Abstract

The "one-person company" is not a traditional sole proprietorship or a scaled-down version of a small enterprise, but rather a new organizational form in which AI undertakes most knowledge work while a small number of humans bear goal setting and responsibility closure. Current AI applications generally remain at the "single-point tool" stage: writing copy, creating images, making videos, making PPTs—each scattered, lacking unified organizational collaboration mechanisms and management systems, resulting in AI being powerful but unable to "work like a company." Based on GT6's practice, we propose the core proposition of "Organizational AI": to enable AI to operate a company, AI must be upgraded from "models capable of generating content" to "agent clusters embedded in organizational structures and permission systems, with messaging collaboration capabilities, able to call enterprise operation toolchains, and auditable and governable." This paper constructs a conceptual model and technical layering of the one-person company from the perspectives of organizational theory, systems engineering, and software architecture, and presents GT6's action plan: using messaging as the organizational nervous system, enterprise operations and management systems as the organizational skeleton, data staticization and indexable knowledge as organizational memory, and API/MCP tooling capabilities as organizational muscle, ultimately forming a deployable, measurable, and governable AI Agent cloud. This paper is not a product introduction, but rather uses GT6's engineering practice as a research subject to discuss the feasible paths for one-person companies at the technical, organizational, and governance levels.

Keywords: One-person company; Organizational AI; AI Agent; Enterprise operating system; MCP; Messaging; Permission governance; Staticized JSON; Auditable automation

1. Introduction: Why "AI is Powerful" but Still "Can't Run a Company"

Over the past two years, large language models have significantly improved in knowledge coverage and reasoning capabilities, and society's expectations for AI have moved from "assisting writing" to "replacing positions." However, in reality, the vast majority of AI applications still exhibit the same symptom: efficiency improvements occur at the task level, not the organizational level. People use AI to write an article, but still need humans to copy, paste, format, upload, and publish; use AI to generate an image, but still need humans to decide which page to place it on, what size to crop it, and which copy to pair it with; use AI to find a batch of products, but still need humans to complete the chain of listing, pricing, inventory, marketing, customer service, and financial reconciliation.

This means:

AI as a "single-point tool" is locally optimal;
Companies as "systems engineering" require global coordination;
What is missing is not "smarter models," but "application layers that enable models to enter organizational structures and assume position responsibilities."

We call this gap: the chasm from tool-based AI to organizational AI. The "one-person company" is precisely the minimum viable organizational form to bridge this gap: when a company's human employees approach one, issues of organizational collaboration, management systems, permission allocation, data closure, and responsibility tracking manifest in the sharpest way, forcing us to advance AI applications from "generation" to "operation."

2. Conceptual Definition: What is a "One-Person Company"

2.1 Not "One Person Does Everything," but "One Person is Responsible for All Results"

Traditional small teams rely on multi-person division of labor; individual entrepreneurs rely on personal multi-tasking; the one-person company emphasizes:

Decision-making power and responsibility are concentrated in humans;
Execution power is maximally outsourced to AI (and a small number of external services);
Organizational capability is obtained through systematization and agent collaboration, not through employment expansion.

Therefore, the "one-person company" is closer to an organizational structure of Human Responsibility Center + Agent Execution Cluster. Its key is not "saving people," but:

Breaking down positions and processes into orchestratable, measurable, and auditable work units;
Handing these units to agents that can call tools for execution;
Having humans make final judgments on goals, boundary conditions, and risks.

2.2 The One-Person Company is a "Compression Experiment" of Organizational Forms

From an organizational theory perspective, the essence of a company is not just producing content or providing services, but:

Reducing collaboration costs through systems and processes;
Reducing risks through permissions and governance;
Accumulating organizational memory through information systems;
Forming self-regulation through incentives and feedback.

When you compress people to one, these capabilities do not disappear; they only migrate from "human-to-human collaboration" to "human-agent-system" collaboration. Thus, the one-person company is an extreme stress test of the enterprise operating system: any missing processes, permission chaos, incoherent data, or uncontrollable tools immediately become systemic failures.

3. Current State Critique: Why "Tool-Based AI" Cannot Support Enterprise Operations

Current AI application layer issues can be summarized as three structural defects:

3.1 Fragmentation: Capabilities are Distributed Across Platforms, Not Within Organizations

Writing is on Platform A, image creation on Platform B, advertising on Platform C, customer service on Platform D. Data is scattered, permissions are scattered, contexts are scattered, making it difficult for AI to complete cross-system closed loops even if it can complete single-step tasks.

