How I 10x'd My AI Coding Productivity (And You Can Too)

Want to get at least 10x better results when coding with AI agents? It's not that hard. I spent a long time building the process that got me there. Let me show you why your current workflow is probably broken — and how to fix it.

I build apps with AI agents every day. Claude, Cursor, the whole stack. And I need to tell you something: almost every demo you've seen about "building an app with one prompt" is bullshit.

Not because the technology doesn't work — it does. But because those demos skip over the part where the code actually has to run in production, scale with your business, and not turn into an unmaintainable nightmare three features later.

Here's what nobody tells you: without the right workflow, AI agents produce mediocre code riddled with mistakes that compound over time. And if you're building a startup, that technical debt will kill you.

I spent a lot of time iterating and developing the right agentic workflow that makes AI agents genuinely productive — like 10x productive. The kind of productivity where you're shipping production-ready features, not debugging spaghetti code at 2 AM because the agent "fixed" one bug and broke three others.

Let me break down what actually works.

The Problem with One-Prompt Coding

The vibe coding demos look incredible. Type a prompt, watch the AI build your app, ship it. Magic.

Except it's not magic. It's a carefully curated demo where:

• The scope is tiny
• The architecture doesn't matter
• Nobody's thinking about what happens when you add feature #5
• Security is an afterthought (or no thought at all)

In the real world, I've seen AI agents make catastrophic mistakes:

Architectural disasters: Dumping all the logic into a single API tier, creating spaghetti code that's impossible to read, debug, or extend. Once you're three features deep into this architecture, refactoring becomes a multi-day nightmare.

Security holes: Exposing sensitive data, missing authentication checks, writing database queries that are vulnerable to injection attacks. Recent research shows that 45% of AI-generated code contains security flaws. That's not a typo.

The death spiral: Here's the worst part. You find a bug. You ask the agent to fix it. It tries. It fails. It tries again. It "fixes" the bug but breaks something else. You spend hours in this loop on something that should take 15 minutes.

The problem isn't the AI. The problem is context.

Why AI Agents Struggle: The Context Problem

AI coding agents like Claude have a fundamental limitation: context window size. They can only "remember" so much while they work.

Yes, they can search your codebase efficiently. But search doesn't give them understanding. They might find the code, but they don't know why you built it that way. They don't know what decisions you made in phase 1 that affect how phase 3 should work.

Without that context, they're essentially coding blind. They make assumptions. They repeat mistakes. They build features that don't fit your architecture.

The solution isn't a bigger context window. It's better documentation.

The Workflow That Actually Works

I spent a lot of time iterating and developing the right agentic workflow, and I've settled on a process that consistently produces production-ready code. It's based on a simple cycle:

Plan → Code → Document

The key insight: you spend way more time on the planning phase, but that makes the coding phase faster and dramatically higher quality.

Here's exactly how it works.

Phase 1: Product Planning & PRD

I always start a new project by discussing goals and features with the agent. This isn't a 5-minute chat. This is a real planning session.

AI is exceptionally good at this kind of analysis. I use subagents to:

• Research the market
• Analyze competitors
• Research UX patterns
• Break down feature requirements

The output is a PRD (Product Requirements Document) that I save directly in the repo.

This document becomes the north star for the entire project. When the agent starts coding in phase 4, it knows what we're building and why.

Phase 2: Implementation Planning

Next, I work with the agent on a detailed implementation plan. We break the project down into phases and design the architecture.

This stage takes time. Sometimes a few hours of back-and-forth discussion. But this is where you get it right.

You're making architectural decisions. Choosing patterns. Thinking about security, scalability, and maintainability before writing a single line of code.

The plan goes into a plans/ folder in the repo. Detailed. Specific. Opinionated.

Phase 3: Breaking Down Into TODOs

Once we have the implementation plan, we break it down into a series of small, concrete todos. Each todo is small enough to complete in one focused coding session.

These todos are organized into phases. Phase 1 might be "Set up auth and user management." Phase 2 might be "Build the core data models." And so on.

We save all of this to a TODOS.md file in the repo. Here's the crucial part: the agent updates this file after each coding task. It marks todos as complete, adds notes about what was done, and keeps the file current.

This creates a living record of progress that both you and the agent can reference at any time.

Small phases. Clear boundaries. Always up to date.

Phase 4: Coding (Finally)

Now we code. Phase by phase. Todo by todo.

But here's the crucial part: at the end of each phase, we document what was built.

Not just what was built — why decisions were made.

This documentation goes into a docs/ folder. It's detailed. It explains:

• What features were implemented in this phase
• Why we chose this architecture pattern
• What security considerations we addressed
• What edge cases we handled
• What the next phase should know

Phase 5: Context Reset

Here's where the magic happens.

Before starting the next phase, I clear the agent's context. This frees up space in the context window for what actually matters.

The next agent (or the same agent with fresh context) loads:

• The PRD
• The implementation plan
• The documentation from previous phases

Now it has understanding, not just code. It knows what was built and why. It can make decisions that align with your architecture instead of fighting against it.

The Results: 10x Productivity

Since I dialed in this workflow, my productivity has increased at least 10x. Not because I'm coding faster — because I'm debugging less.

Way less.

The bugs that do appear are straightforward to fix. There's no death spiral where the agent breaks one thing while fixing another. No hours wasted refactoring spaghetti code from phase 1 because we didn't think about architecture.

The code that comes out of this process is production-ready. It scales. It's maintainable. I can actually build a company on it.

Why This Workflow Works

Let's be clear about what makes this different:

1. Front-loaded planning prevents architectural mistakes You're not letting the agent make structural decisions on the fly. You've thought through the architecture. The agent is executing a plan, not inventing one.

2. Documentation solves the context problem Instead of relying on the agent to search and infer, you're giving it explicit knowledge about decisions and rationale. This is context that actually matters.

3. Small phases prevent compounding errors When mistakes happen (they will), they're contained to a small phase. You catch them early, before they're baked into five other features.

4. Context resets keep the agent focused By clearing context between phases, you're not letting the agent get distracted by implementation details from three phases ago. It has just enough context to do the current work well.

Getting Started

If you want to try this workflow, here's where to start:

1. Start with a plan, not a prompt Before you ask the agent to code anything, have a conversation about what you're building. Take an hour. Write it down.

2. Create a docs folder Start documenting your decisions. Even if you're mid-project, you can start now. Document what you've built so far and why.

3. Break work into phases Stop trying to build everything at once. Break your project into discrete phases with clear boundaries.

4. Load context deliberately Before starting a new phase, think about what context the agent actually needs. Load the relevant docs. Don't just dump the entire codebase into the context window.

Want to Make This Even Easier?

To make life easier, I actually built an open-source framework that codifies this entire workflow. It's called AgentsPack.

It injects the right prompts, commands, skills, and rules for all major agentic platforms — Claude Code, Cursor, Codex, and more. You run a simple CLI command and it sets up everything in any local or remote Git repo.

Side bonus: you can add your own rules and customizations once, and AgentsPack translates them to the right format for all coding agents automatically. No more maintaining separate configs for each tool.

Get it here: github.com/farfarawaylabs/agentspack

The Real Difference

The difference between vibe coding and production-ready AI development isn't the tools. It's the process.

AI agents are incredibly powerful, but they're not magic. They need structure. They need context. They need a workflow that plays to their strengths and mitigates their weaknesses.

Spend time on planning. Document your decisions. Build in small phases. Give the agent the context it needs to understand, not just search.

Do this, and you'll stop debugging at 2 AM. You'll start shipping features that actually work. And you'll build a codebase that can scale with your business.

That's the difference between a demo and a product.