentioned over 140,000 code improvements produced through the platform since it left beta.

The async model is what makes it different from tools like Cursor or Claude Code. You can queue up tasks and come back later.

Spec Kit vs. Conductor: Planning Before Coding

This was the part that interested me the most as a developer. Both tools try to solve the same problem: AI chat sessions are stateless. The agent loses all context between conversations. That makes it unreliable for anything beyond quick fixes.

Spec Kit, from GitHub, uses a four-phase workflow (Specify, Plan, Tasks, Implement) and generates Markdown artifacts. It is agent-agnostic and works with Copilot, Claude, or Gemini.

Conductor is Google’s answer. It is a Gemini CLI extension that structures work into tracks. Each track produces a spec.md and a plan.md. You approve the plan before any code gets generated. If something breaks during implementation, the agent tries to fix it before coming back to you. All state lives in Markdown files inside your repo, so you can pause, resume, or switch machines without losing progress.

Worth noting: the community has already ported Conductor to Claude Code as a plugin. The approach is not locked to any specific model.

Vertex AI: Agents in Production

The enterprise layer is Vertex AI Agent Builder. This is where you build and deploy multi-agent systems using the Agent Development Kit (ADK), which supports Python and Java. Agent Garden provides starter templates. Agent Engine handles the runtime with persistent sessions, memory, and sandboxed execution. It integrates with BigQuery, Workspace, and over 100 connectors through Apigee.

For larger organizations, tool governance goes through the Cloud API Registry, and agents can communicate with each other using the A2A protocol.

The Takeaway: Integration as Strategy

The interesting thing about this meetup was not any single tool. It was seeing how all of them connect.

Design in Stitch. Prototype in AI Studio. Develop in Antigravity. Delegate tasks to Jules. Plan with Conductor. Deploy on Vertex AI. All linked through MCP. All running on Gemini.

That level of integration across the full development lifecycle is something I have not seen from other players in this space. Anthropic makes excellent models and created the MCP protocol, but its tooling stops at Claude Code and the API. OpenAI has ChatGPT and a mature API, but nothing equivalent for design, IDE-level agentic development, or enterprise agent orchestration.

Whether this integrated approach is better than picking the best tool at each step is an open question. Some teams will prefer the flexibility of mixing providers. Others will value having everything work together without configuration. Both approaches have trade-offs.

But it is clear that the conversation around AI development is shifting. It is less about which model scores highest on a benchmark and more about which workflow gets you from idea to production with the least friction. Google is making a strong case that integration is the answer. Time will tell if developers agree.