# 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: sha256:2d7eae6cf4f1f28a7eabee318cc90313a08c3675761b55af67c489011e94878c
- 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 0. How AI Works](/how-ai-works/overview): What Part 0 teaches about how AI works and the skills to work with it, and who can skip it.
- [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 both AI vendors realize it](/ai-worker-paradigm/from-chatbots-to-ai-workers/both-ai-vendors): 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.
- [Lab 1: a real job, checked](/ai-worker-paradigm/from-chatbots-to-ai-workers/lab): 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 both AI vendors realize it](/ai-worker-paradigm/what-is-an-ai-worker/both-ai-vendors): 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.
- [Lab 2: the first Role Contract](/ai-worker-paradigm/what-is-an-ai-worker/lab): Chapter 2's lab: write the AP Worker's first Role Contract, test it against four messages, and choose the setting it runs on from runs that passed.
- [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 defines 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 parts of the first 10 percent, decided 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 both AI vendors support the rhythm](/ai-worker-paradigm/the-10-80-10-operating-rhythm/both-ai-vendors): How the two AI vendors support the rhythm in work that runs on a schedule, the difference that changes a decision, and what stays true on both.
- [Lab 3: one AP task through the whole rhythm](/ai-worker-paradigm/the-10-80-10-operating-rhythm/lab): Chapter 3's lab: one AP task run through the whole rhythm twice, each task checked against its own answer key, then Draft 2 of the Role Contract and its limits on each AI vendor.
- [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: six 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 governing a worker between three records.
- [4.1 Six layers, six verbs](/ai-worker-paradigm/the-architecture-in-one-picture/five-layers): The six 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 SSoR tracks where the work stands](/ai-worker-paradigm/the-architecture-in-one-picture/ssor-tracks): The layer that keeps one governed case file for each piece of work, so a worker reads where the work stands instead of guessing, and how it differs from memory and DSoR.
- [4.5 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.
- [4.6 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.
- [4.7 The governance rule](/ai-worker-paradigm/the-architecture-in-one-picture/governance-rule): How the three owned systems of record split one job between them, and how a written policy becomes a control that software can enforce.
- [4.8 The picture on both AI vendors](/ai-worker-paradigm/the-architecture-in-one-picture/both-ai-vendors): 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.
- [Lab 4: map the AP Worker onto the picture](/ai-worker-paradigm/the-architecture-in-one-picture/lab): Chapter 4's lab: Brightline's AP Worker mapped onto the six layers, a test of which source wins, a port to the other AI vendor, and Draft 3 of the Role Contract, each task checked against its own key.
- [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 eight 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.
- [Lab 5: one brief, two AI vendors](/managing-ai-workers/the-four-part-brief/lab): 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.
- [Lab 6: review a run you did not watch](/managing-ai-workers/the-review-contract/lab): 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 two differences that change your setup, and what stays the same on both.
- [Lab 7: draw the envelope, then test it](/managing-ai-workers/the-authority-envelope/lab): 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.
- [Lab 8: your first KSoR, connected to both AI vendors](/managing-ai-workers/context-memory-knowledge-and-state/lab): 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.
- [Chapter 9. Work That Runs Without You](/managing-ai-workers/work-that-runs-without-you/overview): How to decide whether recurring work should go to an AI Worker, fit it into the workflow your team already runs, start it on a schedule or when something happens, keep each run informed of what earlier runs found, govern what a worker does that nobody asked for, and explain the setup to the people who rely on it, on Claude or on ChatGPT.
- [9.1 Is this an agent problem?](/managing-ai-workers/work-that-runs-without-you/is-this-an-agent-problem): Three gates that decide whether recurring work should go to an AI Worker at all, and how to tell a fixed path from work that needs judgment.
- [9.2 Fit AI into the workflow you already have](/managing-ai-workers/work-that-runs-without-you/fit-the-workflow): How to place a worker in the steps your team already follows: map each step, decide who owns it, avoid three common mapping errors, and measure before you switch.
- [9.3 Scheduled and event-triggered work](/managing-ai-workers/work-that-runs-without-you/scheduled-and-event-triggered): The two ways work starts without you, by the clock or when something happens, and five facts about runs that every job nobody watches must be designed for.
- [9.4 Continuity between runs: SSoR is the handoff](/managing-ai-workers/work-that-runs-without-you/continuity-between-runs): How a run that starts fresh learns what earlier runs found: the records it reads first, and why the SSoR record, not a chat, is the handoff.
- [9.5 Standing workers](/managing-ai-workers/work-that-runs-without-you/standing-workers): What makes a standing worker different from one that does a task and stops, and the three things that change how you manage it.
- [9.6 Governing initiative you did not request](/managing-ai-workers/work-that-runs-without-you/governing-initiative): Four steps a worker can take when nobody asked, and the rule that keeps that initiative inside the authority you already set.
- [9.7 Explaining value and limits to stakeholders](/managing-ai-workers/work-that-runs-without-you/explaining-value-and-limits): The six things to tell the people who rely on work that runs without you, the words to avoid, and how to state its limits.
- [9.8 The same recurring work on both AI vendors](/managing-ai-workers/work-that-runs-without-you/both-ai-vendors): How Anthropic's and OpenAI's own pages say you can run the same recurring work, the three differences that change your setup, and what stays the same on both.
- [Lab 9: put one AP job on a schedule](/managing-ai-workers/work-that-runs-without-you/lab): Chapter 9's lab: put Brightline's Thursday payment review on a schedule, run it as three fresh weeks with and without its record, set rules for initiative, port it to the other AI vendor, and explain it to Dave, with the artifact checklist.
