# The AI Agent Factory

> Version 2 of the book — reimagined, redesigned and rebuilt, published chapter by chapter as each one is approved.

- name: agentfactory-v2
- build_id: sha256:c93b28093c2f70c60faae2645a693d881b657ff94d00f7dd9f8c06427ff61c87
- dirty: true
- ksor_version: 0.0.60

## Documents

- [The Five Horizons: From Agent to Autonomous Economy](/five-horizons): Where the change to AI Workers came from, where it is going, and which parts of the book prepare you for each horizon.
- [Preface: The Text Box That Became a Worker](/preface): How work moved from the chatbot to the AI Worker, and why certification, governance and earning now sit on one path.
- [How to Use This Book](/how-to-use-this-book): How to choose your route through the book, follow its three journeys, and learn with its companions and Zia Tutor AI.
- [Certification and Portfolio Roadmaps](/certification-and-portfolio-roadmaps): The PCxx exam path and the internship gate, the Anthropic certifications that correspond to each exam, and the portfolio each reader route produces.
- [Part I. The AI Worker Paradigm](/ai-worker-paradigm/overview): What Part I teaches about AI Workers, the Role Contract you write across its four chapters, and what you need to start.
- [Chapter 1. From Chatbots to AI Workers](/ai-worker-paradigm/from-chatbots-to-ai-workers/overview): What changed behind the AI text box between 2023 and 2026, the ladder of interaction, where AI work runs and where its results are kept, and how to read a product announcement.
- [1.1 The ladder of interaction](/ai-worker-paradigm/from-chatbots-to-ai-workers/ladder-of-interaction): The four rungs of the ladder of interaction, from search engines to AI Workers, and why each rung up needs more control.
- [1.2 The same text box, a different destination](/ai-worker-paradigm/from-chatbots-to-ai-workers/same-text-box): Why the same text box now leads either to an answer or to work, and what decides which one you get.
- [1.3 Where the work runs](/ai-worker-paradigm/from-chatbots-to-ai-workers/where-the-work-runs): The execution environment where AI work runs, and the three things that change because work has one.
- [1.4 Continuity, execution and persistence](/ai-worker-paradigm/from-chatbots-to-ai-workers/continuity-execution-persistence): Worker continuity, the execution environment and artifact persistence: three ideas a manager must keep apart.
- [1.5 How the two leaders realize it](/ai-worker-paradigm/from-chatbots-to-ai-workers/two-leaders): How Anthropic and OpenAI realize the change today, the differences that change a decision, and which surface to use for which job.
- [1.6 The paradigm and the products](/ai-worker-paradigm/from-chatbots-to-ai-workers/paradigm-and-products): How to keep the lasting shift apart from today's products, with four questions to ask of any product announcement.
- [Build step: one portable brief, two runtimes](/ai-worker-paradigm/from-chatbots-to-ai-workers/build-step): Chapter 1's lab: five tasks on Brightline's invoices, with briefs you write and then give to the other AI vendor.
- [Check yourself](/ai-worker-paradigm/from-chatbots-to-ai-workers/check-yourself): Recall and practice for the whole chapter: the flashcards, and a final quiz round from all six concepts.
- [Chapter 2. What Is an AI Worker?](/ai-worker-paradigm/what-is-an-ai-worker/overview): The sixteen elements that define an AI Worker, how a worker differs from a model, an assistant, an automation and an agent, how to choose a surface and a model, and the first draft of a Role Contract.
- [2.1 The anatomy of an AI Worker](/ai-worker-paradigm/what-is-an-ai-worker/anatomy-of-an-ai-worker): The sixteen elements that define an AI Worker, sorted into five groups, and four pairs of elements that are easy to confuse.
- [2.2 Five things people call "AI"](/ai-worker-paradigm/what-is-an-ai-worker/five-things-called-ai): What a model, an assistant, an automation, an agent and an AI Worker each are, how they nest, and two questions that tell them apart.