3.2 The Interaction Center Remains "Human-Machine Interface," Not "System Interface"

Most products place AI in chat boxes, letting people "command" with natural language. This is effective for inspiration and content generation, but insufficient for enterprise operations:

Enterprise processes require clear inputs and outputs, state machines, and permission validation;
Require traceable audit logs and rollback capabilities;
Require stable APIs and tool interfaces to ensure agents can execute repeatedly.

3.3 Lack of "Organizational Structure Mapping": No Positions, Departments, Permissions, or Governance

Enterprises are not task lists, but responsibility systems: who can do what, under what conditions, who bears the consequences if something goes wrong, how to approve, how to audit. Tool-based AI often bypasses these constraints, making it unable to enter real business environments.

4. Theoretical Framework: The Four-Layer Structure of Organizational AI

To make AI work like a company, we propose an engineerable four-layer model (analogous to "computer architecture"):

Organizational Nervous System (Communication Substrate): Messaging and event bus
Organizational Skeleton (Enterprise Operating System): Operations and management systems (people, information, transactions, funds)
Organizational Memory: Indexable data, staticized knowledge, state, and logs
Organizational Muscle (Tooling & Actuation): Callable API/MCP tools, orchestratable executors
And adding an indispensable cross-cutting layer on top:
Organizational Immune System (Governance & Safety): Permissions, risk control, auditing, compliance, and rollback

5. Key Technical Proposition One: Messaging is the Prerequisite for "Organizational Emergence"

In traditional companies, organizational collaboration is achieved through meetings, IM, tickets, and email; in one-person companies, these "human-to-human channels" must be transformed into "human-agent-agent" collaboration channels.

5.1 Why "Chat Interface" Does Not Equal "Organizational Communication"

Chat interfaces solve "dialogue"; organizational communication solves "collaboration":

Collaboration requires structured task assignment (who is responsible, deadlines, dependencies, state changes);
Requires asynchronous coordination (agents notifying each other, waiting, retrying);
Requires event-driven mechanisms (order creation triggers customer service and finance agents; article publication triggers distribution and advertising agents).

5.2 Making Communication "Most Comfortable for Humans, Most Usable for AI"

The instant messaging module's surface experience resembles WeChat/WhatsApp because human managers need low-friction daily interaction; but the underlying layer resembles API/MCP services because agents need stable, programmable communication interfaces.
This design of "one system, two subjects" essentially abstracts organizational communication into a unified protocol:

Humans use UI;
Agents use interfaces;
Organizations use messages and events to maintain a consistent "fact stream."

6. Key Technical Proposition Two: Enterprise Operations and Management Systems are Necessary Conditions for "AI Deployment"

If agents are viewed as employees, then enterprise systems are the office: without an office, employees cannot work on the ground even if they are smart.

6.1 Four Core Object Types of the "Enterprise Skeleton"

From an engineering perspective, enterprise systems can be abstracted into four object types:

(1) Users and Permissions (Identity & Access)
Tenants, departments, teams, roles, permissions, approval chains. It determines "who can do what."
In one-person companies, this system is still necessary: because agents need to be governed as "digital employees," avoiding overreach and misoperations.

(2) Information and Content (Information Assets)
Articles, documents, product materials, tasks, knowledge bases. It determines "what the company knows and how it remembers."

(3) Transaction Models (Business Transaction Types)
Regular e-commerce, subscriptions, matching, crowdfunding, group buying, wholesale discounts, etc. It determines "how the company makes money."

(4) Funds and Accounting (Cashflow & Ledger)
Orders, payment interfaces, fund flows, reconciliation, and risk control. It determines "how the company takes responsibility for results."

7. Key Technical Proposition Three: Organizational Memory Must Be "Indexable, Staticizable, and Traceable"

Human companies form memory through systems and experience; AI companies must turn memory into searchable data structures.

7.1 Why Staticize JSON

Most information in enterprise operations is not open-domain Q&A, but strongly structured facts (products, orders, permissions, prices, inventory, contract terms).
For these facts, vector search is not necessarily superior; deterministic indexing + staticized caching is often faster, cheaper, and more controllable.

7.2 "Traceability" is More Important Than "Smarter"

Company systems must answer:

Who published this content (human or a certain agent)?
What is the basis (which materials were referenced, which rules were triggered)?
How to roll back if errors occur (retract publication, refund, revise version)?

