I'm going to tell you something that sounds like an exaggeration. It's not.

I'm one person. No employees. No co-founder. No office. I'm building an AI-leveraged business from a small town in Pennsylvania, and I'm running the entire thing through an AI agent on my laptop.

Not using it as an assistant. Not asking it to write emails. I mean the whole business (strategy, research, project management, long-term memory, knowledge retrieval, web scraping, code deployment). All of it flowing through a single system.

That system is Google's Antigravity.

Most of this foundation was influenced by Jack Roberts' YouTube channel. Definitely recommended.

On the surface, Google Antigravity looks like another AI coding tool. People compare it to Cursor, Lovable, Windsurf. And on paper, that's fair. You open it, you write code, you ship things. Most people use it that way and move on.

I'm doing something different.

I'm using Antigravity as the operating system for my entire business. Not metaphorically. Literally. Every major function of my business is being routed through it.

When I ask Antigravity a question, it doesn't just generate an answer from a language model. It knows where to look. It knows which tool to reach for, which file to check, which database to query. It has memory. It has context. It has specialized skills it can activate. And the way it connects to all of these external tools is through something called MCP (the Model Context Protocol).

Let me show you exactly how I set this up.

MCP is the connective tissue of the entire system. Think of it like a universal plug standard. Any tool that speaks MCP can talk to any AI agent that speaks MCP. It's an open protocol, and it's what makes the "operating system" concept possible.

In Antigravity, I have a single configuration file (mcp_config.json) that declares every external tool my agent can reach. Each tool runs as a named MCP server with its own command, environment variables, and curated set of enabled “tools” specific to that server. I also have individual control over these specific “tools” to enable or disable each. This provides control over resource constraints, which I go into more detail later in this post.

Here are the 6 MCP servers I'm running currently, and what each one does:

What it does: Manages all code directly from within the agent: repositories, branches, pull requests, commits, issues, and file pushes.

How it's configured: Runs via Docker with a fine-grained personal access token (PAT). I created the token with granular read/write permissions and connected my Google account to GitHub for SSO login.

What I disabled: 21 tools I don't need in my daily workflow. Things like fork_repository, assign_copilot_to_issue, search_users, list_releases, and other administrative tools I can handle through the GitHub UI when needed. This was critical. More on why later.

How I use it: When I build something, I push it to GitHub without leaving the agent. I create repos, manage branches, open PRs, and commit files. All of this is done as tool calls inside a conversation.

What it does: Gives my agent access to up-to-date documentation and code examples for any programming library or framework. This sounds boring. And it is. But the issue with most AI tools is that they still are guessing when they touch newer libraries. You are actually dependent on the LLM's training data, which may be outdated at the pace new tools are being developed. Context7 fixes this by feeding agents live, version-specific docs to stop the hallucinations and enable basically plug-and-play deployments.

How it's configured: Runs via npx with an API key. Very lightweight.

How I use it: When I'm building something with a library I don't use every day, I don't Google the docs. I tell the agent to look it up through Context7, and it pulls the latest documentation inline. This keeps me in flow instead of context-switching to a browser.

What it does: Creates AI-powered research notebooks, ingests sources (articles, PDFs, YouTube videos, web pages), runs automated web research, and lets me query synthesized knowledge bases conversationally.

How it's configured: This was the hardest integration. There's no official NotebookLM API, so I'm using a community-built MCP server (notebooklm-mcp). I built a dedicated Python virtual environment at /Users/.../Antigravity/.shared/notebooklm-venv/ and installed the package there. A centralized location so every project can share it. The mcp_config.json points directly to the binary inside that venv.

Authentication was a battle. The early approach used browser cookies that expired silently every few days, killing the integration without warning. I eventually moved to a persistent token-based auth configuration baked into the venv environment. Robust and stable. I disabled the save_auth_tokens and refresh_auth tools in my config after locking this in. I don't want the agent touching auth. That's mine to control.

What I disabled: 16 tools (sharing features, studio creation, artifact exports, and other tools that aren't part of my core research loop.

How I use it: This is my research department. I've written a separate post that goes deep on this integration. The short version: I tell the agent to create a notebook, run research, and query the results. No browser. No manual steps. I have eight notebooks covering everything from PA business law to AI orchestration patterns, each with up to 50 curated sources. All built programmatically with a conversation.

What it does: Gives the agent full read/write access to my Obsidian knowledge base (my notes, blog drafts, project plans, research explorations, etc.).

How it's configured: Runs via npx using the @modelcontextprotocol/server-filesystem package, pointed at my vault directory. This gives the agent the ability to create files, edit files, read directories, search across notes, and move things around.

What I disabled: Just 2 tools (list_allowed_directories and get_file_info) utility tools I never need.

How I use it: Obsidian is my active project layer. When I'm working on a task, the agent reads my notes for context. When work is done, it updates them. Planning lives here. Meeting prep/notes live here. Blog posts get drafted here. The agent doesn't just reference my notes. It writes them.

What it does: Web scraping, search, and content extraction at production quality. It can scrape single pages, search the web, extract structured data with LLM-powered parsing, and even run browser automation sessions.

How it's configured: Runs via npx with an API key.

What I disabled: 5 tools (the crawl, map, and agent features), keeping only the focused tools I actually use: scrape, search, extract, and browser sessions.