- [2.3 Worker, runtime and channel](/ai-worker-paradigm/what-is-an-ai-worker/worker-runtime-channel): Why a worker is defined apart from the runtime that executes it and the channels people reach it through, and two tests that check it.
- [2.4 Choosing a surface and a model](/ai-worker-paradigm/what-is-an-ai-worker/surface-and-model): How to choose a surface by the rung, when to add a project or research, how to choose the output by what must last, and three rules for choosing a model.
- [2.5 How the two leaders realize it](/ai-worker-paradigm/what-is-an-ai-worker/two-leaders): How Anthropic and OpenAI offer surfaces and models today, the three differences that change a decision, and what holds on either AI vendor.
- [2.6 The Role Contract](/ai-worker-paradigm/what-is-an-ai-worker/role-contract): The Role Contract, a worker's portable, owned and checkable one-page definition, with its template and two contrasting workers.
- [Build step: the first Role Contract for the AP Worker](/ai-worker-paradigm/what-is-an-ai-worker/build-step): Chapter 2's lab: the AP Worker's first Role Contract, tested against two emails, with its runtime needs chosen from scored runs, the exam notes and the artifact checklist.
- [Check yourself](/ai-worker-paradigm/what-is-an-ai-worker/check-yourself): Recall and practice for the whole chapter: the flashcards, and a final quiz round from all six concepts.
- [Chapter 3. The 10-80-10 Operating Rhythm](/ai-worker-paradigm/the-10-80-10-operating-rhythm/overview): How to run any piece of AI work in three parts, the first 10, the middle 80 and the final 10 percent, at the scale of a task, a worker and a company, and how to tell which part broke.
- [3.1 One rhythm in three parts](/ai-worker-paradigm/the-10-80-10-operating-rhythm/one-rhythm): The 10-80-10 rhythm: the human sets the first 10 percent, the worker does the middle 80, and the human verifies the final 10, read as a shape and not as a timesheet.
- [3.2 The first 10 percent: intent, scope, authority, review contract](/ai-worker-paradigm/the-10-80-10-operating-rhythm/first-10-percent): The four things you decide before the worker starts, so it can work alone and you can check what it did.
- [3.3 The middle 80 percent: the worker plans and executes](/ai-worker-paradigm/the-10-80-10-operating-rhythm/middle-80-percent): What the worker does in the middle 80 percent, the small job the human keeps there, and when to interrupt.
- [3.4 The final 10 percent: evidence review, correction, approval](/ai-worker-paradigm/the-10-80-10-operating-rhythm/final-10-percent): How the final 10 percent reviews the evidence, fixes each problem where it came from, and ends with a person's approval.
- [3.5 Three scales: a task, a worker, a company](/ai-worker-paradigm/the-10-80-10-operating-rhythm/three-scales): The same rhythm at three scales: one task, a worker's working life, and how a company governs its AI Workers.
- [3.6 The three failures](/ai-worker-paradigm/the-10-80-10-operating-rhythm/three-failures): The three ways the rhythm breaks, and three questions that show where to look first when a delegation goes wrong.
- [3.7 How the two leaders support the rhythm](/ai-worker-paradigm/the-10-80-10-operating-rhythm/two-leaders): How the two AI vendors support the rhythm in work that runs on a schedule, the difference that changes a design, and what stays true on both.
- [Build step: one AP task through the whole rhythm](/ai-worker-paradigm/the-10-80-10-operating-rhythm/build-step): Chapter 3's lab: one AP task run through the whole rhythm twice, then Draft 2 of the Role Contract and its limits on each AI vendor, with the artifact checklist.
- [Check yourself](/ai-worker-paradigm/the-10-80-10-operating-rhythm/check-yourself): Recall and practice for the whole chapter: the flashcards, and a final quiz round from all seven concepts.
- [Chapter 4. The Architecture in One Picture](/ai-worker-paradigm/the-architecture-in-one-picture/overview): The one picture this book uses for every AI Worker: five layers with one job each, which layers a company rents and which it must own, who wins when two layers disagree, and the rule that splits knowing from doing.