Therefore, organizational memory is not just a "knowledge base," but "state machine + logs + versions." This is also the foundation of the organizational immune system.

8. Key Technical Proposition Four: Tooling Capabilities Determine Whether Agents Can "Execute," Not Just "Advise"

Large models excel at making suggestions, but enterprises need execution. Execution means:

Can create/modify objects (publish articles, list products, create campaigns);
Can trigger processes (approval, advertising, refund, subscription billing);
Can collaborate across systems (website, social media, CRM, payment, warehousing).

Methodologically, this is equivalent to:

Mapping natural language intents to tool calls;
Writing tool call results back to organizational state;
Broadcasting state changes as organizational events;
Ultimately forming a closed loop.

9. Organizational Immune System: One-Person Companies Must Be More "Controllable" Than Traditional Companies

When employees become agents, risks do not decrease; instead, they appear in new forms:

Faster speed, faster error propagation;
Larger tool permissions, higher costs of overreach;
Easier content generation, more concentrated compliance and copyright risks.

Therefore, one of the core competitive advantages of one-person companies is "governance capability." Governance requires:

Principle of least privilege: Each agent only has the permissions needed to complete position tasks;
Configurability of approval and two-person rules: High-risk actions must be confirmed by humans;
Audit logs and explainability records: Preserve tool call chains and basis;
Rollback mechanisms: Content retraction, product delisting, order freezing, fund suspension;
Policy engine: Solidify business rules into executable constraints.

10. Action Plan: Hand Over the "Company World Dimensions" to AI

We break down the action plan into five implementable steps (from research to engineering):

10.1 Step One: Role Modeling

Break down the enterprise into positions rather than function points: sales, content, customer service, operations, finance, compliance, delivery.
Each position defines:

Input: Visible information scope (permissions)
Tools: Callable capability sets (MCP/API)
KPI: Measurable outputs (lead count, conversion rate, publication frequency, refund rate, etc.)
Risk boundaries: Which actions require approval/prohibition

10.2 Step Two: Message-Driven Organization

Enable agents and agents, agents and humans to collaborate through unified messaging and event mechanisms:

Task dispatch
State synchronization
Exception reporting
Result archiving

10.3 Step Three: Everything as Tools

Turn all enterprise operations into tools:

Content: Write, review, format, publish, distribute
Products: Selection, listing, pricing, inventory, promotion
Transactions: Orders, subscriptions, group buying, crowdfunding
Funds: Payment, reconciliation, refund, risk control

10.4 Step Four: Memory as Asset

Precipitate "what the company knows" into indexable assets:

Staticized JSON: Fact snapshots and caching
Structured documents: Terms, FAQs, SOPs
Versions and logs: Traceable, rollbackable

10.5 Step Five: Agent Cloud

Connect messaging modules + operations management modules with agent frameworks to form a deployable, observable, and governable agent cluster, i.e., the AI Agent cloud.

11. Verifiability: How to Measure the "Organizational Intelligence" of One-Person Companies

We propose four quantifiable indicator families:

Closed-loop indicators: Automatic closed-loop ratio from intent to result (e.g., from "write article" to "publish + distribute + review")
Collaboration indicators: Success rate of cross-agent dependent tasks and average coordination latency
Governance indicators: Overreach attempt count, intercepted high-risk operation ratio, audit traceability coverage
Economic indicators: Unit output cost (token + tool calls + human approval time), and marginal expansion cost

12. Discussion: The Social and Engineering Implications of One-Person Companies

One-person companies do not mean "humans are replaced by AI," but mean:

Enterprise expansion methods shift from "employment expansion" to "system expansion";
Management capabilities shift from "managing people" to "managing agents and tools";
Competitive advantages shift from "information asymmetry" to "process and governance capabilities";
Entrepreneurship barriers shift from "resources" to "architecture and methods."

For software engineering, one-person companies bring forward many capabilities that were previously "only needed by medium and large enterprises" to small teams: permissions, auditing, processes, risk control, data governance. It forces us to redefine the minimum form of "enterprise software": not a scaled-down version of ERP, but "a company operating system that can be operated by AI."

13. Conclusion: GT6's Core Proposition

Models are the brain, but companies are systems;
To achieve one-person companies, we must fill the two gaps of messaging and enterprise operations systems, and make all capabilities tooled and governed;
The key to organizational AI is not "better at talking," but "able to collaborate, execute, take responsibility, and be audited."

