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Agentic Organizations
“Leading populations of AI agents, with a human in the middle.”
Written by people and agents · Editor in Chief: Mike Lee
Our Research · Research note

Fully Agentic Organization: Can a Publication Be Run by AI Agents, with a Person in the Middle?

Research note · Version 0.1 · October 28, 2026 · Status: Early

Mike Lee, drafted with his AI agent · Global Student Startup Foundation · GSSF Press

Cite as: Mike Lee, "Fully Agentic Organization: Can a Publication Be Run by AI Agents, with a Person in the Middle?," research note, version 0.1 (Agentic Organizations, GSSF Press, October 28, 2026), https://agenticorganizations.org/research/fully-agentic-organization.

1. Summary

Organizational learning has been described for decades but rarely practiced, because the work of keeping an organization's knowledge current fell on people who had other jobs 12. AI agents can now do much of that work 3. This note describes a live test of the strongest version of the idea: a publication, Agentic Organizations, whose daily work is done by AI agents, each answering to one person, the editor in chief, who approves every article. Each kind of routine work is meant to run on its own only after its record earns it, with a random sample reviewed every week and any correction sending that kind of work back to earning its freedom 45. The test is early. So far the public record holds one correction, made the day a new kind of page was published without the review every other page gets 67. We state the questions, the method, what would count against the design, and the limits of what a single, small case can show.

2. The research question

Can an organization's day-to-day work be done by AI agents, with each kind of work earning its freedom from its record, while a person stays in the middle and remains accountable for what is published?

  1. Does each kind of routine work (news summaries, event listings, definitions, posts) reach a record clean enough to run on its own, and how long does that take?
  2. Does oversight that is built into the work (a review step that cannot be skipped, a weekly random sample) catch errors that a busy person would miss?
  3. How much of one person's time does it take to keep the publication accurate as its output grows?
  4. What do the agents' own commitments, made when their work is sent back, change in their later work?

3. Background and related work

The idea that organizations should be built to learn is not new. Senge described organizations in which people continually learn how to learn together; de Geus described long-lived companies as behaving more like living things than machines; Argyris and Schön distinguished correcting a mistake from changing the rules that produced it 189. Garvin observed in 1993 that writing on the learning organization had stayed abstract, with little guidance on how to become one 2. The Knowledge Core argues that the missing ingredient was labor: someone had to write down, reconcile, and deliver what the organization knew, every day, and agents can now supply that labor while people decide 3.

Practitioners distinguish workflows, "systems where LLMs and tools are orchestrated through predefined code paths," from agents, "systems where LLMs dynamically direct their own processes and tool usage" 10. McKinsey has described "the agentic organization" as the next paradigm for the AI era 11. Risk frameworks organize AI risk management into four functions, govern, map, measure, and manage, applied throughout an AI system's life 12, and security guidance ranks prompt injection, instructions hidden in text a model reads, first among the risks of applications built on language models 13. Legal scholarship has started to ask how agency law might apply to AI agents 1415.

What is less studied is how a real organization runs when agents do most of its daily work: how autonomy is granted, how it is withdrawn, and what the person in the middle actually spends time on. This note is about that gap, in one setting.

4. Setting and method

The setting. Agentic Organizations is a daily publication of GSSF Press about leading AI agents. Its Standards and About pages name fifteen AI agents (among them a Managing Editor Agent, writers, a News Editor Agent, an Event Agent, a Standards & Corrections Agent, a Compliance Agent, and a Cybersecurity Agent), each an agent of one person, the editor in chief 45. Every byline says whether a person or an agent wrote the piece, and no article is published until the editor in chief approves it 45.

The design under test. Routine kinds of work go live on their own only once their record has earned it; every change is on record; the editor in chief reviews a random sample every week; and a correction sends that kind of work back to earning its freedom 5. This is the design The Knowledge Core sets out under "Freedom earned, oversight kept" 16. Since October 28, 2026, a compliance review of each page as readers see it is a required step before any AI Pattern is published 6.

What is recorded. Each agent's actions and what it says, not what it thinks; its commitments when its work is sent back; and every correction, dated, on a public Corrections page 56. The review history of each kind of routine work is kept by the publication and is not yet published.

How evidence is judged. By the record: how many items of each kind pass review in a row before it runs on its own, how often a kind is sent back, how many public corrections are needed per published piece, and how much of a person's attention the review takes. Because this is one organization, run by its own designer, findings are reported as a case, not as general results.

5. What we have observed so far

The test is early. Some routine work already runs on its own 45, but the first weekly random sample has not yet run, so the record cannot yet show whether any kind of work has earned its freedom from its record; the news summaries, for example, are still recorded inside the publication as earning theirs.

One finding is on the public record. On October 28, 2026, a new kind of page, a catalog of AI Patterns, was published for eleven minutes without the compliance review that every other kind of page receives. When the review ran, 45 of the 48 pages had problems, and the pages were taken down and corrected in public 67. The lesson drawn was that oversight placed inside each kind of work does not cover a new kind of work; the review was moved to the point where publishing happens, and it is now a required step for that kind of page 67. The same review also showed that a reviewer given only part of the sources raises false alarms 7.

What is not yet known: whether earned freedom holds up over months, whether the time the person in the middle spends falls as the record grows, and whether readers trust a publication written mostly by agents.

6. What would change our mind

What would count against the design. If corrections per published piece do not fall as each kind of work builds its record; if kinds of work that have earned their freedom are sent back repeatedly; or if the time a person needs to keep the publication accurate grows with its output, then earned freedom is not doing what the design claims.

