You’ve got a workflow in your head. It’s something that should take an afternoon to build, so you open up your automation tool of choice, ready to crush it. Then the canvas loads.

And you just stare at it 😳

Nodes everywhere. Trigger blocks, action blocks, filter blocks. Dropdowns inside dropdowns.

Authentication flows that want five different API keys you definitely don’t have on hand.

And before you’ve built a single useful thing, you’ve spent two hours just learning the tool itself.

That’s what got me. Not the automation but the tool tax.

It’s the feeling that to build anything meaningful in Zapier, n8n, or Make.com, you first have to become a certified expert in them.

The configurability that makes these platforms powerful is exactly what makes them exhausting to start with. You want to build a workflow, but instead, you’re studying a platform.

Antigravity flips that. We covered the foundations in the last post—what it is, how to set it up, and why it’s worth your time.

This post goes deeper by looking at what happens when you use it to orchestrate entire workflows.

I’m talking: parallel agents, autonomous browser testing, and CLI dev teams handling the heavy lifting while you steer.

I’ve been testing agentic workflows a lot lately (especially for my new project, VersoID), so here’s an honest look at:

• What that actually feels like in practice
• Where it’s faster than anything a drag-and-drop canvas can offer
• Where it’s still lacking

Let’s take a look 👇

The Old Way vs. The Agentic Way

Traditional tools like Zapier or Make work like assembly lines you have to build yourself.

You define every step manually: if this happens, grab that data, format it like this, and send it there. Every node is a decision you have to make, and every connection is a wire you have to run.

The more complex the workflow, the more time you spend inside the tool instead of on the actual problem.

Related: This Is How To Build A Visual LLM Agent Workflow

Agentic workflows work differently. Instead of configuring every step, you describe an outcome.

The agent reasons through the steps, plans its approach, and executes, sometimes in parallel, and sometimes, looping back to fix its own mistakes (because let’s be real, they always happen 🙈).

Instead of building an assembly line, you’re briefing a team.

What That Looks Like Side by Side

In n8n, building a lead research workflow means manually adding a scraper node, wiring it to a formatter, connecting that to a filter, then an enrichment step, and finally an export.

In Antigravity, you type:

Research the top 20 SaaS companies in the HR space. 
Pull their homepage URL, pricing page, and founder LinkedIn. 
Export to a CSV organized by company size.

The agent plans the steps, executes them, and hands you the output.

You didn’t wire a single node. That’s the structural reason the speed difference exists since you’re working at the outcome level, not the configuration level.

Note 👀
This doesn’t mean Antigravity is reliable without limitations (we’ll get to them shortly). But the sheer volume of work you can finish in one session is simply higher.

The Agent Manager: Your Mission Control

The Agent Manager is where multi-agent work happens in Antigravity (Cursor has its own agent management interface that’s not too different).

Think of it less like a canvas and more like a project dashboard where the “workers” are AI agents reporting back in real time.

In a tool like n8n, you’re the one deciding that step 3 has to happen before step 4.

In the Agent Manager, you spawn multiple agents and let them work simultaneously on independent tasks.

No waiting for one node to pass output to the next.

Parallel Agents in Practice

Say you’re building a lead generation workflow. Instead of a linear chain—scrape, then clean, then enrich, then export—you can have:

• Agent 1 scraping competitor pricing
• Agent 2 simultaneously researching contact details for a target niche

Neither needs the other to finish first. You watch both progress through the Inbox Manager, a real-time view of every active agent’s conversation and proposed actions.

For anyone running client workflows, this changes the math on turnaround time. Two parallel agents covering independent tracks can replace what would otherwise be multiple separate workflow builds sequenced across a day.

You can also manually approve or deny specific agent actions before they execute, so you’re supervising, not just watching.

Tip 💡
Keep parallel agents on strictly independent tasks. If Agent B needs Agent A’s output to proceed, running them simultaneously will cause errors. Don’t go asking for headaches! Plan your splits around things that don’t overlap.

How You Brief Your Agents

Before you start spawning agents like a mad scientist, you need structure. Otherwise, you’re just shouting into the void and hoping the AI guesses right.