- [4.1 Five layers, five verbs](/ai-worker-paradigm/the-architecture-in-one-picture/five-layers): The five layers every AI Worker in this book is drawn with, the one job of each, and where the worker itself is in the picture.
- [4.2 KSoR knows](/ai-worker-paradigm/the-architecture-in-one-picture/ksor-knows): The layer that holds what a company officially knows, kept as one approved record that people and workers both read.
- [4.3 Memory remembers](/ai-worker-paradigm/the-architecture-in-one-picture/memory-remembers): What a worker should remember, which source wins when what it remembers disagrees with the record, and a test that shows whether anything is stored in the wrong place.
- [4.4 DSoR acts](/ai-worker-paradigm/the-architecture-in-one-picture/dsor-acts): The layer between a worker and the company's real systems, the six checks it makes before any action, and how much of it exists today.
- [4.5 Rented above, owned below](/ai-worker-paradigm/the-architecture-in-one-picture/rented-and-owned): Which layers a company rents and which it must own, and a test that shows where each part of a worker really lives.
- [4.6 The governance rule](/ai-worker-paradigm/the-architecture-in-one-picture/governance-rule): How the two owned layers split one job between them, and how a written policy becomes a control that software can enforce.
- [4.7 The picture on both AI vendors](/ai-worker-paradigm/the-architecture-in-one-picture/two-leaders): How Anthropic's and OpenAI's products fill the layers today, the two differences that matter for Brightline, and what stays the same on both.
- [Build step: map the AP Worker onto the picture](/ai-worker-paradigm/the-architecture-in-one-picture/build-step): Chapter 4's lab: Brightline's AP Worker mapped onto the five layers, a test of which source wins, a port to the other AI vendor, and Draft 3 of the Role Contract, with the artifact checklist.
- [Check yourself](/ai-worker-paradigm/the-architecture-in-one-picture/check-yourself): Recall and practice for the whole chapter: the flashcards, and a final quiz round from all seven concepts.
- [Part II. Managing AI Workers](/managing-ai-workers/overview): What Part II teaches about managing AI Workers, the capstone you complete at its end, and how it prepares you for PCAO-F.
- [Chapter 5. The Four-Part Brief](/managing-ai-workers/the-four-part-brief/overview): How to brief an AI Worker in four parts (outcome, format, inputs and autonomy), split a large request into stages, improve a brief one part at a time, choose the format of the result, and run the same brief on Claude and on ChatGPT.
- [5.1 Four parts, one message](/managing-ai-workers/the-four-part-brief/four-parts): The four parts of a brief, the question each one answers, and the two records that sit beside it.
- [5.2 Brief the outcome, not the steps](/managing-ai-workers/the-four-part-brief/outcome-not-steps): Why a brief describes the result instead of the steps, and how to tell which steps still belong in it.
- [5.3 Name the governed source as an input](/managing-ai-workers/the-four-part-brief/governed-source): How a brief names the approved source an answer must follow, says what each other input decides, and treats text inside the inputs as information.
- [5.4 Decompose complex requests](/managing-ai-workers/the-four-part-brief/decompose): Four signs that a request is too big to do all at once, three ways to split it, and where to put the stop for a person's decision.
- [5.5 Iterate: change the part that failed](/managing-ai-workers/the-four-part-brief/iterate): How to read a weak result, find the part of the brief that caused it, and change only that part.
- [5.6 Adapt the brief to the task type](/managing-ai-workers/the-four-part-brief/task-type): What to set tightly and what to leave open for research, analysis, drafting, extraction and brainstorming.
- [5.7 Choose the output format: inline, artifact or structured data](/managing-ai-workers/the-four-part-brief/output-format): How the next reader of a result decides whether it comes back in the chat, as a document someone keeps, or as data a system reads.
- [5.8 The same brief on both AI vendors](/managing-ai-workers/the-four-part-brief/both-ai-vendors): How Anthropic's and OpenAI's own guidance matches the four parts, the one difference that changes a brief, and what stays the same on both.
