Six hours. That is how much time I was losing every week to management meetings before I changed anything.

Not because the meetings were badly run. They were efficient. The problem was structural: as a CEO of a bootstrapped company operating in 30 countries, I was the bottleneck on every major decision. Pricing changes, market entry calls, hiring at VP level — everything routed through me, and I was making most of these decisions with whatever mental energy was left after the operational noise.

In September 2025 I stopped doing it that way.

THE SETUP

Every major decision now goes through a council of 5 AI agents before I touch it. Each agent has a fixed role:

- REVENUE STRATEGIST (DeepSeek Chat): argues from a pure revenue and margin lens - CTO (DeepSeek Chat): evaluates technical feasibility, build cost, scalability risk - CMO (DeepSeek Chat): asks what the market signal is, whether timing is right - PRODUCT MANAGER (DeepSeek Chat): stress-tests assumptions, identifies what we do not know - DEVIL'S ADVOCATE (DeepSeek Chat): explicitly assigned to break the other four's reasoning

All five run in parallel on the same question. Then Gemini 2.5 Pro with extended thinking synthesizes their outputs — not a summary, but a structured analysis of where they agree, where they conflict, and why. DeepSeek Reasoner does a final pass as a sanity check before I read anything.

A REAL EXAMPLE: GAMBLEGRIP PRICING

In October we were running GambleGrip (our iGaming CRM vertical) at $49/month for the Starter tier. The council session took 8 minutes total compute time.

Revenue Strategist: "At $49 you are attracting operators who cannot afford proper CRM. These are churn risks and support burdens." CTO: "The infrastructure cost per client at this tier is $18/month. Margin is thin and will compress as we scale support." CMO: "The $49 price point signals 'tool' not 'platform'. iGaming operators evaluate vendors on credibility, not price." PM: "We have zero data on whether $49 customers convert to higher tiers. This is an assumption we have not tested." Devil's Advocate: "You are assuming enterprise operators care about price signal. Some just want the cheapest option that works."

Gemini synthesis: move to enterprise-only pricing, remove self-serve Starter tier, test with next 10 inbound leads.

We did. Close rate on inbound went from 18% to 31% over the next 6 weeks. Average contract size went from $490 MRR to $2,800 MRR.

THE NUMBERS AFTER 5 MONTHS

- Strategic decision time: down 40% (from ~6 hours/week to ~3.5 hours) - Product iteration speed: 1.5x faster (fewer revision cycles because the initial brief is sharper) - Decision reversal rate: from ~22% to ~8% (we reverse fewer decisions within 30 days)

WHAT DOES NOT WORK

The council does not have internal context unless I give it explicitly. It does not know that a specific enterprise client threatened to churn last month. It does not know which team member is about to quit. Without a knowledge base feed, it is reasoning from general business principles, not from our specific situation.

We are building a knowledge base pipeline — structured summaries of weekly OKR updates, key client signals, team feedback — that feeds into each council session as context. It is not done yet. Right now, briefing the council well is still a manual skill.

The other failure mode: autonomous AI agents. We tried AutoGPT-style autonomous execution in Q2 2024. It generated plausible-looking action plans and executed none of them correctly without supervision. Abandoned after 3 weeks.

What works is supervised AI plus human checkpoints at the decision boundary. The agents debate. I decide.

THE CODE

The Debate Engine that powers this is open source. It is a Python framework that orchestrates parallel agent calls, enforces role constraints, and routes to a synthesis model. We use it internally and we have put it on GitHub for anyone who wants to build the same system.

→ github.com/nealkhis/debate-engine