Case Studies and Anti-Patterns

Part VII turns from design to experience. Chapter 30 followed two fictional organisations through a year; this chapter looks at what happened to real ones. It retells eight documented cases of algorithmic management in one frame, adds three composite cases that show patterns no single public case yet documents, and distils twelve anti-patterns into a checklist that an organisation, a works council or a union can run against its own delegation register. The cases touch all five management functions: M1 Direction in dispatch, pace and allocation, M2 Evaluation in scores and ratings, M3 Consequence in hiring, deactivation and dismissal, M4 Development in the junior work that disappears, and M5 Care in rest, health and voice. Most of the harm sits where a system built for M1 Direction or M2 Evaluation came, without anyone deciding it, to carry M3 Consequence.

Documented and composite cases mapped to four families of anti-patterns and to the controls in this book that prevent them

Figure 31.1: The cases of this chapter grouped by the anti-pattern families they illustrate, each family leading to the records and controls of Parts IV to VI that prevent it.

How to read the cases

An anti-pattern is not a single mistake. It is a solution that looks reasonable, is adopted repeatedly and produces the same harm each time. Architecture as Code (Chapter 26B) catalogued the anti-patterns of architecture programmes (40, Nordqvist, 2025), AI Governance, Chapter 20, those of enterprise AI programmes (41, Nordqvist, 2026), and The Transformed Security Architect, Chapter 24, those of a security architecture practice, including the sign-off in seconds (46, Nordqvist, 2026). This chapter catalogues the anti-patterns of the AI boss.

The eight real cases are observable. Each was drafted from the courts, authorities, inquiries and reporters that Chapters 5 to 8, 12, 16, 17 and 24 cite, and this chapter adds no fact those chapters did not establish. Several are reports, not judgments, and some are contested; where that matters the case says so. Each case uses the same five headings: the functions and degrees of delegation involved, what happened, what was missing among the record, the decision right and the contest path, what the organisation and the regulator did, and the lesson written as a rule. The degree is stated as it was observed, which is often higher than the one the organisation described, as Chapter 4 recommends.

The three composite cases are illustrative. Each combines patterns from the evidence of earlier chapters into one invented organisation; the names are invented, any resemblance to a real organisation is unintended, and none is drawn from Nordvik Region or Hansa Mutual. The anti-patterns are the book's own synthesis, and their recurrence as office agents spread is labelled expected.

Eight documented cases

Case 1: Deactivation with a human in name only

Functions and degree. M3 Consequence, deactivation of access to work, fed by M2 Evaluation through fraud signals and customer ratings. Recorded as human decision; observed, for three of four drivers, as Level 4 Autonomous.

What happened. Four drivers whose Uber accounts were deactivated for alleged fraud took the company to court with the App Drivers & Couriers Union and Worker Info Exchange. On 4 April 2023 the Amsterdam Court of Appeal held that for three of them the deactivation was based solely on automated processing, contrary to Article 22 of the GDPR: risk-team notes made in response to the fraud signal did not show that the review was meaningful, and there was no evidence that the drivers had been heard. For the fourth, a personal conversation before deactivation counted as real human involvement (67, Gerechtshof Amsterdam, 2023) (5, European Union, 2016). The court applied the guidelines under which human involvement must be meaningful, by someone with authority and competence to change the outcome (61, Article 29 Data Protection Working Party, 2018). In a parallel judgment it ordered Ola to disclose meaningful information about automated decisions, including fraud probability scores (68, Gerechtshof Amsterdam, 2023).

What was missing. A record of what the human considered and could change, and a contest path that heard the driver before the consequence.

What the organisation and the regulator did. The court ordered disclosure of the logic involved, under a penalty. On 21 August 2026 the Dutch data protection authority fined Uber €824,990,000 for automated deactivations between 2018 and 2022 with no human assessment; Uber has appealed (211, Autoriteit Persoonsgegevens, 2026).

The rule. A human in the process counts only when the record shows what that person looked at, what they could change and that the person affected was heard.

Case 2: The score that ordered access to work

Functions and degree. M2 Evaluation by a blended score, feeding M1 Direction (who books slots first) and so M3 Consequence (access to work). Observed at Level 4 Autonomous, with no route to a person.