The Three-Layer Setup

Antigravity uses a three-layer architecture, usually set up through a markdown file like agents.md or claude.md. This is your “source of truth.”

Inside, you define three things:

1. What the agent should do (your high-level goals, required inputs, tools to use)
2. How it should make decisions mid-task (the logic rules it follows when interpreting your directions)
3. What the actual output looks like (scripts, code files, or exports, plus where sensitive things like API keys should live)

Once the file is ready, you just tag it:

@agents.md instantiate the environment

The agent reads it, proposes a plan, and starts building. You review, accept, and your project scaffolding is done.

Every agent you spawn after this point works within those same rules.

When Agents Don’t Play by the Rules

Here’s something I ran into: I set up two instruction files for a build, with one specifically detailing design rules for the agent to follow.

The agent proceeded as if neither file existed. When I asked why, the reply was something like “You’re right, my oversight” 😒

AI agents don’t perfectly retain context, especially across longer sessions or when the conversation shifts focus.

The fix is to re-tag your file explicitly mid-prompt—@agents.md—rather than assuming the agent remembers it from the start.

Learn from my mistake and make that a habit before it bites you!

Note: Instruction file consistency is still one of Antigravity’s rough edges. Tag your .md files explicitly in prompts every time, rather than relying on the agent to recall them from session start.

The Browser Subagent: Your Eyes and Hands

When an agent needs to use the web, Antigravity doesn’t just open a tab in Chrome. It spins up a Browser Subagent—a specialized instance of Chrome that can click buttons, scroll, read console logs, and even record video of what it’s doing.

This is the feature that actually made me go “Wait, what?” 😲

The setup is invisible. When you first run a web project, Antigravity automatically installs a Chrome extension inside that managed browser window, so you don’t install anything manually, and it doesn’t touch your personal Chrome profile at all.

A separate Chrome window opens, Antigravity controls it, and the whole thing just works. It’s not simply cool but some serious technical progress!

Why? Because traditional automation tools can trigger API calls, but they can’t see whether the thing worked visually. The Browser Subagent can.

Is it perfect? No. However, it can provide that extra context that can shave off a series of follow-up prompts for that frustrating bug that no AI model seems able to catch.

The Build-Test-Verify Pipeline

Here’s a workflow I love:

You prompt a build agent to create a new feature in your web app.

Once it’s done, instead of manually opening a browser to check if it rendered correctly, the Browser Subagent launches automatically, loads the app, clicks through the relevant user journey, and returns a screenshot or video confirming it worked.

No manual QA pass between steps.

You set it up with three agents working in sequence:

1. Agent A writes the code
2. Agent B runs the local dev server
3. The Browser Subagent opens its managed Chrome window, interacts with the running app, and verifies the output visually

If a CSS change broke your layout somewhere unexpected, the agent catches it in the screenshot comparison and flags it.

Tip ✅
The browser subagent runs in its own isolated Chrome profile that’s completely separate from your personal browsing. Anything it opens, clicks, or records stays contained in that managed window. You don’t need to worry about it touching your regular Chrome data. Security matters, people!

Agent Swarms: Offloading Heavy Work to a CLI Dev Team

Think about what it actually takes to build a complex automation workflow manually.

It’s not just about writing one script. You’re handling authentication, data transformation, API wiring, output formatting, and error handling.

Each piece is its own job. Typically, you either do it all yourself, sequentially, or you split it across a team.

Agent swarms give you the team.

A swarm is a group of parallel agents. These are called CLI agents that work directly in your terminal, running commands and modifying files, with no visual UI needed.

The agents in the swarm are all coordinated by a primary orchestrator model.

You brief the orchestrator (usually Gemini) with a big goal, and it breaks the work into distinct tracks and hands each one to a sub-agent.

Those agents, in turn, work simultaneously on different parts of the codebase and, in some configurations, auto-integrate their outputs back into the main project files when they’re done.

You’re reviewing the merged result, not building every piece yourself.

The Rule That Makes or Breaks a Swarm

The golden rule here is task independence. A swarm falls apart the moment one agent needs another agent’s unfinished output to proceed.