How I use it: When I need to pull information from the web (competitor pricing, subreddit discussions, article content, structured data from a page). It's faster and more reliable than asking the agent to browse manually. I've used it to scrape Reddit for trending AI discussions, extract structured product data, and pull content from documentation sites. You can even pull branding info like color palettes and fonts, which is incredible.

What it does: Vector database for long-term memory. Stores documents as embeddings and retrieves them through semantic search. This just means I can ask questions and get relevant results even when the wording doesn't exactly match.

How it's configured: Runs via npx with an API key. No tools disabled.

How I use it: Pinecone is my long-term memory. Anything that's finished but might be needed later gets stored here (contracts, completed research, meeting transcripts, archived proposals). When I ask the agent about something historical, it searches Pinecone. For example, "What did that contract say?" or "What research did we do on AI liability?".

Beyond MCP servers, I've built custom skills. These are specialized instruction sets that extend what the agent can do. Each skill is a folder with a SKILL.md file that the agent reads before executing a task. Think of them as playbooks.

Here are the five skills currently in my system:

The skill system is self-extending. When I encounter a repeating pattern in my work, I create a new skill for it. The agent learns the pattern once, and then it's available forever.

At the core of the entire system is a single file: Business_DNA.md. This is the standard document that defines what the business is, and more importantly, what it isn't.

It covers:

Every strategic decision the agent helps me with runs through this file first. When I ask for help writing a pitch, it checks the DNA. When I ask for advice on a new service line, it checks the DNA. This is what makes Antigravity feel less like a chatbot and more like a business partner who actually knows the business.

Here's something most people don't think about: there's a limit on how many tools an agent session can register. Each MCP server exposes a set of callable tools, and when you're running 6 servers, the total adds up fast.

When I first wired everything together, I blew past the limit. Antigravity shows a warning message in the Manage MCP settings: Warning: To optimize Agent, we recommend that up to 50 tools are enabled.. It doesn't outright block MCP access, but I imagine there are resource constraints that I'd rather optimize for.

The fix was surgical. I audited every tool across every server and disabled anything I don't use in my daily workflow. GitHub went from 35 tools to 14. NotebookLM went from 26 to 10. Firecrawl went from 10 to 5.

I now treat tool count like a budget. Every time I consider enabling a new tool, I ask: "Will I use this more than once a week?" If not, it stays disabled. If I need it for a one-off task, I can always enable it temporarily. Even entire MCP integrations can be disabled if I know I won't be using it that day.

This constraint turned out to be a feature, not a bug. A focused tool set means the agent makes better routing decisions. Less noise, sharper context.

Let me make this concrete. Here's what an example workday looks like running through Antigravity:

Morning: Research & Prep
I ask the agent to check if any of my NotebookLM notebooks have new information relevant to upcoming client conversations. Maybe I query my "Trust Gap Strategy" notebook: "What are the key trust-building tactics for first-time AI consulting conversations?" I get a synthesized, sourced answer in seconds to help me prepare.

Mid-Morning: Active Work
I'm building a deliverable for a client. The agent reads my Obsidian project notes for context, checks Context7 for the latest library docs, and writes the code. When it's done, it pushes to GitHub.

Afternoon: New Knowledge Topic
A prospect asks about something I haven't covered yet... maybe AI compliance for healthcare data. I tell the agent to create a new NotebookLM notebook and run deep research to find 40-50 sources that are also relevant to my state. Ten minutes later, I have 50 sources organized and queryable. I'm ready for a call I couldn't have taken an hour ago.

End of Day: Archive & Update
Completed work gets recorded. The agent updates Obsidian notes, archives finished deliverables to Pinecone, and logs any metrics that moved.

Late Night: System Improvement
I notice I'm repeating a pattern in my work. I create a new skill for it. Tomorrow, the agent will handle it automatically.

I need you to understand what this actually represents.

I'm one person in a small town in Pennsylvania. And I have:

All of this runs off of one laptop. All of it is orchestrated by one AI agent. All of it costs almost nothing beyond the time I invested building it.

A consulting firm with five employees couldn't replicate this setup without months of work and tens of thousands of dollars in tooling. I built it in a couple weeks, iteratively, one integration at a time.

That's not productivity. That's a structural advantage.

I'm not going to tell you this is completely polished. It's not.

But here's the thing: it doesn't have to be perfect. It just has to be functional. And it is. As with any new technology, there's an inherent risk with being early. But the current is accelerating every hour. By the time the mainstream decides it is safe to enter the water, I will have already mapped the deep-sea canyons and anchored the infrastructure required to own the depths.

Here's what I'd tell someone starting from zero:

People keep asking whether AI is going to replace jobs. That's the wrong question.

The right question is: What happens when one person can do the work of ten?

I'm not theorizing about that. I'm living it. I built an operating system from tools that are mostly free, wired together by an open protocol, and orchestrated by an AI agent that gets smarter every time I use it and more capable every month (or less depending on when you are reading this).

This is what the future of work looks like for solopreneurs. Not bigger teams. Not more funding. Better systems.

And I'm just getting started.

I'm documenting everything as I build it. If you want to follow the architecture, the integrations, and the hard-won lessons, stick around. There's a lot more to show.