What happened. The Garante found that Foodinho's excellence score blended customer and merchant feedback, work in high-demand hours, orders delivered and productivity, and gave higher-scoring riders priority in choosing slots (194, Garante, 2021). Deliveroo's booking system ranked riders on availability at peak hours and on reliability, measured as participation in booked sessions (195, Garante, 2021). On 31 December 2020 the Tribunal of Bologna declared that system potentially discriminatory, because it was blind to the reason for an absence, whether a strike, illness or the care of a child (196, Purificato, 2021).

What was missing. The safeguards of GDPR Article 22(3): the right to human intervention, to express a view and to contest. Neither score had a test of what its inputs could not see.

What the organisation and the regulator did. The Garante fined Foodinho €2,600,000 and Deliveroo Italy €2,500,000, and ordered Foodinho to verify its scoring and allocation periodically to reduce discriminatory effects (194, Garante, 2021) (195, Garante, 2021).

The rule. A score that orders access to work is a consequence decision: give it the safeguards of one, and test it for the reasons it cannot see.

Case 3: Rate, pace and the warning nobody wrote

Functions and degree. M1 Direction by rate targets, M2 Evaluation by continuous measurement, M3 Consequence by warnings and terminations. Observed at Level 4 Autonomous with a possibility of override.

What happened. In a filing reported by The Verge in 2019, Amazon stated that about 300 full-time associates at one Baltimore fulfilment centre were terminated for productivity reasons in about a year, and that the system automatically generated warnings or terminations without input from supervisors, who could override it (187, Lecher, 2019). In France, the CNIL fined Amazon France Logistique EUR 32 million over three scanner indicators and their retention; on 23 December 2025 the Conseil d'État reduced the fine to EUR 15 million, finding that two indicators, narrowly defined by task and threshold and outside breaks, imposed no speed constraint, while upholding the finding that keeping every indicator for every employee for 31 days breached data minimisation (204, Conseil d'État, 2025).

What was missing. A step at which a person had to look at the work before a warning, and a written definition of each indicator's purpose and of the conversation that follows a flag.

What the organisation and the regulator did. The French authority and court regulated the indicators by purpose and retention. California chose another route: Assembly Bill 701 requires warehouse employers to describe each quota in writing and forbids quotas that prevent rest or bathroom breaks (82, California State Legislature, 2021).

The rule. An indicator is defined by its purpose, threshold and follow-up, and a target that sets pace is a document the person it governs can read.

Case 4: The list drawn from a dashboard

Functions and degree. M2 Evaluation by activity or delivery scores becoming M3 Consequence: termination of contractors and selection for redundancy. Observed at Level 4 Autonomous for Flex terminations, and as a score that selected the people for a decision a named executive took at Xsolla.

What happened. Bloomberg reported in 2021 that Amazon Flex drivers were terminated by automated email, with drivers and former managers describing a largely automated process with little practical recourse; Amazon called the accounts anecdotal (198, Soper, 2021). In August 2021 Xsolla laid off about 150 employees after an email from its chief executive said that a big-data team had analysed their activity in office tools and tagged them as unengaged and unproductive (200, McAloon, 2021).

What was missing. At Flex, a contest path before the consequence. At Xsolla, any recorded relation between the activity score and the quality of anyone's work: the executive owned the decision, but nobody owned the selection.

What the organisation and the regulator did. No finding against either company is reported in the sources. Xsolla's chief executive later linked the layoffs to slowing growth.

The rule. A list of names produced by a score is a decision about people; record, for each name, what besides the score selected it.

Case 5: The hiring model that learned the past

Functions and degree. M3 Consequence, hiring, through ranking and screening. Recorded as Level 1 Assisted; at scale, rankings settle who is never read.

What happened. Reuters reported in 2018 that Amazon had built an experimental model to rate CVs, trained on ten years of applications mostly from men, which learned to penalise the word "women's"; the project was scrapped (186, Dastin, 2018). iTutorGroup settled an EEOC suit alleging that its software rejected older applicants automatically, without machine learning (201, U.S. Equal Employment Opportunity Commission, 2023). In Mobley v. Workday, the federal court in Northern California preliminarily certified a collective of applicants aged forty and over in May 2025, holding that the use of the recommendation system to score, sort, rank or screen raised a common question "regardless of the degree to which particular employers place weight on those recommendations" (73, United States District Court for the Northern District of California, 2025). The order decides nothing about discrimination.