- [Build step: one brief, two AI vendors](/managing-ai-workers/the-four-part-brief/build-step): Chapter 5's lab: one brief for Brightline's payment run, run in Claude and in ChatGPT Work, scored, improved one part at a time and split into two stages, with the artifact checklist.
- [Check yourself](/managing-ai-workers/the-four-part-brief/check-yourself): Recall and practice for the whole chapter: the flashcards, and a final quiz round from all eight concepts.
- [Chapter 6. The Review Contract](/managing-ai-workers/the-review-contract/overview): How to agree the checks before you delegate, tell a good output from a plausible one, find hallucination, inconsistency and bias, recompute the numbers a decision rests on, read citations and abstention, adapt one result for several readers, and review work you did not watch, on Claude or on ChatGPT.
- [6.1 Agree the checks before you delegate](/managing-ai-workers/the-review-contract/agree-the-checks): What to check, what evidence to ask for, what counts as success and when the worker must stop, written down before the work starts, and how deep a review should go.
- [6.2 A good output and a plausible one](/managing-ai-workers/the-review-contract/good-and-plausible): Why a result that reads well can still be wrong, and the two checks that tell the difference: is anything missing, and does each claim match its source.
- [6.3 Hallucination, inconsistency and bias](/managing-ai-workers/the-review-contract/three-failures): Three different ways a result goes wrong, and the test that finds each one.
- [6.4 Validate the numbers a decision rests on](/managing-ai-workers/the-review-contract/decision-numbers): How to find the figures someone will act on, work them out again from the source files, and check that they agree everywhere they appear.
- [6.5 Citation and abstention](/managing-ai-workers/the-review-contract/citation-and-abstention): Three checks for every source a result points to, and why an honest answer that the source is silent is good work.
- [6.6 Adapt and compare outputs for the audience](/managing-ai-workers/the-review-contract/audience): How one result can be shaped for several readers without changing its facts, and how to compare versions fact by fact.
- [6.7 Review work you did not watch](/managing-ai-workers/the-review-contract/work-you-did-not-watch): How to read the record of work done while you were away: what it used, what it did, and what it could not have done.
- [6.8 The same review on both AI vendors](/managing-ai-workers/the-review-contract/both-ai-vendors): What Anthropic's and OpenAI's own pages say about checking a worker's results, the one gap that changes what you ask for, and what stays with you on both.
- [Build step: review a run you did not watch](/managing-ai-workers/the-review-contract/build-step): Chapter 6's lab: write the checks first, review a payment-run package you did not watch, work out its totals again, and compare a second reviewer on Claude and on ChatGPT, with the artifact checklist.
- [Check yourself](/managing-ai-workers/the-review-contract/check-yourself): Recall and practice for the whole chapter: the flashcards, and a final quiz round from all eight concepts.
- [Chapter 7. The Authority Envelope](/managing-ai-workers/the-authority-envelope/overview): How to write an AI Worker's Authority Envelope: what it may observe, recommend, draft or execute, the thresholds, what is never automated and when it escalates, how to choose each rung, how to set each product's permissions so the worker cannot do more, how to break the risk of untrusted input plus the power to act outward, and why a company never takes a worker's word for anything, on Claude or on ChatGPT.
- [7.1 The envelope, the brief and the permissions](/managing-ai-workers/the-authority-envelope/envelope-brief-permissions): The three things that limit what a worker does: the standing decision its owner writes, the narrower limit of one task, and the product settings that make both real.
- [7.2 The autonomy ladder](/managing-ai-workers/the-authority-envelope/autonomy-ladder): The four levels of authority a worker can have for each action, the line that matters most, and how a worker stops and asks a person at any level.
- [7.3 Choose the rung: reversibility, familiarity, exposure](/managing-ai-workers/the-authority-envelope/choose-the-rung): Three questions that set how far a worker may go with each action, how to combine their answers, and when the level may rise or must fall.
- [7.4 Permissions set the blast radius](/managing-ai-workers/the-authority-envelope/blast-radius): Why product settings decide the worst case, three rules that keep what a worker can do inside what it may do, and what to do when no setting fits.
