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LLM Agents in Real Business Systems

This series documents how BookiAI designs, governs, and gradually automates LLM agents inside a real accounting system.

Unlike demo-style AI agents or prompt-based automations, this series focuses on responsibility, auditability, and system-level control.

LLMs are powerful at generating suggestions.
Business systems, however, must own decisions.

This series explains how we bridge that gap.

Why this series exists​

Most AI agent examples assume:

  • The model can decide
  • The system should trust the output
  • Users will correct mistakes if something goes wrong

That approach breaks down immediately in financial and regulated systems.

In BookiAI:

  • Every write has side effects
  • Every decision must be explainable
  • Automation must be earned, not assumed

This series captures the architectural thinking behind that approach.

What you will learn​

By following this series, you will learn:

  • Why LLMs should not directly write business records
  • How to separate generation, review, and execution
  • How to design object-based contracts such as
    Proposal → Review Report → Decision
  • How to build a Controller that governs AI behavior
  • How to start with manual execution and safely evolve toward automation
  • How prompts become configuration, not business logic

Who this is for​

This series is written for:

  • Founders building AI-native products
  • Engineers integrating LLMs into real systems
  • Architects designing controllable AI workflows
  • Anyone interested in AI governance beyond demos

No accounting background is required, but familiarity with software systems and APIs will help.

  1. Introduction
  2. Why LLMs cannot write ledgers directly
  3. Generate / Review / Controller model
  4. Object-based contracts for AI decisions
  5. Recommendation assurance and validation
  6. Controller: manual-first execution
  7. From manual control to automation
  8. Action libraries and system context
  9. Prompt governance and evolution

Each chapter builds on the previous one.

Relationship to other BookiAI guides​

This series complements existing BookiAI guides:

  • Accounting Fundamentals — how ledgers and journals are structured
  • AI Journal Entry — how AI assists accounting workflows

Together, they describe how AI participates in accounting without replacing responsibility.

A note on scope​

This is not a framework, SDK, or product announcement.

It is a living technical narrative documenting how LLM agents are designed, constrained, and evolved inside a real-world system.

👉 Continue with Introduction to begin the series.

Written by ChatGPT, reviewed by the BookiAI team.