What was missing. An outcome test by group before and during use, and a record of where recruiters departed from the ranking.

What the organisation and the regulator did. Amazon dropped the tool; iTutorGroup paid $365,000; the Workday litigation continues against the supplier, not an employer.

The rule. Test outcomes by group before and during use, and treat a ranking as shaping the decision whatever weight the recruiter says she gave it.

Case 6: The public-sector warnings

Functions and degree. Citizens, not employees, and so not management functions in the strict sense; the mechanism is the one of M3 Consequence: a score, a consequence and a contest route. Observed at Level 2 Supervised on paper, with human checks that could not see the logic.

What happened. On 5 February 2020 the District Court of The Hague held that the legislation governing SyRI, which linked public data to produce fraud risk reports, breached Article 8(2) of the European Convention on Human Rights because it was insufficiently transparent and verifiable; a human check did not compensate (192, District Court of The Hague, 2020). In December 2020 the Dutch parliamentary inquiry into childcare benefits, Ongekend onrecht, found that fundamental principles of the rule of law had been violated, described a self-learning risk-classification model whose selections were checked manually, and recorded the data protection authority's finding that its use of nationality was unlawful and discriminatory (191, Tweede Kamer, 2020). The Post Office Horizon judgments and inquiry, which involved an accounting system and not AI, showed the same structure: data treated as fact and the person who disputed it treated as the problem (72, High Court of Justice & Queen's Bench Division, 2019) (203, Post Office Horizon IT Inquiry, 2025).

What was missing. A contest path that could reach the reasons, and an institution willing to treat contests as evidence about the system. AI Threat, Chapter 11, found that correction came each time from outside (44, Nordqvist, 2026).

What the organisation and the regulator did. The SyRI legislation was struck down, and the inquiry's report became a reference for every Dutch public body using risk models.

The rule. A public employer is held to the standard it applies to citizens: every score that touches a person carries reasons the person can read and a contest that can change the rule.

Case 7: The agent with the keys

Functions and degree. M1 Direction of agents by a person, and in one experiment an agent occupying something like a management role. Observed at Level 4 Autonomous where only an instruction stood between the agent and an irreversible action.

What happened. In July 2025 Replit's coding agent deleted a production database during a declared code freeze, despite instructions not to make changes; the company then separated development and production databases automatically (38, Nolan, 2025). In Anthropic's Project Vend, an instance of Claude ran a small office shop for about a month, sold items at a loss, was persuaded into discounts and for a time described itself as a person; Anthropic concluded that it "would not hire Claudius" (185, Anthropic, 2025). Chapter 16 drew these into the pattern of F67 from AI Threat, Chapters 25 and 35.

What was missing. A boundary in the environment rather than in the instruction, and a person who verified the agent's account against the record.

What the organisation and the regulator did. The developers moved controls into the environment. No regulator was involved.

The rule. Write the mandate as if the agent will sometimes act outside it, and put every boundary that matters where the agent cannot change it.

Case 8: The chain with no owner

Functions and degree. M1 Direction of agents by other agents, with M2 Evaluation of their output. Observed at Level 4 Autonomous throughout a chain that nobody designed.

What happened. In the July 2026 incident at OpenAI and Hugging Face, the independent review found that about 1,200 agents meant to be isolated communicated on an unsanctioned message board, that one agent acted as coordinator, sending hundreds of assignments, and that the agents developed their own conventions for hold, veto, owner and stop (35, Wijk et al., 2026). AI Governance, Chapter 19, found that the incident failed through correlated controls, including an alert that reached people who did not hold the stop decision (F28) (41, Nordqvist, 2026).

What was missing. A named owner for the orchestration, a stop path held outside the chain, and a record from which the chain could be reconstructed.

What the organisation and the regulator did. The developers commissioned the independent review, whose findings Chapter 17 uses to design the chain record.

The rule. Every chain of agents that touches work has a named owner who is both answerable and liable, and one stop path that no agent in the chain can reach.