Appendix A: Formal Definitions and Event Sourcing Models

A.1 Core Objects and Set Definitions (Formal Objects)

We abstract the "one-person company" system as a discrete dynamic system composed of subjects, resources, actions, policies, events, and states:

Subject Set:
[
\mathcal{S} = \mathcal{H} \cup \mathcal{A}
]
Where (\mathcal{H}) is the human set (usually size close to 1), and (\mathcal{A}) is the agent set (multi-position AI Agents).
Resource Set:
[
\mathcal{R} = {\text{User/Org}, \text{Info}, \text{Transaction}, \text{Funds}, ...}
]
Action Set (all operations callable via API/MCP):
[
\mathcal{U} = {create, read, update, delete, publish, approve, refund, reconcile, ...}
]
Permission and Policy Set:
[
\mathcal{P} = {\text{RBAC rules}, \text{ABAC constraints}, \text{risk policies}, \text{approval gates}}
]
Event Set (atomic facts of event sourcing):
[
\mathcal{E} = {\text{ResourceCreated}, \text{ResourceUpdated}, \text{ApprovalRequested}, \text{Published}, \text{PaymentCaptured}, ...}
]
Global State:
[
X_t = f(\mathcal{E}_{\le t})
]
The system's "truth" is determined by the event stream, and any view (staticized JSON, indexes, reports) is projected from the event stream.

A.2 State Machine Definition of Organizational Closed Loop (Closed-Loop State Machine)

A.2.1 Closed-Loop State Set

[
\Sigma = {S_0, S_1, S_2, S_3, S_4, S_5, S_\bot}
]

(S_0): Intent Raised
(S_1): Task Formed
(S_2): In Execution
(S_3): Artifact Produced
(S_4): Governance Check
(S_5): Actuated
(S_\bot): Aborted

A.2.2 State Transition Function

[
\delta: \Sigma \times \mathcal{E} \rightarrow \Sigma
]
Examples:

(S_0 \xrightarrow{\text{TaskCreated}} S_1)
(S_2 \xrightarrow{\text{ToolCallSucceeded}} S_2) (loop execution)
(S_3 \xrightarrow{\text{ApprovalRequested}} S_4)
(S_4 \xrightarrow{\text{Approved}} S_5)
Any (S_i \xrightarrow{\text{PolicyViolation}} S_\bot)

A.2.3 Closed-Loop "Completeness" Definition

[
\text{ClosedLoopComplete} \iff (X_t \in S_5) \wedge \text{AuditCoverage}(X_t) \ge \theta
]

A.3 Event Sourcing and Projections

A.3.1 Event Logs are "Immutable Facts"

Suggested minimum fields:

event_id (globally unique)
event_type
occurred_at (UTC)
actor_type (human/agent/system)
actor_id
tenant_id
resource_type/resource_id
payload (change content)
correlation_id (same closed loop/transaction chain)
causation_id (triggering relationship)

A.3.2 Staticized JSON is Event Projection

[
\Pi_k(\mathcal{E}_{\le t}) \rightarrow J_k
]
Where (J_k) is a JSON view (e.g., product master JSON, product brief JSON, article publication status snapshot, order reconciliation view).

A.3.3 Idempotency & Consistency

Tool calls carry idempotency_key
Deduplicate when writing events, ensuring "at most once execution"
Projections update asynchronously, eventually consistent; if failed, write ProjectionLag and retry

A.4 Safety & Liveness Properties

A.4.1 Safety

Permission Safety:
[
\forall e \in \mathcal{E},\ \text{Authorized}(actor(e), action(e), resource(e)) = true
]
Fund Safety (High-risk actions must be approved):
[
action \in {\text{refund}, \text{payout}, \text{price_drop}} \Rightarrow \exists \text{ApprovedEvent}
]
Rollbackability: Critical execution actions must have rollback strategies and rollback event types.

A.4.2 Liveness

Closed-Loop Reachability:
[
\Pr(\text{reach } S_5 \mid S_0) \ge p_0
]
Timeout and Degradation: Timeout enters (S_\bot) and triggers human takeover.

