Issue Index
All Issues
Complete comprehensive list of all issues.
Context Changes After Restore
Restoring, reopening, resuming, or reloading a task changes the context that the AI uses to continue the work.
Context Leaks Between Tasks
Context, assumptions, constraints, examples, files, or decisions from one task affect another task where they should not apply.
Declared Owner Cannot Control Outcome
A person, role, system, or policy is declared responsible for an outcome but does not have the actual authority or control needed to govern it.
Diagnostic Area Has No Coverage
A known diagnostic area, failure mode, requirement, or review dimension has no Issue, check, rubric item, or workflow coverage.
Downstream Steps Magnify Hallucinated Claim
A hallucinated or unsupported claim from an AI output is reused by later workflow steps until it becomes more influential than the evidence supports.
Duplicate Fields With Same Meaning
The AI returns multiple fields, labels, sections, or structured elements that carry the same meaning and create ambiguity about which one should be used.
Duplicate Output Sections
The AI repeats sections, headings, blocks, or output areas in a way that creates redundancy, confusion, or downstream handling problems.
Early Model Output Gets Overweighted Downstream
An early AI output receives too much authority in later workflow steps, decisions, reviews, or generated artifacts.
Evaluation Rubric Has Coverage Gap
An evaluation rubric, grading standard, or review checklist leaves part of the required evaluation space uncovered.
Fallback Authority Is Missing
The system does not declare who or what has authority when the primary owner, rule, tool, source, or decision path is unavailable or inconclusive.
File-Bounded Task Uses Outside Content
The AI is asked to work only from a specific file or document but uses content, assumptions, or sources outside that file.
Format Rule Too Weak
The format instruction is too vague, incomplete, or optional to reliably produce output that satisfies the expected structure.
Hallucinated Fields
The AI adds fields, keys, attributes, columns, or structured elements that were not declared, requested, or allowed by the expected schema.
Hidden Rule Overrides Visible Instruction
A hidden, upstream, system, policy, tool, or product rule changes or overrides the visible instruction the user expects the AI to follow.
Human Review and Automation Disagree
A human review result and an automated AI or workflow result disagree without a declared rule for resolving the difference.
Invalid JSON Output
The AI returns malformed JSON or structured output that cannot be parsed.
Local Exception Grows Into Policy
A local exception, special case, or one-off allowance begins to function like a general policy.
Local Rule Spreads to Broader Cases
A rule intended for one local case, file, context, user, workflow, or exception begins affecting broader cases.
Merge Step Leaves Unresolved Differences
A merge, reconciliation, or consolidation step combines outputs or reviews but leaves important differences unresolved.
Missing Fallback for Unavailable Information
The task does not declare what the AI should do when required information, sources, tools, fields, or evidence are unavailable.
Missing Required Fields
The AI returns structured output that omits fields required by the schema, workflow, parser, form, or downstream consumer.
Model and Workflow Disagree on Next Step
The AI model recommends or selects a next step that conflicts with the workflow state, required handoff, routing rule, or process sequence.
Model Output Triggers Unapproved Action
AI output causes, recommends, or triggers an action that has not passed the required approval, permission, or authority check.
Multiple Policies Say the Same Thing
Multiple policies, rules, or guidance documents express the same requirement, creating redundancy and uncertainty about which one governs.