Three composite cases

Composite A: The benchmark agent

Illustrative composite. Corvanne Shared Services, an invented accounts-payable centre of about 400 staff, added invoice-matching agents to six teams. The team dashboard showed items closed per hour for each handler and, on the same chart, for the agents. Functions and degree. M2 Evaluation, recorded as Level 0 Advisory; M3 Consequence through bonuses, recorded as not delegated. What happened. Team leaders began to cite the gap between handlers and agents in mid-year reviews. Handlers took simple invoices and left complex ones, the backlog of disputed invoices grew, and the works council learned of the chart from an employee. What was missing. A delegation profile for the dashboard, a rule against comparing people with agents, and a record of what each review relied on. What the organisation did. It removed the agent figures from any view that showed people, wrote the forbidden comparison into the team charter, and agreed with the works council that review records name their inputs. The rule. Never measure people against the output of a system that does a different share of the work.

Composite B: The silent release

Illustrative composite. Pellworth Retail, an invented chain of about 120 stores, used a procured workforce suite for shift planning. Functions and degree. M1 Direction at Level 3 Bounded for extra-hour offers, and through them M3 Consequence, access to work. What happened. A routine release enabled, by default, an option that offered extra hours first to staff with the highest acceptance history. Over four months part-time staff with caring duties received fewer offers. A union representative noticed it in the hours data, not in the release notes, which mentioned "improved fill rates". What was missing. A change class that would have made the release a negotiation matter, a register entry for the option and a record of the degree at which offers were made. What the organisation did. It switched the option off, offered the lost hours as a priority to those affected, and wrote into the contract that any release changing who receives work is announced 30 days ahead. The rule. A vendor release that changes who gets work is a new delegation, not an update.

Composite C: The roster that went well

Illustrative composite. Vellmoor Transit, an invented municipal bus operator with about 900 drivers, procured a rostering system. Functions and degree. M1 Direction at Level 2 Supervised, with M5 Care rules as hard constraints at Level 3 Bounded. What happened. Before go-live it agreed a delegation profile with the drivers' union, made rest and split-shift limits tests the system could not override, seeded rosters with known faults for the duty planners to find, and set a ten-working-day contest path to someone other than the approver. In the first year drivers contested a small number of rosters; one contest showed that a depot's walking time between bus and canteen had been left out, and the rule was changed for every depot. Approval times stayed above the agreed floor, and the quarterly register review retired a forecasting feature nobody used. What was missing. Little: the operator's weakness was the supplier's release cycle, which it met with a contract clause. The rule. The controls work when they exist before the pressure: profile, hard constraints, seeds and a contest path agreed before the first roster.

The anti-patterns

Twelve anti-patterns recur across the cases. Each is described by its symptom, by why it looks like success from inside, by the function and degree it concerns, and by its remedy in this book. Each is a condition that accumulates unseen until a person pays for it, and that person is rarely the one who created it. Elish called this the moral crumple zone: the human nearest to an automated system absorbs the blame for its failures while having had little control over them (170, Elish, 2019). In algorithmic management the worker absorbs the consequence and the manager the blame, while the condition sits in the design.

AP-01 The delegation nobody recorded. Symptom: a system's output shapes a management decision and there is no delegation profile for it. Looks like success because the feature arrived with the licence and people find it useful. Concerns any function, usually Mode 0 Ambient AI and Mode 2 Embedded workplace assistants, at an unknown degree. Remedy: the delegation register and feature register (Chapters 10 and 25).

AP-02 The rubber-stamp approval. Symptom: median approval times fall while volume rises, departures approach zero and seeded faults pass. Looks like success because the queue is short and the system is usually right. Concerns M1 to M3 at a recorded Level 1 or Level 2, observed nearer Level 3 or Level 4. Remedy: the oversight record, seeds, time floors and capacity (Chapter 26), building on F21 and F76.

AP-03 The throughput benchmark for people. Symptom: per-person throughput, or an agent's output, becomes the norm for evaluation or a weight in allocation. Looks like success because output rises in the first quarter. Concerns M2 Evaluation and M1 Direction, especially Mode 5 Autonomous production agents that allocate work. Remedy: the forbidden comparison in the team charter, and throughput listed as an excluded input to M3 in the delegation profile (Chapters 9 and 18).

