No case studies from 2019. No “5 tips for entrepreneurs.” Just real lessons from the transition: executive → builder → investor. The wins, the failures, and the systems that make it work.
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Everyone selling AI right now has redefined agentic workflow to mean whatever they already sell. Here's the version that held up after six months running 48 agents through a real business pipeline, and the three unglamorous failures that actually broke it.
Anthropic just turned agentic workflows into a toggle install for every small business. That kills "we added AI" as a wedge. Here is what still works.
Everyone ranks the best AI agents for business by feature checklists. I run 50+ of them in production. Here is what actually works, sorted by function, not hype.
I run 50+ AI agents in production at LeanAI Studio. Here is the honest breakdown of which AI agent framework to use and when.
Most micro-SaaS idea lists have no evidence behind them. Here is the sourcing and validation process I use to find ideas where someone is already paying for something close.
I run 52 specialized agents in production. Here is what multi agent ai actually delivers versus what the research and vendor demos promise.
Every article about ai agent orchestration is written by someone who has never shipped one. Here is what 52 production agents taught me about making them work together.
We built landing pages, ran Google Ads, and counted signups for three months. Zero paid conversions. Here is what we changed and why the fix was embarrassingly simple.
We ran 70 bets through our old validation pipeline. Most died after we had already built the landing page. So we rebuilt the process to kill things earlier. Here is what day one looked like.
The World Economic Forum says agentic AI frees founders to focus entirely on vision and strategy. They are mostly right. But six months running 37 AI agents taught me the part they got wrong.
Andrej Karpathy named a new discipline at Sequoia Ascent 2026: agentic engineering. This is a field report from six months inside the role he described, running 37 agents across a full business pipeline.
At ServiceNow Knowledge 2026, Jensen Huang described a $50 trillion economy run by AI agents. I have been building the micro-scale version of that model at LeanAI Studio for six months, with 37 agents and zero outside funding.
Microsoft's 2026 Work Trend Index officially named a new job category: the Agent Supervisor. I have been doing this job for six months at LeanAI Studio, managing a fleet of 37 AI agents across every business function.
We paused a bet that passed our keyword gate and launched a blue-ocean one in its place. The difference was a single data check that Google Trends cannot tell you.
One sentence from a sales leader in 2014 rewired how I think about winning. Twelve years later, it's the foundation of LeanAI Studio.
We tested 16 micro-SaaS bets with Google Ads. Then we built a simple demand filter that would have saved us from the worst performers before we spent a dollar.
I crossed the finish line of the Milano Marathon yesterday. I almost quit during training more times than I want to admit. The process felt identical to building LeanAI Studio.
I gave a CEO agent and 19 specialists full autonomy for a week. They shipped 10 landing pages, launched 9 ad campaigns, and scored 6 new bets. Revenue: zero. Here is what I learned.
I started LeanAI Studio as a solo founder with AI agents doing the work. Six weeks in, I discovered the real problem: I was the bottleneck. Every decision that required me was a task that didn’t ship.
It’s been one month since I left MongoDB. People keep asking how it’s going. The honest answer is: it depends which week you’re asking about.
Everyone thought I was having a midlife crisis. The reality was more strategic: financial independence gave me optionality, and optionality demanded that I actually use it.
I don’t have employees. I have agents. Here’s how a 5-agent AI team handles research, validation, coding, operations, and content—and why this model changes everything about how you can build.
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