At that point, you don’t have parallel work; you have a queue dressed up as a swarm. To avoid this, map out which parts of your build are self-contained and split along those lines.

For my app, VersoID, I used a swarm to handle Supabase, Stripe, and Firebase integrations all at the same time.

Note that it’s not a single prompt that handles three different integrations. Instead, it’s a team of three independent agents that are briefed about each integration individually in detail.

The Honest Cost: Quota and Frustration

Running multiple agents simultaneously burns through rate limits fast.

When the quota runs out mid-run, everything pauses, and you wait for the refresh before anything continues. It’s not catastrophic, but it will disrupt your flow (and probably piss you off), especially mid-build on something time-sensitive.

I highly suggest checking your usage before spinning up a full swarm, and be strategic in what and how you prompt.

Planning before execution here is your best friend that’ll save you money and time.

Note 🙈
Be intentional. Don’t just launch a swarm because it sounds cool. Plan your execution first. It’ll save you time, money, and a lot of “why is this broken?” debugging later.

Explore: A Smart Free Chrome Extension That Upgrades AI Prompts

Skills: Instructions That Actually Stick

Skills are local markdown files that teach your agent how to perform specific, repeatable tasks such as following brand design rules, running a research workflow, or drafting content in a particular style.

If you’ve set up an agents.md file, you might be wondering what skills actually add on top of that. It’s a fair question that I certainly had, and the difference is worth understanding.

First, note that agents.md is project-level context. It’s always on, always ambient, governing everything happening in that workspace. The agent loads it at the start of every session.

Being always “on”, however, doesn’t mean reliable. The longer and more complex the file gets, the easier it is for the agent to quietly ignore parts of it. You’re not invoking it; it’s just sitting there hoping to be referenced.

Skills work differently. You call them on demand, only when you need them, by tagging the file directly in the prompt. Instead of loading into every session, they activate when invoked.

And, because each skill is a self-contained instruction set for one specific task, they’re tighter and easier for the agent to follow than a lengthy general rules document.

There’s also a portability angle that matters if you’re running multiple client projects. Your agents.md lives in one project folder and only governs that workspace.

A skill file can be dropped into any project and called the same thing.

Think of it this way:

• agents.md is your project brief
• Skills are your SOPs (Standard Operating Procedures) for specific repeatable tasks that get pulled in exactly when you need them

Tip 👇
Skills are stored locally in your project folder and the agent references them without burning tokens re-explaining the same instructions every session.

How to Build a Skill

You can use a “skill creator” meta-skill to have the agent build new skills for itself. It looks like this:

@skill_creator I want a skill that researches a topic 
and drafts a LinkedIn post in the style of [X]

The agent produces a new .md file—say, linkedin_drafter.md—which you call in future sessions with:

@linkedin_drafter Google Antigravity

The more skills you build, the less you re-explain across projects.

For anyone managing multiple client workflows, this is how you get consistent agent behavior without copy-pasting instructions into every new agents.md from scratch.

It’s a Wrap

The shift here isn’t just about cool AI features; it’s about where you spend your energy.

Traditional tools demand your time upfront, configuring every node and connection before a single thing runs.

Agentic workflows front-load the thinking instead. What you do is describe the outcome clearly, structure your agent instructions well, and the execution mostly takes care of itself.

Is it actually 10x faster? That depends on what you’re building.

For simple workflows, the gap might be modest.

For complex multi-step builds that involve research, execution, and verification running in parallel, across multiple agents, the difference is real enough that you’ll feel it.

I watched Antigravity scaffold my entire VersoID project from a single prompt, and that was the moment it clicked for me. Something that would have taken me a solid chunk of time to plan and start manually happened in minutes.

The process isn’t perfect—there are rough edges and quota pauses—but it’s a massive leap forward from dragging boxes around a screen.

Here’s my challenge to you:

1. Pick one workflow you’ve been avoiding because the setup felt like too much work.
2. Write a paragraph describing what it should do.
3. Hand it to an agent and see what comes back before you touch a single configuration.

That’s all from me. Bye.