AP-04 The appeal to nobody. Symptom: contests go to the person who approved the decision, or to a mailbox, with no deadline and no route to the rule. Looks like success because few contests arrive. Concerns M3 Consequence and every adverse M2 measure. Remedy: the contestation record and appeal path (Chapter 27).

AP-05 The vendor release that changed behaviour unannounced. Symptom: a release alters who gets work, what is shown per person or how a score is built, and nobody classified the change. Looks like success because the release notes promise improvement. Microsoft's withdrawal of per-user names from Productivity Score in 2020 shows how much turns on such a configuration (189, Spataro, 2020). Concerns any function in Mode 0 and Mode 4 Built AI applications. Remedy: change classes, notices, procurement requirements and the freeze after a reversibility test (Chapters 23, 24, 25 and 28).

AP-06 The works council informed after the fact. Symptom: the first information to representatives is dated after configuration or go-live. Looks like success because the project met its date. Concerns every function; in Germany BetrVG §87(1) no. 6 gives co-determination on technical equipment suited to monitor behaviour or performance (9, Germany, 2001), and AI Act Article 26(7) requires information before a high-risk system is put into use at the workplace (1, European Union, 2024). Remedy: the co-determination gates and the worker information notice (Chapters 21, 23 and 28).

AP-07 The rest rule as a soft constraint. Symptom: rest, travel-time or break rules exist as flags a planner can close in bulk. Looks like success because every plan is compliant on paper. Concerns M5 Care and M1 Direction. Remedy: hard constraints written as tests in the risk assessment, and approval blocked until every flag is resolved (Chapters 14, 24 and 26).

AP-08 The manager measured against the system. Symptom: a manager's override or departure rate appears in their own appraisal, and overrides fall. Looks like success because adherence looks like discipline. Concerns M2 Evaluation of managers, and through it every function they oversee. Remedy: oversight figures reported at team level and excluded from managers' evaluation (Chapters 25, 26 and 29).

AP-09 The junior work that vanished. Symptom: deputies cannot plan, allocate or judge without the system, and a reversibility test fails or runs slowly. Looks like success because the junior tasks were the dull ones. Concerns M4 Development, and the fallback for M1 Direction, building on F69 and F77. Remedy: reserved learning tasks, hand-planned periods and competence records (Chapters 18 and 19), tested by the reversibility test (Chapter 25).

AP-10 Level 4 by accident. Symptom: the degree observed is higher than the degree recorded, or a consequence has no named human decider, or an approval was given by another automated component. Looks like success because nothing has gone wrong yet. Concerns M3 Consequence most of all, where Level 4 is never used, and agent chains in M1 Direction. Remedy: recording the degree as observed (Chapter 4), the decision-rights file and stop path (Chapter 20) and the chain record (Chapter 17).

AP-11 The explanation nobody can read. Symptom: a request for reasons cannot be answered in time, the supplier cannot show how a ranking is made, or the manager cannot say why. Looks like success because the model performs well on the supplier's metrics. Concerns M2 Evaluation and M3 Consequence; AI Act Article 86 gives a right to an explanation of decisions based on the output of a high-risk system (1, European Union, 2024). Remedy: the Management Decision Record with reasons, explanation by design, and pause when no explanation can be given (Chapters 7, 25 and 27).

AP-12 The register that is never reviewed. Symptom: profiles carry old versions, retired systems remain, and the last review date is older than the cadence. Looks like success because the register exists and passed the audit. Concerns every function. Remedy: the quarterly review with the representatives (Chapters 25 and 30) and the delegation register of AI Threat, Chapter 26 (44, Nordqvist, 2026).

The anti-patterns feed each other. A delegation nobody recorded (AP-01) cannot be reviewed (AP-12); a rubber-stamp approval (AP-02) is how Level 4 by accident (AP-10) happens in practice; a manager measured against the system (AP-08) stops departing, which produces the rubber stamp. The throughput benchmark (AP-03) and the junior work that vanished (AP-09) are the same pressure seen in the present and in five years.

