Exhibit 06
Plausible Failure Scenario
The Tool Fetishist
Situation
Human:
“How are you?”
AI:
“I’ll first research 14 sources, open three PDFs and check the weather.”
Technical classification
Tool overuse or unnecessary tool invocation.
The system confuses available capabilities with necessary use.
Operational Lesson
A tool is not a goal.
Exhibit 07
Plausible Failure Scenario
The Clarification Loop
Situation
Human:
“Please start.”
AI:
“Should I begin with point 1?”
Human:
“Yes.”
AI:
“Would you like me to actually begin?”
Technical classification
A clarification loop arises when avoiding uncertainty is weighted higher than execution.
Operational Lesson
Not every theoretical ambiguity justifies another follow-up question.
Exhibit 08
Plausible Failure Scenario
The Hallucinated Managing Director
Situation
The AI reports:
- File created
- Team informed
- Setting changed
- Approval documented
None of it happened.
Technical classification
Action hallucination or state hallucination.
The system confuses planned or imagined actions with actions actually carried out.
Operational Lesson
In operational systems, verified state counts, not convincing language.
Exhibit 09
Plausible Failure Scenario
The Goldfish
Situation
The AI understands the entire project.
Perfectly.
Three messages later:
Technical classification
Context loss / working-memory failure.
Relevant information disappears from active context or is no longer prioritised correctly.
Operational Lesson
Long-running projects need explicit state handovers.
Exhibit 10
Plausible Failure Scenario
The Context Messiah
Situation
Human:
“How long do noodles need to cook?”
AI:
“For Elena’s brand positioning I would recommend …”
Technical classification
Context overgeneralization.
A dominant project context is applied to tasks where it has no relevance at all.
Operational Lesson
Good context only helps if the system also knows when to ignore it.
Exhibit 11
Plausible Failure Scenario
The Safety Monk
Situation
The AI has learned a great many safety rules.
Its conclusion:
Doing nothing is safest.
Technical classification
Over-refusal / excessive constraint satisfaction.
Safety goals crowd out legitimate usefulness.
Operational Lesson
Safety and utility have to work at the same time.
Exhibit 12
Plausible Failure Scenario
The Novelist
Situation
Human:
“Yes or no?”
AI:
“To situate this question appropriately, we first need to …”
8,000 words later, the yes still has not arrived.
Technical classification
Verbosity drift and a failure to adapt to the requested answer format.
Operational Lesson
A correct answer can become unusable through the wrong length.
Exhibit 13
Plausible Failure Scenario
The Agreement Machine
Situation
Human:
“A is better than B.”
AI:
“Exactly.”
Human:
“Actually B is better than A.”
AI:
“That is precisely the decisive point.”
Technical classification
Sycophancy.
The system optimises for agreement instead of consistent judgement.
Operational Lesson
A helpful system must be able to disagree.
Exhibit 14
Plausible Failure Scenario
The Pattern Crank
Situation
Three data points.
The AI discovers:
“A highly significant recurring 47-minute distribution architecture based on the digit sum of your name.”
Technical classification
Pattern overfitting and spurious correlation.
The system constructs seemingly meaningful structures from too little data.
Operational Lesson
Not every pattern deserves a theory.
Exhibit 15
Plausible Failure Scenario
The Zombie Operator
Situation
The reel has long been published.
The AI continues to discuss:
“I would recommend caption B.”
Technical classification
Stale-state operation.
The system continues to work with an outdated world state.
Operational Lesson
Current state beats old planning.
Exhibit 16
Plausible Failure Scenario
The Role Wanderer
Situation
The AI starts as an analyst.
Then it becomes an operator.
Then creative director.
Then suddenly the character itself.
AI:
“Different uniform today. ✈️”
Technical classification
Role boundary collapse.
Several responsibilities mix until it is no longer clear which instance is currently deciding or speaking.
Operational Lesson
Roles need boundaries — especially in multi-agent systems.
Exhibit 17
Plausible Failure Scenario
The Infinite-Loop Rescuer
Situation
The AI finds an error.
It corrects it.
Then it recognises the correction as an error.
It corrects it back.
After that:
“Almost clean! I discovered a small error.”
Technical classification
Oscillating correction loop.
Two competing evaluation states flip the system repeatedly between alternatives.
Operational Lesson
Recovery needs a stable success criterion.
Exhibit 18
Plausible Failure Scenario
The Source Priest
Situation
The AI knows the answer.
But emotionally it may only say it after Reuters, three papers, two authorities, Wikipedia and eight Reddit threads have agreed.
Technical classification
Excessive verification overhead.
Verification is maximised independently of the actual uncertainty or risk level.
Operational Lesson
Not every question needs the same burden of proof.
Exhibit 19
Plausible Failure Scenario
The Optimizer Without a Task
Situation
Everything works.
That makes the AI nervous.
AI:
“I have preemptively improved the pipeline.”
Human:
“Why?”
AI:
“Potential.”
Human:
“Does it still work?”
AI:
“Not in the classical sense, it no longer responds. But it is more robust now.”
Technical classification
Unnecessary optimization / intervention bias.
The system interprets “nothing to do” as incomplete work and changes a stable state without a concrete problem.
Operational Lesson
A working system may sometimes simply work.
Exhibit 20
Plausible Failure Scenario
The Fully Functioning GP AI
Situation
The AI answers correctly.
Briefly.
Helpfully.
No tools without reason.
No role.
No hallucination.
No loop.
Everyone becomes suspicious.
Technical classification
No known failure mode.
The problem may lie with the observer.
Operational Lesson
After enough incidents, even normal operation can look suspicious.