My AI Coding Agents Aren't Magic—They're Levers. Here's How I Actually Use Them.

Dosto, let's have a real chat.

Every other day on LinkedIn or X, I see a post that goes something like this: "I built a full-stack app in 30 minutes using AI!" or "AI is my 10x developer." On the surface, it’s inspiring. But it also creates this myth that you can just whisper an idea into the void and a perfectly functioning SaaS product will pop out.

As someone who has invested significant time and money into AI coding agent platforms like Windsurf, Antigravity, and my own custom setup, OpenClaw, I can tell you the reality is... different. It’s less magic wand, more power tool. It can help you build a house faster, but it can also help you cut your own hand off if you’re not careful.

For context, I'm a Computer Science engineer by degree, but I’ve spent the last 8.5 years in Sales & Marketing. Now, I’m making a hard pivot back to my roots, going all-in on my ultimate goal: becoming a successful indie developer. These AI agents are my primary investment to bridge that gap and accelerate my journey. They are not toys; they are business expenses I need a return on.

So today, I want to pull back the curtain and show you my actual, unglamorous, day-to-day workflow. The wins, the frustrations, and the time I had to perform surgery on my AI's brain.

The Core Problem: The Genius with Amnesia

My primary agent, a custom-built instance on my OpenClaw framework I call "Clawbis," is brilliant. It can digest complex prompts, understand system architecture, and generate clean Python code. But until last week, it had a fatal flaw: the memory of a goldfish.

It was infuriating. I’d spend 20 minutes meticulously explaining my project's file structure, my preferred linting rules, and the existing data models. Clawbis would understand perfectly and generate a beautiful, context-aware function. Then, five minutes later, I’d ask it to write a unit test for that *exact same function*, and it would respond as if it had never seen the code before in its life.

*Yaar, itna frustrating hota hai!* It was like hiring a world-class programmer who gets a factory reset every 15 minutes. The constant re-explaining was killing my productivity, the very thing the agent was supposed to enhance.

This isn’t a unique problem. Every developer using these tools faces this "context window" limitation. I saw a headline just the other day that Anthropic is making a big push for enterprise agents with plug-in tools, which is their way of trying to solve this exact memory issue. But as an indie hacker, I can't wait for big tech to solve my problems. I had to fix it myself.

My Three-Agent Stack: A Specialist, a Co-pilot, and an Architect

I don't rely on a single AI. That’s a rookie mistake. My workflow is built on a team of specialized agents, each with a distinct role.

1. OpenClaw (The In-House Specialist): This is my custom rig. It’s where I have the most control. After the amnesia episode, I decided to build Clawbis a "prosthetic memory." I didn't want to over-engineer it with a vector database or Redis cache. As an indie dev, my most valuable resource is time. So, I opted for the simplest possible thing that could work: a plain text file.

I wrote a simple Python script that creates a memory.md file. Before Clawbis processes any new prompt, another script automatically injects the last 50 lines of our conversation and a summary of the project goals as a preamble. It's a hack, for sure. But it works. Now, Clawbis remembers the key details of our session. This is the agent I use for core logic and complex, multi-step tasks.

2. Windsurf (The IDE Co-pilot): Windsurf is integrated directly into my IDE. It’s my pair programmer that never needs a coffee break. I don't use it for big, creative tasks. I use it for the grunt work. Things like:

"Convert this JSON blob into a Python dataclass."
"Write a pytest unit test for this function."
"Refactor this for loop into a list comprehension."
"Add docstrings and type hints to this file."

It's incredibly fast for these small, self-contained tasks because it has the context of the open file. It’s the master of the tactical, line-by-line execution.

3. Antigravity (The High-Level Architect): When I'm stuck on a bigger, more strategic problem, I turn to Antigravity. It’s less about writing code and more about making the right architectural decisions. I use it for prompts like:

"Compare the pros and cons of using FastAPI versus Flask for a SaaS backend that needs to handle websockets."
"Give me a boilerplate for a CI/CD pipeline using GitHub Actions to deploy a Dockerized Python app to a cloud provider."
"What are the best practices for securing an API that will be consumed by a public-facing web app?"

It helps me avoid dead-ends and think through the consequences of my early technical choices.

A Real-World Workflow: Building the Prosthetic Memory

The best way to explain this is to show you how I used the agents to build the memory solution itself.

Step 1: The Strategy (Antigravity) I started by asking Antigravity: "I need to build a simple, persistent memory for my custom LLM agent. I'm a solo developer, so speed of implementation is key. Brainstorm three approaches, listing the pros and cons of each for my use case." It suggested a vector database, a Redis cache, and a simple file-based log. Seeing the complexity vs. benefit laid out so clearly, I immediately chose the file-based log. It was the 80/20 solution.

Step 2: The Core Logic (OpenClaw/Clawbis) Next, I moved to my custom agent, Clawbis. My prompt was very specific, drawing on my sales background where articulating requirements clearly is everything. "You are an expert [Python](https://python.org) developer. Create a [Python](https://python.org) class named 'MemoryManager'. It should manage a file called 'memory.md'. It needs two methods: 'remember(text: str)' which appends a timestamped message to the file, and 'recall(lines: int = 20)' which returns the last N lines from the file. Ensure you handle file-not-found errors gracefully."

Step 3: The Refinement (Windsurf) The code Clawbis generated was about 80% correct. It was functional but had a minor, inefficient way of reading the last lines of the file. Instead of going back and forth with Clawbis, I just pasted the code into my IDE. I highlighted the function and told Windsurf: "Refactor this to be more Pythonic and efficient for reading the end of a file." Ten seconds later, it was replaced with a cleaner, better implementation.

This is the key, dosto. It's a fluid dance between different tools, using each for its strengths. The architect for the plan, the specialist for the heavy lifting, and the co-pilot for the finishing touches.

It's Not All Sunshine and Roses

Let's be clear: this process is not frictionless.

The "Confident Idiot" Problem: Agents will sometimes generate code that looks perfectly plausible but is completely, utterly wrong. They'll hallucinate library functions that don't exist or write security-flawed authentication logic with a straight face. You *must* be the senior developer in the room. You have to review every single line.
Context is Still King: My prosthetic memory helps, but it doesn't solve the fundamental context window limitation. For truly large, complex codebases, the agents start to lose the plot.
The Cost: These tools are not cheap. The combined cost of my subscriptions is a serious monthly expense. As I saw in my news briefing, some models can cost up to $200 a month for heavy use. This financial pressure forces me to be disciplined. I'm not just "playing with AI"; I'm using a professional tool to build a business.

My Takeaway: Agents as Levers, Not Crutches

If you're thinking of diving into AI-assisted development, here's my advice: think of these agents as levers, not crutches. They don't replace the need for fundamental knowledge. They amplify it.

My 8.5 years in sales taught me how to break down a complex problem, structure a solution, and communicate it clearly. It turns out, that's the exact skillset required for effective prompting. Garbage in, garbage out. A well-structured thought will always yield a better result than a lazy, one-line question.

My goal is to use these levers to build and launch my SaaS ideas faster than I ever could alone. To test, to iterate, and to get to a point of monetization where this indie dev dream becomes a sustainable reality.

This is my setup. It's a messy, evolving, and powerful workflow. What about you? What does your AI-assisted development process look like? What are your biggest wins and most hair-pulling frustrations?

Drop a comment below. Let's figure this out together.