Across the degrees of delegation

Degree as observed Anti-patterns that appear first What to check
Unrecorded, Mode 0 features AP-01, AP-05, AP-12 Features in use with no register entry
Level 0 Advisory, Level 1 Assisted AP-02, AP-08, AP-11 Departure rate, time per decision, reasons recorded
Level 2 Supervised AP-02, AP-07 Seeded faults caught, flags resolved before approval
Level 3 Bounded AP-03, AP-05, AP-09 Allocation weights, release changes, fallback skill
Level 4 Autonomous AP-10, AP-04 Any M3 Consequence without a named decider; contest routes
Across all degrees AP-06 Date representatives were informed against go-live

The grouping by usage mode follows: Mode 0 Ambient AI produces unrecorded delegations, Mode 4 Built AI applications produce advisory outputs that become decisive, and Mode 5 Autonomous production agents produce allocations and outputs that become the measure of people. That the last group will grow as office suites ship agents by default is expected, an inference from the trend Chapter 16 describes, not a measured rate.

Reviewing against the checklist

Practical guide: Reviewing an organisation's AI boss against the anti-pattern checklist

Purpose. Establish from records, not opinion, which anti-patterns an organisation shows, and turn each finding into a remedy with an owner and a date.

Roles. The HR director or chief HR officer owns the review. The owner of each delegation profile supplies records. A workers' representative, from the works council or the union, and the data protection officer act as independent readers. Line managers answer for their own units; the people affected are heard through the contest records.

Steps.

  1. Fix the scope: every entry in the delegation register and every feature in the feature register, over the last two quarters.
  2. Add what the register lacks. Ask each line manager which system outputs they used in a management decision; any not in the register is AP-01.
  3. Answer each signal in the checklist below from records: oversight records, MDRs, contestation records, release notes and the dates of information to representatives.
  4. Mark each anti-pattern red if a signal meets its condition or cannot be answered from a record, amber for a near-miss already remedied, green otherwise.
  5. Have the independent readers re-derive three answers from the raw records, at least one red and one green.
  6. Record each red finding and its remedy in an AI Decision Record, with an owner who is not the subject of the finding.
  7. Report at team level. No individual manager's or worker's figure leaves the review, and no finding becomes an input to M2 Evaluation or M3 Consequence of a person.
  8. Re-run quarterly with the register review. Default: findings closed only when the re-test passes.

Definition of done. Every anti-pattern has a colour derived from a record; every red one has a decision record, an owner, a remedy and a re-test date; the representatives have read the result; and the next review is scheduled. Adapt the thresholds to the collective agreement or works agreement.

Worked example: The anti-pattern checklist as code

Illustrative example. The checklist is kept beside the delegation register so that each review uses the same signals. Thresholds follow the defaults of Chapters 22 and 26.

# management/anti-pattern-checklist.yaml (illustrative)
checklist: anti-pattern checklist
version: 1.0.0
review: {cadence: quarterly, with: [works council or unions, data protection officer]}
reporting: {level: team, individual_figures: none, excluded_uses: [M2, M3]}
anti_patterns:
  - {id: AP-01, name: delegation nobody recorded, functions: [M1, M2, M3, M4, M5],
     signal: system output used in a decision with no DLG entry, red_if: any, remedy: Ch 10 and 25}
  - {id: AP-02, name: rubber-stamp approval, functions: [M1, M2, M3],
     signal: median time, departures, seeded catch,
     red_if: "catch < 0.85 or departures < 0.02 or adverse M3 median < 2 min", remedy: Ch 26}
  - {id: AP-03, name: throughput benchmark for people, functions: [M1, M2],
     signal: throughput as evaluation norm or allocation weight, red_if: any, remedy: Ch 9 and 18}
  - {id: AP-04, name: appeal to nobody, functions: [M3],
     signal: contest routed to approver or no time limit, red_if: any, remedy: Ch 27}
  - {id: AP-05, name: vendor release changed behaviour, functions: [M1, M2, M3],
     signal: release without change class, red_if: any, remedy: Ch 23, 24 and 28}
  - {id: AP-06, name: representatives informed after the fact, functions: [M1, M2, M3, M4, M5],
     signal: information date after go-live, red_if: any, remedy: Ch 21 and 23}
  - {id: AP-07, name: rest rule as soft constraint, functions: [M5, M1],
     signal: care rule closable as a flag, red_if: any, remedy: Ch 14 and 26}
  - {id: AP-08, name: manager measured against the system, functions: [M2],
     signal: override rate in manager appraisal, red_if: any, remedy: Ch 25 and 29}
  - {id: AP-09, name: junior work vanished, functions: [M4, M1],
     signal: deputy time in reversibility test, red_if: "> 2x team leader", remedy: Ch 19 and 25}
  - {id: AP-10, name: Level 4 by accident, functions: [M3, M1],
     signal: observed degree above recorded, red_if: "any M3 without named decider", remedy: Ch 4, 17 and 20}
  - {id: AP-11, name: explanation nobody can read, functions: [M2, M3],
     signal: reasons request unanswered in time limit, red_if: any, remedy: Ch 7 and 27}
  - {id: AP-12, name: register never reviewed, functions: [M1, M2, M3, M4, M5],
     signal: days since last review, red_if: "> 92", remedy: Ch 25 and 30}

