AI-assisted planning, architecture exploration, and design before coding

🚀 I built this project using Claude Code but not casually.

I applied real workflow engineering principles behind it.

After going deep into how top teams use Claude internally, I realized most AI frustration is not about capability.

It’s about workflow.

So while building my upcoming project, I followed these principles:

1️⃣ Plan Mode First

Before writing a single line of code, I:
• Broke tasks into clear steps
• Wrote specs
• Reduced ambiguity
• Designed verification before implementation

No rushing into coding.

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2️⃣ Subagent Strategy

For complex problems:
• Used multiple parallel explorations
• Offloaded research and structure analysis
• Kept main context clean and focused

Think of it like running a small AI engineering team instead of a single assistant.

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3️⃣ Verification Before Done

Nothing was marked complete unless:
• Logs were checked
• Edge cases reviewed
• Behavior diffed between versions
• Production state verified

No “it works locally” mindset.

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4️⃣ Autonomous Bug Fixing

Instead of micromanaging fixes:
• Pointed AI at logs
• Let it trace distributed flows
• Forced root cause analysis

Real debugging. Not patching.

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5️⃣ Skill Reuse & System Thinking

Turn repeated tasks into reusable skills.
Reduce context switching.
Design process once, reuse forever.

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6️⃣ Continuous Self-Improvement Loop

After every correction:
• Document the lesson
• Update rules
• Reduce future mistake rate

AI improves when your workflow improves

I’ve been using the Copilot CLI on a daily basis for coding, review, planning, design, and for debugging production systems. It’s awesome! Glad to see it reach GA. 👏

Most people use AI coding tools to write code. I'm using Claude Code to help build a personal operating system as a CEO.

I've hooked it up to many different tools that are important to us: Todoist, Slack, Linear, Notion, Salesforce, Gong, email, and so on. I've found it to be far more useful and sticky than the vanilla ChatGPT approach.

I now think of it as a mini coach+chief of staff. Some particularly valuable ways I'm using it right now or things I'm experimenting with:

🧠 Distillation of knowledge
I have a knowledge base that auto-syncs meeting transcripts from Granola, documents from Notion, indexes them by type (1:1s, customer calls, leadership), and makes them queryable across any conversation. It syncs to my Mac hourly. This means I can go see someone at their desk, Granola the conversation, and the context is now available for any future work I want to do with Claude.

🧘‍♂️ Removing distractions
A small but useful skill I've built is the slack-cleanup skill. It scans my 170+ channels, checks 6 months of activity, cross-references Salesforce and many other tools before deciding what to leave. Keeps me focused.

💭 Self-reflection and persistent memory
It knows our yearly initiatives, quarterly targets, and I write a weekly check-in before the week starts. So when I ask "what should I focus on today?", it doesn't just read my calendar. It checks whether my week is tracking against what actually matters. It's a gentle and useful accountability system.

🧐 Thinking quality
I'm experimenting with a /frame skill that takes messy context and distils it into a one-sentence problem, the binding constraint, and the eigenquestion — the question whose answer determines the answers to all the other questions. I use it to force forward progress on hard problems.

🔎 "Cheap", one-off analyses
For example, a competitive deep-dive into win rates, backed by specific customer quotes on gaps, with Gong snippets. That was a multi-day project compressed into minutes, backed by hard evidence, so I can sense check correctness.

Ultimately, I think the most benefit comes from work that would never have happened in the first place because it was far too expensive to do, vs. making me faster at what I already do. I recommend!

I use AI to write code every single day.

Copilot, Claude, ChatGPT - they're part of my workflow.
They make me faster. They handle boilerplate.
They suggest patterns I might not have considered.

But here's what they don't replace:
• 8+ years of understanding why systems fail
• The intuition to know when generated code looks correct but isn't
• The ability to design a system architecture from scratch
• The judgment to make tradeoffs between speed, cost, and quality
• The experience of debugging production issues across distributed systems
• The skill to look at requirements and know which ones will change

AI is the best tool I've ever had.
But a tool is only as good as the person using it.

A vibe coder with AI is someone with a power tool and no training.
A senior engineer with AI is a professional with a power tool.

Both will produce output.
Only one will produce something you can build a business on.

If you're hiring someone to build your MVP, don't ask if they use AI.
Ask what they were building before AI existed.

That answer tells you everything.

I wrote my first real code in 2015
10 years and hundreds of thousands of lines of code later, that flow state is all but gone. Coding agents are awesome and here to stay: I can ship at ~10x my earlier pace and handle more of the full stack while AI writes code faster and often better than me. My role has shifted to guiding the agents
providing context, correcting course, reviewing/verifying their work and focusing more on what problem to solve, what to build, how to iterate from feedback, and how to architect for scale (with AI doing much of the execution under my supervision).

For my full-time dev work I use Claude Code and Cursor daily, and I’ve learned they work best when I force a plan-first workflow. I built a “Plan Mode” for my OpenClaw agent where Claude (planner) reads the codebase, asks clarifying questions, and writes a concrete step-by-step plan (with file paths, schema changes, risks). Then a separate model/personality (“Dredd”, running GPT-5.3 Codex) reviews the plan adversarially to find gaps (edge cases like timezones, failure modes, incorrect assumptions). Only after I approve the revised plan does any code get executed—often as a background worker—so I avoid hours of back-and-forth and reduce wrong-build risk.

In my previous post I listed the top 5 things I use AI for apart from writing code.
Reena Garg asked which AI tools I use for these tasks 🙏 — sharing my list👇

⭐️ Thinking partner for ideas Tool: Cursor (Plan/Ask mode) I use Plan mode to discuss a feature/design: “what am I missing?”, “what can go wrong?”, “what’s the simplest version?”, “how should I break this into steps?”, “How can we implement this?”,

⭐️ Finding edge cases Tool: Claude (Opus-4.6) I paste the context (API, flow, PR description) and ask it to list edge cases: retries, timeouts, caching, permissions, weird inputs, etc. Opus works great for any code related tasks.

⭐️ Code review before the code review Tool: CodeRabbit, GitHub Copilot, Cursor Review These tools are really great to get a review before asking for a human review. I use them on all of my PRs.

⭐️ Turning manual processes into clean steps Tool: ChatGPT / Claude This is usually a back-and-forth conversation until it becomes a clean checklist/runbook and sometimes an automation script.

⭐️ Writing clearer communication Tool: Any AI works, but I mostly use ChatGPT PR descriptions, Slack updates, incident notes — it helps turn messy thoughts into clear writing.

If you’re using any AI tools in a different way, please share — I’d love to learn and try them too 🙌
Cheers, Princi 👩🏻💻