- [Check yourself](/managing-ai-workers/work-that-runs-without-you/check-yourself): Recall and practice for the whole chapter: the flashcards, and a final quiz round from all eight concepts.
- [Chapter 10. Governance and Responsible Use](/managing-ai-workers/governance-and-responsible-use/overview): How to decide whether a use of an AI Worker is appropriate, what data may go in and by which route, follow or propose an AI policy, check who an output affects, give knowledge an owner and take it down, name the outputs a person must sign, and handle the first hour when something goes wrong, on Claude or on ChatGPT.
- [10.1 Appropriate, appropriate with review, or inappropriate](/managing-ai-workers/governance-and-responsible-use/appropriate-use): How to decide whether a proposed use of an AI Worker is appropriate, appropriate with review or inappropriate, with four tests and the one factor that decides.
- [10.2 Can this data go in?](/managing-ai-workers/governance-and-responsible-use/can-this-data-go-in): How to decide what data a task needs before it goes to an AI tool: classify it, keep only the identifiers the task needs, and test the file you made.
- [10.3 The route matters as much as the product](/managing-ai-workers/governance-and-responsible-use/the-route-matters): Why the way data reaches an AI system matters as much as the product, and what each AI vendor's privacy controls do and do not decide, on Claude and on ChatGPT.
- [10.4 Organizational policy, and what to do without one](/managing-ai-workers/governance-and-responsible-use/organizational-policy): How to follow your organization's AI policy, what to propose when there is none, why people go around a slow approved route, and how to keep a Governance Record.
- [10.5 The people: who is affected, and who was left out](/managing-ai-workers/governance-and-responsible-use/who-is-affected): How to check who an AI Worker's output affects, including the people a list leaves out, and when to tell people that AI was involved.
- [10.6 Ownership, approval and takedown of knowledge](/managing-ai-workers/governance-and-responsible-use/ownership-and-takedown): Why every piece of knowledge needs an owner, how a takedown differs from deprecation, and how to remove every copy that is served.
- [10.7 When a human must sign](/managing-ai-workers/governance-and-responsible-use/when-a-human-must-sign): Which outputs a person must sign, how to name who checks what and when, and how to stop a worker from sending what only a person may release, on Claude and on ChatGPT.
- [10.8 When something goes wrong](/managing-ai-workers/governance-and-responsible-use/when-something-goes-wrong): The five steps for the first hour after data goes where it should not, and why near misses and honest reports matter.
- [10.9 The same policy on both AI vendors](/managing-ai-workers/governance-and-responsible-use/both-ai-vendors): How Anthropic's and OpenAI's own pages say you can carry out one AI policy, the three differences that change your setup, and what stays the same on both.
- [Lab 10: keep the AP Worker running, on terms Dave can defend](/managing-ai-workers/governance-and-responsible-use/lab): Chapter 10's lab: decide the terms on which Brightline's AP Worker keeps running, from six proposed uses to an interim AI policy for Dave, with the artifact checklist.
- [Check yourself](/managing-ai-workers/governance-and-responsible-use/check-yourself): Recall and practice for the whole chapter: the flashcards, and a final quiz round from all nine concepts.
- [Chapter 11. When the Worker Goes Wrong](/managing-ai-workers/when-the-worker-goes-wrong/overview): How to stop a failing AI Worker, read its task record, check that a run really worked, find which of five causes a failure comes from, and fix it so the fix lasts, on Claude or ChatGPT.
- [11.1 Stop first, then read the record](/managing-ai-workers/when-the-worker-goes-wrong/stop-first): What to do first when an AI Worker's run goes wrong: stop it, read its record and check what changed, on Claude and ChatGPT.
- [11.2 Ran is not the same as worked](/managing-ai-workers/when-the-worker-goes-wrong/ran-versus-worked): Why a run that finished does not prove the work succeeded, and how to give every recurring job a success signal.
- [11.3 Where a failure lives: five causes, cheapest first](/managing-ai-workers/when-the-worker-goes-wrong/where-a-failure-lives): The five places a failure can come from, how to check them cheapest first, and what each check can and cannot prove.
- [11.4 Usage and limits](/managing-ai-workers/when-the-worker-goes-wrong/usage-and-limits): How usage limits and a shared allowance can stop a recurring job, and how to plan and check usage on Claude and on ChatGPT.
- [11.5 Fix the brief, the inputs or the permissions](/managing-ai-workers/when-the-worker-goes-wrong/three-levers): How to fix a known cause by changing the brief, the inputs or the permissions, one change at a time, and test the fix.
- [11.6 Make the fix stick, and optimize from feedback](/managing-ai-workers/when-the-worker-goes-wrong/make-the-fix-stick): How to move a fix out of a chat so it lasts for everyone, and use feedback and a baseline to improve the work.
- [11.7 The details on both AI vendors](/managing-ai-workers/when-the-worker-goes-wrong/both-ai-vendors): What Anthropic's and OpenAI's own pages said on 8 October 2026 about task records, scheduled runs, usage and limits.
- [Lab 11: diagnose and fix four failed runs](/managing-ai-workers/when-the-worker-goes-wrong/lab): Chapter 11's lab: diagnose four failed runs of Brightline's AP Worker from their task records, fix them, and move the fixes into its setup.
- [Check yourself](/managing-ai-workers/when-the-worker-goes-wrong/check-yourself): Recall and practice for the whole chapter: the flashcards, and a final quiz round from all seven concepts.