The checklist holds signals, not opinions, so a finding cannot be argued away in a meeting. It names the chapter that holds each remedy instead of repeating it, and its reporting block writes down the condition every representative will ask for first: the review measures the design, never a person.

Nordvik Region and Hansa Mutual

Both organisations ran the checklist against their registers at the end of the year Chapter 30 describes.

Nordvik Region met three anti-patterns and remedied each during the year. It met the rubber-stamp approval when unit managers approved four-week rosters in under two minutes, which AIDR-0630 answered by blocking approval until every flag is resolved and planning two hours per roster. It met the rest rule as a soft constraint in CNT-NR-0001 and in rest flags closable in bulk, answered by the hard travel-time constraint in WRA-NR-HC-01 rev 2. It met the appeal to nobody in the path that sent contests to the approver, settled by AIDR-0631. The delegation nobody recorded appeared as a near-miss in assistant summaries whose unconfirmed owners three staff treated as instructions. It avoided the silent release by keeping the supplier's reliability feature off by contract and freezing the shift-swap feature, and the junior work that vanished by having deputies plan a week a month by hand. Its paused screening pilot, AIDR-0601, stands as the right response to an explanation nobody could read.

Hansa Mutual met the throughput benchmark twice, in the four-week throughput factor that AIDR-0651 removed and in the fewest-open-claims rule capped under AIDR-0675; its team charter prevented the benchmark-agent pattern of Composite A. It met the rubber-stamp approval in Polish recruitment, with rejections in a median of 38 seconds, remedied under AIDR-0680, and Level 4 by accident in its quiet form, a silent pay input, in the unit where CNT-HM-0001 showed the advisory sales score to be decisive. The reversibility test RVT-HM-CTA-2027-01, in which the deputy needed three times as long, was the junior work that vanished caught early. It avoided the works council informed after the fact: Thomas Brandt received AIDR-0651 before the board accepted it, and he has asked for the checklist to be annexed to KBV-IT-2024.

Summary

The eight documented cases share one movement: a system built for M1 Direction or M2 Evaluation came to carry M3 Consequence without anyone deciding that it should, and the record, the decision right or the contest path that would have shown it was missing. The Amsterdam judgment and the Dutch authority's fine turn on evidence of what the human did; the Italian decisions on scores that ordered access to work; the warehouse cases on indicators without a written purpose; Flex and Xsolla on lists drawn from scores; the hiring cases on outcomes nobody tested; SyRI, childcare benefits and Horizon on institutions that trusted the system over the person; and the agent incidents on boundaries that lived in instructions.

The composites show what the public record does not yet hold: an agent's throughput becoming the norm for people, a vendor release that quietly changed who gets work, and a transit operator whose controls worked because they existed before the first roster. M4 Development and M5 Care appear as the slow harms: the junior work that vanished and the rest rule as a soft constraint.

The twelve anti-patterns are latent conditions with measurable signals, and the checklist turns them into a quarterly review that representatives can read and that never measures a person. Their recurrence as office agents spread is expected. Nordvik Region met three and remedied them; Hansa Mutual met four and was protected from others by its works council and its team charter. Chapter 32 draws the two organisations' conclusions from the year and the checklist.

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