From Assistant to Autonomous Partner: My First Contact With Google's Antigravity

For too long, AI coding tools felt like an autocomplete on steroids. They were reactive, waiting for a prompt, and only capable of tackling discrete, simple tasks. I thought things would stay this way for longer, but Google has released its new Antigravity, an advanced agentic development platform designed to enhance coding projects.

Antigravity embraces this philosophy, moving us from “AI-assisted” to “agent-native” development. At its core lies Gemini 3 Pro (and notably, supports other models like Claude Sonnet 4.5 and GPT-OSS) to not just suggest the next line of code, but to take true, autonomous action across the three core development surfaces: the code editor, the terminal, and the browser.

I genuinely believe the future lies in agentic AI: entities that can reason, plan, and execute complex workflows on their own. So I was very excited and decided to give Antigravity a shot and see what it was all about.

A day flowing in Antigravity

The first step was finding a case, a project that I could take using it. I have been working on deploying a prediction model for a client lately. Although this is a new challenge, with its own characteristics and unique architecture, it also is the type of project I have done many times over, thus using Antigravity to revisit old designs and models seemed a perfect opportunity.

The very first thing that has impressed me is how serious it takes the critical challenges of agentic systems: transparency, accountability, and seamless integration. My first takeaway is that Antigravity is not just a new feature for an IDE, it is the architecture for the “AI-infused” enterprise I have been discussing for months.

My interactions were pretty much like if I was supervising a junior developer, literally debating and explaining how I wanted the model to be developed, how data should be treated, how functions should be called, how tests should be performed, and how the whole thing should be integrated and deployed.

Through the experience, four things stood out:

1. The Artifacts System: Trust Through Verifiable Outputs

The “black box” is the silent killer of trust in AI. How can you sign off on code if you cannot verify the steps the agent took? Antigravity solves this with Artifacts: structured, verifiable evidence of the agent’s actions.

I noticed that instead of sifting through thousands of raw logs, I was presented with clear Task Lists, Code Diffs, Screenshots of the UI after changes, and even Browser Recordings of end-to-end tests. It had an interesting impact on me, as I moved from line-by-line debugging to high-level review and architecture, elevating my analysis and judgment by automating the tedious execution and providing audit-ready proof.

It felt like Antigravity has accountability built in by design.

2. Cross-Surface Agentic Control

An agent is only as good as its access to the environment. Antigravity agents are truly cross-surface, meaning they can write and refactor code in the editor, run terminal commands to install dependencies, run tests, and spin up a local server.

One cool feature is how it acts on the browser to test its own work in a real Chrome instance, verify the output, and even record the user flow.

I delegated a task (“build a small Python app with a dynamic event trigger and full search functionality”) and the agent autonomously handled the dependencies, the coding, the local launch, and the test validation. It delivered a ready-to-review result and its own proof of work. This is no small potatoes.

3. Multi-Agent Orchestration with Manager View

For large-scale enterprise projects, we know that successful architecture is about orchestration, not isolated tools. Antigravity has the Manager View, a “mission control” type of dashboard designed for high-level oversight.

I am impressed with parallelism: I was able to spawn and monitor multiple agents. A Refactoring Agent cleaning up an authentication module in one workspace while another was implementing a new front-end feature in a parallel workspace. This is the practical realisation of multi-agent systems in production, moving the developer’s role to that of an architect and orchestrator, maximising velocity by parallelising tasks that were previously sequential and context-switching heavy.

4. Continuous Feedback Loops and Self-Improvement

Integrated feedback that kept context and persistence: Antigravity allows you to add Google Docs-style comments directly onto any Artifact (a screenshot, a plan, or a code diff) without stopping the agent’s work. The agent seamlessly incorporates this feedback and continues its execution, ensuring continuous refinement. This solves the “20% problem” I have seen derail so many POCs.

Although it is not full knowledge management capability, it sure is a strong step towards it. Agents can learn from past projects, storing successful code snippets, execution patterns, and architectural decisions to an internal knowledge base. This means your agents get smarter, and your enterprise memory grows with every project.

After the rush

As I am writing this, I was able to achieve in one day what used to take me a full sprint of work, full of syncs, dailies, code-review sessions, refactoring, and who-knows-how-many versions. This has strengthened my belief that this type of AI development, embodied by Antigravity, is the future.

It is an urgent call for us as leaders (CTOs, CDOs, and Product Heads) to step up and lead the charge.

Antigravity and its competitors will redefine roles, starting yesterday. However we need to view this not as a job replacement, but a skill elevation. Our junior developers become “AI Supervisors”, learning to architect and validate rather than just implement line-by-line.

It is not all praises and high sugar rush though. Documentation of the final artifact result is the low-point that I believe deserves attention from Google’s development team. I might be wrong but I think there is still a lot to do till Antigravity can autonomously run a full Dev/Sec/Ops cycle on its own. I might have missed something but if you are running a tech team not only focused on developing brand new things but also sustaining, patching, and updating legacy code and modes, the documentation on how things were built, the vulnerability analysis, the governance structure, the quality test results, and other aspects central to a decent technology area will still require a serious dose of old-fashion human work.

This is the perfect segue for an old message of mine, still fresh and relevant: focus on governance and observability. The complexity of autonomous agents demands a robust central nervous system. We must build the ethical and operational guardrails to ensure agents are effective, compliant, and accountable. People is still central, documentation is even more important, and governance is paramount.

With that caveat, Antigravity is promising and easy to use. I believe it is capable of delivering innovation at a velocity we could only dream of before.