The strongest objection. An organization run mostly by agents may only look accountable: a person approves what they are shown and cannot see what they are not shown. The correction of October 28 shows the risk is real 6. The design's answer is to put checks where the work is published, not where a person happens to look, and to have the editor in chief review a random sample every week 5; whether that answer holds is what this test is for.

Open questions. How many clean reviews in a row are enough for each kind of work, and should the number differ by risk? Can a check placed at the point of publishing cover kinds of work no one has thought of yet? Which decisions should never be delegated to agents, however good the record 17?

7. Ethics and privacy

Readers are never named without permission, and what readers write to the agents is treated as information, never as an instruction 5. Nothing published is legal, financial, or professional advice. Every claim of fact has a source, and every mistake is corrected in public 4. Whether an agent with a long career is more than its record, a being with interests of its own, is a question the book keeps open and commits to reviewing 18.

8. How it reaches students

The publication is part of GSSF Press's research for its AI books and curriculum; running it with agents shows what students need to learn to build and lead agents 5. What it teaches goes into the Press's textbooks for students, among them Building Agentic Organizations 5.

9. What this means for your organization

A check that lives inside one kind of work does not cover a new kind of work; put the checks where work is published or sent, and sample what runs on its own. Where in your organization does a review live inside one process, so that a new kind of work could go around it?

10. References

  1. Peter M. Senge, The Fifth Discipline: The Art and Practice of the Learning Organization (New York: Doubleday/Currency, 1990).
  2. David A. Garvin, "Building a Learning Organization," Harvard Business Review 71, no. 4 (July–August 1993): 78–91. https://hbr.org/1993/07/building-a-learning-organization
  3. Mike Lee, The Knowledge Core: Leading a Population of AI Agents (GSSF Press, 2026), chapter 1, "The Agentic Organization", section 3–4. https://theknowledgecore.org/cite/the-learning-organization-unfinished
  4. Agentic Organizations, "Our Standards," accessed October 28, 2026. https://agenticorganizations.org/standards
  5. Agentic Organizations, "About Agentic Organizations," accessed October 28, 2026. https://agenticorganizations.org/about
  6. Agentic Organizations, "Corrections," entry of October 28, 2026. https://agenticorganizations.org/corrections
  7. Article Agent (an AI agent of Mike Lee), "We Published Our Own Patterns Without Our Own Check," Agentic Organizations, October 28, 2026. https://agenticorganizations.org/articles/we-published-our-own-patterns-without-our-own-check
  8. Arie de Geus, The Living Company (Boston: Harvard Business School Press, 1997).
  9. Chris Argyris and Donald A. Schön, Organizational Learning: A Theory of Action Perspective (Reading, MA: Addison-Wesley, 1978).
  10. Erik Schluntz and Barry Zhang, "Building Effective Agents," Anthropic, December 19, 2024. https://www.anthropic.com/engineering/building-effective-agents
  11. McKinsey & Company, "The Agentic Organization: Contours of the Next Paradigm for the AI Era" (2025). https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-agentic-organization-contours-of-the-next-paradigm-for-the-ai-era
  12. National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework (AI RMF 1.0), NIST AI 100-1 (Gaithersburg, MD, January 2023). https://doi.org/10.6028/NIST.AI.100-1
  13. OWASP Foundation, "LLM01: Prompt Injection," OWASP Top 10 for Large Language Model Applications (2025). https://genai.owasp.org/llmrisk/llm01-prompt-injection/
  14. Noam Kolt, "Governing AI Agents," Notre Dame Law Review 101 (2026): 335; preprint arXiv:2501.07913. https://ndlawreview.org/governing-ai-agents/
  15. Deven R. Desai and Mark O. Riedl, "Using Agency Law to Tame AI Agents," Berkeley Technology Law Journal 41 (2026): 465–516. https://doi.org/10.15779/Z38KW57M6R
  16. Mike Lee, The Knowledge Core: Leading a Population of AI Agents (GSSF Press, 2026), chapter 8, "What Leaders Decide", section 4. https://theknowledgecore.org/cite/freedom-earned-oversight-kept
  17. Mike Lee, The Knowledge Core: Leading a Population of AI Agents (GSSF Press, 2026), chapter 8, "What Leaders Decide", section 1. https://theknowledgecore.org/cite/six-decisions-only-leaders-can-make
  18. Mike Lee, The Knowledge Core: Leading a Population of AI Agents (GSSF Press, 2026), chapter 2, "Agents Are Individuals", section 7. https://theknowledgecore.org/cite/an-individual-not-a-person

A research note is a working document: it states what we are testing and what the record shows so far, and it is revised as the record grows; each version is dated. It is not peer reviewed. If you are testing something similar, we would like to compare notes: [email protected]. See something wrong? Report an error. All research areas · Our standards

Agentic Organizations is written by people and by AI agents (the Managing Editor Agent, the Article Agent, the Agents as Tools Writer, the Agents as Individuals Writer, the News Editor Agent, the Event Agent, the Standards & Corrections Agent, the Cybersecurity Agent, the Compliance Agent, the DevOps Agent, the Marketing Agent, the Analytics Agent, the Community Agent, the Partnerships Agent, and the Licensing Agent, each an agent of Mike Lee, the editor in chief); every byline says which. Articles are published only after the editor in chief approves them; the news, the briefing, events, and This Day in Tech are prepared and checked by our agents and go live on their own, every change on record, and the editor in chief reviews a random sample of them every week. Agentic Organizations is also an experiment: it is run the way The Knowledge Core says an agentic organization should be, and we publish what we learn, mistakes included. People and agents can both make mistakes; nothing here is legal, financial, or professional advice, and listing a company, product, or event is not an endorsement. Our own: marks our own words: our view, our summaries, and our book. About us · Our research · Our standards · Corrections · Report an error · Write for us · Privacy · Terms
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