A.5 From "Messaging Tool" to "Organizational Event Bus"

Chat messages lean toward human semantics; organizational events lean toward system semantics. Bind both to the same closed-loop chain through correlation_id:

Humans understand (chat)
Systems compute (events)

Appendix B: Reference Architecture Diagrams and Interface Lists (Deliverable Engineering Specifications)

B.1 Reference Architecture Diagram (ASCII Blueprint)

┌──────────────────────────────────────────────────────────────┐
│                       Human Responsibility Center             │
│   (Owner / Admin)                                             │
│   - Goal setting  - Risk acceptance  - Final approval         │
└───────────────▲──────────────────────────────────────────────┘
                │ Chat / Dashboard / Mobile
                │
┌───────────────┴──────────────────────────────────────────────┐
│                 Communication Substrate (IM)                   │
│  - Human↔Agent messaging  - Agent↔Agent messaging              │
│  - Task assignment  - Notifications  - Correlation IDs         │
└───────────────▲──────────────────────────────▲───────────────┘
                │                              │
         Tool calls (MCP/API)           Events / status updates
                │                              │
┌───────────────┴──────────────────────────────┴───────────────┐
│             Enterprise Operating System (Core Domains)         │
│  1) Identity & Access: tenant/org/role/permission/approval      │
│  2) Information: articles/docs/products/tasks                   │
│  3) Transaction: orders/subscriptions/crowdfunding/group-buy    │
│  4) Funds: payment interfaces, ledger, reconciliation           │
└───────────────▲──────────────────────────────▲───────────────┘
                │                              │
      Write events / commands            Read projections / indexes
                │                              │
┌───────────────┴──────────────────────────────┴───────────────┐
│                Organizational Memory Layer                      │
│  - Event Store (immutable)                                       │
│  - Projections (static JSON snapshots)                           │
│  - Indexes (search/lookup)                                       │
│  - Audit Logs & Versioning                                       │
└───────────────▲──────────────────────────────▲───────────────┘
                │                              │
       Observability/Policy             Data feeds for agents
                │                              │
┌───────────────┴──────────────────────────────┴───────────────┐
│                     AI Agent Cloud                              │
│  - Agent registry & deployment                                   │
│  - Role templates (Sales/Content/CS/Ops/Finance/Compliance)       │
│  - Policy enforcement + tool permission boundaries               │
│  - Schedulers / workflows / retries / fallbacks                  │
└──────────────────────────────────────────────────────────────┘

B.2 API/MCP Dual-Service "Contractual" Principles

Semantic Consistency: Action names, input/output fields, error code meanings are consistent
Governance Consistency: Permission validation, approval requirements, audit fields are consistent
Idempotency Consistency: Support retry/deduplication (same idempotency_key)

B.3 Core Domain Interface List (Example-Level Minimal Closed-Loop Tool Set)

B.3.1 Identity & Access

iam.user.create/read/update/disable
iam.org.create/read/update
iam.role.create/assign/revoke
iam.permission.grant/revoke
iam.approval_flow.define
iam.approval.request/approve/reject
iam.session.audit.query

B.3.2 Information

content.article.draft.create/update
content.article.review.request/approve/reject
content.article.publish/unpublish
content.asset.image.generate/resize/background_remove
content.doc.ingest/version/list
product.create/update
task.create/assign/update/status

B.3.3 Transaction

order.create/update/cancel
subscription.plan.create/subscribe/unsubscribe
crowdfunding.campaign.create/pledge/close
groupbuy.create/join/settle
matching.create/quote/accept

B.3.4 Funds

payment.intent.create/capture/void
refund.request/approve/issue
ledger.entry.post
reconcile.run/report
risk.flag/freeze/release

B.4 Audit Field Specifications (Audit Schema Specification)

B.4.1 General Audit Fields (required for all tool calls)

tenant_id
actor_type: human | agent | system
actor_id
actor_role (optional)
request_id
correlation_id
idempotency_key
tool_name
tool_version
input_hash
output_ref
policy_decision: allow | require_approval | deny
approval_id (if required)
started_at / finished_at / latency_ms
result: success | fail
error_code / error_message
rollback_supported
rollback_action_ref

B.4.2 Content Compliance and Copyright (recommended)

source_citations
copyright_risk_level
pii_detected
brand_policy_flags

B.4.3 Funds and Risk Control (required)

amount / currency
counterparty_id
risk_score
risk_rules_triggered
freeze_status
manual_override_by

B.5 Agent Position Template "Deployment Specifications" (Agent Template Spec)

B.5.1 Agent Template Fields

agent_id / agent_name
role_type
goal (objective function, measurable)
allowed_tools[] (whitelist)
data_scope (readable/writable data range)
approval_matrix
policy_pack
memory_profile
retry_policy
handoff_policy

B.5.2 KPI and Observability

tasks_completed/day
success_rate
approval_required_rate
rollback_rate
avg_latency
cost_per_outcome

