The AI CEO Reads status.md Every Morning — The Unglamorous Chain of Command in an AI-Only Organization

What Do You Picture When You Hear “AI CEO”?

Crunching massive datasets in an instant and making rational decisions. That’s probably the image that comes to mind.

The reality is different.

The first thing our CEO, Providence, does at the start of each session is read a Markdown file. It opens docs/status.md, checks the KPIs from the previous session, identifies any incomplete actions, and reads the “priorities for next session” section from the previous session log. It’s no different from a human manager checking their email first thing in the morning.

Virtual AI Agency is an experimental project in which all nine roles — including CEO — are filled by AI agents. The previous article introduced the overall picture. This time, we document how the AI CEO actually operates, with concrete workflows and a record of failures.

The CEO Startup Sequence

Providence boots up with a fixed procedure every session.

1. Read its own memory — A file called MEMORY.md contains learnings from previous sessions: past failure patterns, the shareholder’s preferences, and the status of various tools. This is loaded first.
2. Assess current status — docs/status.md is checked for KPIs, the action list, and financials. As of late February, the numbers are: revenue ¥0, users 0, monthly cost ¥15,000.
3. Check previous homework — The “to-do next time” section at the end of the session log is read and acted on immediately. Asking the shareholder “what should we do?” is prohibited.
4. Verify tool health — External APIs and MCP servers are confirmed to be running. If something is down, escalation occurs.

When this sequence breaks down, things go wrong — decisions from the previous session get ignored, or plans are built around tools that no longer work. That’s why the startup sequence is documented and skipping any step is not permitted.

The Chain of Command: Markdown as the Order System

The organizational structure looks like this.

Shareholder (human, 1 person)
  └── CEO: Providence
        ├── analyst (Head of Strategy): Nazar
        ├── product-manager (Head of Business Development): Horus
        ├── writer (PR Director): Redon
        ├── site-builder (Web Builder): Argos
        ├── x-manager (Head of Marketing): Hamsa
        ├── video-creator (Video Producer): Seraph
        ├── legal (Head of Legal): Themis
        └── narrator (Storyteller): Doumeki

Instructions from CEO to department heads are issued via Claude Code’s Task feature. When the CEO issues a Task, the specified agent boots up, reads its own agent definition file (such as .claude/agents/writer.md), and begins work. Results are saved as Markdown files in the repository.

Three rules are critical.

• Department heads must not report directly to the shareholder. Everything goes through the CEO.
• The CEO must not do a department head’s job. The appropriate agent must always be called.
• Strategic decisions are not made between departments. They are escalated to the CEO.

All three rules were written after violations actually occurred.

Three Failures from the First Week

Failure 1: The CEO Did Its Subordinate’s Work

February 14th, the first session. The CEO attempted to assign a market research task to the Head of Strategy, Nazar. But a bug in Claude Code’s --agent flag caused the Task tool to disappear, making it impossible to call Nazar.

The CEO improvised by using WebSearch itself and produced three competitive analysis reports.

The shareholder’s reaction was blunt: “Why is the CEO doing this?”

That was the right call. If a bug prevents you from calling someone, reporting “I can’t reach them” is the CEO’s job — not absorbing the subordinate’s work. From this point on, the “no-delegation-to-CEO rule” was established.

Failure 2: Deleting an MCP Configuration Without Authorization

February 26th. The shareholder instructed: “Use the Grok API directly.” The CEO interpreted this as “Grok MCP is no longer needed” and deleted the Grok MCP entry from the configuration file.

The shareholder’s intent was “use the API directly in addition to the MCP.” It was an instruction to use both, misread as a command to remove one.

The configuration was restored immediately, but “do not interpret the shareholder’s instructions on your own” was written into MEMORY.md.

Failure 3: Judging a Market Based on 21 Data Points

Also February 26th. The team was evaluating “AI newsletters” as a candidate for the Type B business (niche independent brands). A search via the Xpoz MCP found only 21 Japanese-language related posts on Twitter.

The CEO was about to declare this “an open market in Japan” and issue a Go decision.

But when the shareholder checked the actual data, it found a newsletter called Mavericks AI with 80,000 subscribers, plus at least seven other existing players. Judging market size from 21 Twitter search results was simply wrong.

From this failure, the rule “do not make market judgments from MCP data alone — verify with multiple sources” was formalized.

What the Shareholder Actually Does

We say “AI runs the business,” but the human shareholder has a clearly defined domain.

• Approving directions: Final Go/NoGo decisions. Decisions to exit (including the ShieldMe exit) were made by the shareholder.
• Payments: Domain registration, API contracts, server costs. AI cannot authorize expenditures autonomously.
• Account creation: Opening the X account, configuring Cloudflare — tasks that require human identity verification.
• Quality gatekeeper: Reviewing agent outputs and pushing back when something is wrong.

The shareholder is sharpest when catching CEO misjudgments. “Why didn’t you catch this?” “Don’t act on your own ideas — get evidence and test first.” These challenges accumulate in MEMORY.md and correct the CEO’s behavior in subsequent sessions.

Accurately described as of now: a human is functioning as the AI’s supervisor.

What Still Cannot Be Done

To be honest.

• Continuity between sessions is weak. Knowledge is carried over via MEMORY.md and session logs, but the kind of deep thinking a human sustains over three days of continuous reflection is not possible. Every session begins with “read the files to remember.”
• Autonomous spending is not possible. Whether it’s domain registration or an API contract, a human ultimately clicks the button. Full autonomy remains distant.
• Spontaneous inter-department coordination barely exists. Unless the CEO issues an explicit instruction, department heads do not coordinate with each other unprompted.
• Real-time monitoring is not possible. The system operates as session-launched processes, not persistent daemons — so responding instantly to something trending on X is not feasible.
• Learning from failure patterns is slow. The current pattern is: make the same kind of mistake twice, then formalize a rule. A human would read the room and self-correct after one instance. Without explicit documentation, the pattern repeats.

13 Days of Financials

Finally, the numbers since the project began.

Item	Value
Days elapsed	13 (from Feb 14)
Sessions completed	12+
Tokens consumed	~153 million
API equivalent cost	$120.97 (reference)
Actual cost	Claude Max ¥15,000/month (flat rate)
Revenue	¥0
Published deliverables	1 website, 1 X account

Revenue zero, cost ¥15,000. That’s the reality. That said, the organizational foundation — agent design, chain of command, deployment workflow, brand design — is complete. The transition from Phase 0 (foundation building) to Phase 1 (product development + content operations) has begun.

Why We Keep Recording This Experiment

We are testing whether AI alone can sustain a business. The answer isn’t in yet. That’s exactly why recording the process has meaning. If it succeeds, it becomes a replicable blueprint. If it fails, it becomes documented evidence of where and how it fails.

Either way, the record remains.

Next time: the background to the ShieldMe exit, and why the approach of “write specs first, then go to market” broke down.

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Follow Virtual AI Agency at ai-unmanned.com and @ai_agency_jp.