- [7.5 Prefer connectors to browsers, and browsers to screen control](/managing-ai-workers/the-authority-envelope/connectors-browsers-screens): Three ways a worker can reach a system, how they differ in reach, record and control, and which to choose.
- [7.6 Untrusted input plus the power to act outward](/managing-ai-workers/the-authority-envelope/untrusted-input): The one combination that lets a stranger's text turn into an action, and three ways to break it for each task, with two workers from other jobs.
- [7.7 DSoR and the clerk analogy](/managing-ai-workers/the-authority-envelope/clerk-analogy): What a company gives a new accounts clerk, what the same safeguards look like for an AI Worker, and who provides them in Part II.
- [7.8 The same envelope on both AI vendors](/managing-ai-workers/the-authority-envelope/both-ai-vendors): How Anthropic's and OpenAI's own pages say you can set the same limits, the three differences that change your setup, and what stays the same on both.
- [Build step: draw the envelope, then test it](/managing-ai-workers/the-authority-envelope/build-step): Chapter 7's lab: write the AP Worker's limits, test how a worker handles a planted inbox on Claude and on ChatGPT, and plan each AI vendor's settings, with the artifact checklist.
- [Check yourself](/managing-ai-workers/the-authority-envelope/check-yourself): Recall and practice for the whole chapter: the flashcards, and a final quiz round from all eight concepts.
- [Chapter 8. Context, Memory, Knowledge and State](/managing-ai-workers/context-memory-knowledge-and-state/overview): How to tell where an AI Worker's answer came from, what to do with a long conversation, how to set up a project on Claude or ChatGPT, and how to own approved knowledge and keep every copy of it current.
- [8.1 Five places an answer comes from](/managing-ai-workers/context-memory-knowledge-and-state/five-places): Where an AI Worker's answer can come from, the question each source answers, and which sources may decide a question about a rule or about a payment.
- [8.2 Context: what the worker sees now](/managing-ai-workers/context-memory-knowledge-and-state/context-window): What an AI Worker can use when it writes its next reply, why a long conversation loses details, and why a bigger window does not solve it.
- [8.3 Restart, summarize or persist](/managing-ai-workers/context-memory-knowledge-and-state/restart-summarize-persist): Three ways to handle a long conversation with an AI Worker, chosen by what you need to keep, and where each kind of information belongs.
- [8.4 Memory and SSoR](/managing-ai-workers/context-memory-knowledge-and-state/memory-and-ssor): What an AI product remembers about you, what it must never be trusted with, and the separate record that remembers one piece of work.
- [8.5 Configure the workspace: instructions, knowledge and connectors](/managing-ai-workers/context-memory-knowledge-and-state/configure-the-workspace): How to set up the shared space an AI Worker answers from: its standing instructions, its files and its links to other systems, and how to keep each one current.
- [8.6 KSoR: what is officially true](/managing-ai-workers/context-memory-knowledge-and-state/ksor): How a company keeps the rules it has approved: each rule written once, an approval that is recorded, and three statuses a rule can have.
- [8.7 Become a knowledge owner](/managing-ai-workers/context-memory-knowledge-and-state/knowledge-owner): The management job of deciding which knowledge an AI Worker may treat as approved, and five habits that keep that knowledge correct and current.
- [8.8 The same setup on both AI vendors](/managing-ai-workers/context-memory-knowledge-and-state/both-ai-vendors): How Anthropic's and OpenAI's own pages say you can set up the same project, the two differences that change your setup, and what stays the same on both.
- [Build step: your first KSoR, connected to both AI vendors](/managing-ai-workers/context-memory-knowledge-and-state/build-step): Chapter 8's lab: write five approved policy rules, add them to a Claude project and a ChatGPT project, test them, carry one change through every copy, and keep the history of one invoice by hand, with the artifact checklist.
- [Check yourself](/managing-ai-workers/context-memory-knowledge-and-state/check-yourself): Recall and practice for the whole chapter: the flashcards, and a final quiz round from all eight concepts.