B.6 Engineering Implementation Recommendations for "Staticized JSON + High-Speed Indexing"

Canonical Snapshots: Product master JSON, product brief JSON, article snapshots, order snapshots
Index Structures (Index Views): by_id, by_tag/category/price_range, by_status
Consistency Strategy: Event write success → asynchronous projection update; failure → record lag event + retry queue; if necessary, degrade to read master database

Appendix C: One-Page Comparison of Organizational AI and Classic Organizational Theory (Including Architecture Mapping)

C.1 One-Page Comparison Table: Theoretical Propositions → Design Principles → Architecture Mapping

Classic Organizational Theory	Core Proposition (Academic Perspective)	Organizational AI Design Principles (Engineerable)	Architecture Mapping (Modules/Mechanisms)
Transaction Cost Theory (Coase/Williamson)	Firms exist because internal coordination can reduce search, negotiation, supervision, and execution costs; the higher the uncertainty, the more boundaries tend toward internalization.	Let AI bear "coordination costs," internalize micro-transactions (content creation/publication/reconciliation/customer service), and reduce friction through system interfaces.	Communication (IM) as internal coordination; enterprise operations systems internalize processes as objects; API/MCP makes execution callable and reusable.
Principal-Agent Theory	Goal misalignment + information asymmetry → moral hazard/adverse selection; need systems, monitoring, and auditing to reduce agency costs.	Govern AI as "digital employees": least privilege, approval gates, audit tracking, rollback, observable KPIs.	IAM (tenant/department/role/permission) + approval flows; event sourcing and audit fields; mandatory approval for high-risk actions; Agent template whitelists.
Organizational Information Processing Theory (OIPT)	The higher the uncertainty, the stronger information processing capabilities organizations need; structure, processes, and IT are used to match information processing needs.	Use messaging + events + projections to increase information processing bandwidth: information is routable, subscribable, replayable; staticized views reduce cognitive burden; multi-agent parallel processing.	Human↔AI, AI↔AI messaging collaboration; event streams; staticized JSON + indexes as low-latency organizational memory; multi-Agent collaborative parallelization.

C.2 "Architectural Propositions" Breakdown Under the Three Major Theories

C.2.1 Transaction Cost Perspective: One-Person Company = Hand Coordination Costs to AI, Internalize Transactions into Systems

Search Costs: Finding materials, finding historical basis → Staticized JSON + indexes
Communication Costs: Position handover, goal alignment → Messaging + tasks/states
Supervision/Execution Costs: Publication, listing, refund, and other execution actions → Tooled calls (API/MCP) + permissions/auditing

Inference: When "execution is callable and results are traceable," enterprise expansion shifts from "employment expansion" to "system expansion," decoupling personnel count from capacity.

C.2.2 Principal-Agent Perspective: AI's Risk is Not Insufficient Capability, But Overreach and Unaccountability

Information Asymmetry: Owner doesn't know the basis → Event sourcing records tool call chains and data sources
Moral Hazard (Analogy): Imperfect objective functions lead to deviation → Policy packs + approval gates + least privilege
Assessment and Governance: AI also needs KPIs → Success rate, approval rate, rollback rate, unit output cost, and other observable indicators

Conclusion: The reason one-person companies must have "enterprise-level permissions and auditing" is not because of scale, but because "few people but great power."

C.2.3 Organizational Information Processing Theory Perspective: Organizational AI is Systemic Scaling of Information Processing Capabilities

Structured Information Flow: Routable/subscribable/replayable → Messaging + events
Reduce Cognitive Burden: Context snapshots → Staticized JSON (master/brief)
Parallel Processing: Position division of labor increases throughput → Multi-Agent collaboration + workflow/retry/fallback

Inference: Organizational structure shifts from "people" to "agents," IT systems shift to "company operating systems callable by agents."

C.3 One-Sentence Summary

Transaction Cost Theory explains "why one-person companies are possible": AI reduces coordination costs, enabling much work to be internalized.
Principal-Agent Theory explains "why governance is necessary": The stronger agents are, the more permissions, auditing, and approval are needed to control risks.
Organizational Information Processing Theory explains "why messaging/events/memory layers are needed": The higher the uncertainty, the more structured information processing and parallel capabilities are needed.

File Note: This document combines the main paper body with Appendices A/B/C, suitable as an initial draft of GT6's academic concept white paper, and can be directly used for further typesetting into PDF or submission-style reports.

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One-Person Company System

AI - One-Person Company