Beyond the Vibe: Engineering with Google’s Antigravity

The honeymoon phase of “vibe coding”, that magical flow where you describe a feature and an LLM spits out working code, is exhilarating. We are democratizing the ability to create, and some amazing applications are coming to life. But the definitions of “good” and “good enough” are based on context. That definition can morph depending on when we ask the question: “Is this good?”

In building CIL Studio, an AI-native comic studio, I’ve had to move beyond vibing. Initial outputs were good enough for validating concepts, but insufficient to meet my larger ambitions. I needed more rigour, better engineering, and a more robust implementation. To do so, I’m thoughtfully engaging with Google’s Antigravity.

This isn’t just about making the AI write code; it’s about configuring an environment where architectural integrity and human reasoning are realized in software that has the “ilities” — stability, reliability, maintainability, and scalability.

Google’s Antigravity: The Agentic IDE

Antigravity is an agentic AI development environment , currently in public preview, that treats AI as a development team rather than just a coding assistant. Because the platform is still evolving, it has its quirks. Features like the automated Knowledge system are not yet working consistently for everyone. Still, the tool is powerful, and we’re creating some interesting software together!

Context: The Engineered Brain

The secret to disciplined AI development isn’t better prompting, it’s better context. Every agent interaction must be grounded in established rules, workflows, skills, and tools. This is the foundation that separates engineered software from vibed software.

Within my project, I’ve built a structured knowledge layer inside an .agent/ directory. A set of Markdown files defines the mental models and system constraints that every agent must internalize before acting:

• ARCHITECTURE.md: The technical blueprint covering our frontend, backend, data layer, and Google ADK orchestration.
• STUDIO_AGENTS.md: The organizational roster defining roles and responsibilities across specialized agent teams.
• TESTING_GUIDE.md: Quality standards emphasizing mock-heavy pytest and vitest approaches.
• UX & Studio-specific files: Details about unique UX constructs and the core concepts the agents support.

This is how the “ilities” get realized. Reliability comes from a documented testing strategy the agents are required to follow. Scalability is grounded in an explicit architecture. Maintainability is enforced by shared conventions that every agent works from, not just the human.

Skills: Specialized Instructional Modules

Beyond the knowledge files, I’ve curated a library of Skills, specialized instructional modules that give agents deep expertise in specific domains. Rather than hoping the model knows best practices, they are explicitly codified.

A few examples from my setup:

• architecture-review: Patterns for modern architecture, DDD, and system scalability.
• autonomous-agents: Reliability, self-correction, and goal decomposition.
• google-adk-patterns: Best practices for Google ADK and agent-to-agent protocols.
• tool-design: Architectural reduction patterns for building effective agent tools.

Agent capability isn’t something you get out of the box. It’s something you design.

Workflows: The End of Chaos

To maintain discipline, I’ve moved away from “Just do X” prompts. Instead, I use standardized protocols for common tasks.

For example, a /start-task workflow mandates knowledge consultation, platform alignment, and an explicit implementation plan, connected to my backlog, before any code is written. Platform alignment means the agent confirms which MCP tools are available and relevant before proceeding, so it’s never reaching for a tool that isn’t there, or ignoring one that is.

The Knowledge Gap: Automated vs. Engineered Memory

Antigravity includes a native Knowledge system intended to act as long-term memory, distilling chat logs into persistent Knowledge Items. If you’ve checked yours and found an empty view, you’re not alone.

But working around this limitation led me somewhere more interesting. Rather than waiting for automated memory to work, I engineered it intentionally. My MD files and skills library are the memory system. They don’t distill conversations after the fact, they encode decisions, patterns, and constraints up front.

Automated distillation captures what happened. Engineered memory captures what should always be true.

Providing Antigravity with Tools (MCP)

An agent that can only chat is an agent with one hand tied behind its back. The Model Context Protocol (MCP) changes that, connecting Antigravity directly to your infrastructure.

I use MCP servers to bridge critical gaps:

• Docs-server: Ensures agents are building against live specs for Google ADK.
• Supabase-local: Allows for data-aware engineering where agents can inspect schemas and execute migrations directly.
• Linear: Lets agents create and update issues within our project backlog, ensuring a clear “paper trail”.

Feedback: Comments, Reviews & Mentions

When Antigravity starts “grepping” or searching blindly, that’s a signal — not a platform failure, but a context gap on my end. Antigravity supports nuanced collaboration to avoid this: comments on implementation plans, feedback during walkthroughs, and mentions (@) to direct the agent toward specific artifacts. Adding context in small doses keeps the implementation grounded and the output worth keeping. Missing this opportunity leads to more rework and wasted tokens, a cost that compounds quickly, and always seems to peak at the worst possible moment.

A Safety Net

When building new software, we need a safety net. This remains true in Antigravity. Beyond standard version control and automated tests, Antigravity allows you to undo steps taken with the agent, spanning many files and actions at once. This freedom to “bravely explore” is essential when working with an agentic team.

Conclusion: Engineering for Fun

There’s a pattern to everything described here: MD files before prompts. Skills before features. Workflows before execution. Engineered memory before automated distillation. In each case, the discipline comes first, and the velocity follows.

Vibing is a great way to start, to explore and validate. But ambition eventually outgrows it. When you invest in the context layer, when you design your agent’s capabilities rather than just hoping for them, something shifts. The AI stops feeling like a shortcut and starts feeling like a collaborator that actually knows your system.

Building the infrastructure that builds the art has its own satisfaction. Every skill I add, every workflow I refine, makes the next feature faster and the whole system more coherent. That compounding effect is what engineering has always promised. Antigravity just makes it strange and new again.

Antigravity and I are both evolving, occasionally glitchy, and absolutely worth it.