Canonical Taxonomy Specification

The workflow never formally defines what the AI may decide, what requires validation, where AI authority ends, where human approval begins, and who structurally owns correctness decisions. This is the upstream architectural instability condition.

The AI expands its operational scope incrementally across workflow stages. Operators do not notice the expansion because outputs continue appearing plausible. Authority over correctness decisions is assumed rather than assigned. No explicit handoff criteria exist between AI generation and human verification.

• —No formal workflow authority design
• —Absence of execution boundary documentation
• —Implicit trust in AI operational scope

• —Authority Leakage (direct manifestation)
• —Weak Output Validation
• —Hidden Assumption Accumulation

• —Operators unsure who is responsible for output correctness
• —AI decisions passing through without formal verification
• —Increasing scope of AI-generated content without corresponding validation increase
• —Ambiguous handoff points between AI and human workflow stages

• —Define explicit authority boundaries at each workflow stage
• —Document what the AI may generate, modify, approve, or propagate
• —Assign structural ownership of correctness decisions to named roles
• —Establish formal handoff criteria between AI generation and human verification

• Investigation 001 — AI-Assisted Publishing Workflow Degradation

Responsibility for correctness gradually diffuses across human and AI layers until no explicit authority remains structurally accountable for validation, approval, or truth declaration. This is the downstream operational manifestation of Undefined Execution Boundaries.

Outputs are reviewed but not formally verified. Each workflow stage assumes verification occurred somewhere else. Corrections are made without identifying the structural cause. Review behaviour becomes routine rather than diagnostic. The workflow continues producing output while validation accountability dissolves.

• —Undefined Execution Boundaries (direct upstream cause)
• —Absent authority boundary documentation
• —Implicit validation assumptions

• —Weak Output Validation
• —Repeated Manual Correction Loops
• —Hidden Assumption Accumulation

• —Outputs reviewed but not formally verified against source material
• —Corrections made without investigating structural cause
• —Each stage assumes the previous stage validated the output
• —Growing verification effort without corresponding validation structure
• —Trust in outputs weakening despite continued production

• —Define explicit validation ownership at each workflow stage
• —Establish structural verification checkpoints with named criteria
• —Separate review (quality) from verification (structural accuracy)
• —Assign correctness accountability to specific workflow roles

• Investigation 001 — AI-Assisted Publishing Workflow Degradation

Workflows inherit decisions, constraints, and interpretations that were never explicitly declared or validated. Each stage builds on assumptions from the previous one. Over time, the workflow operates on a foundation that no one has examined.

Outputs appear locally correct while overall workflow coherence weakens. Operators cannot identify why the workflow behaves inconsistently. Each stage appears reasonable individually while the accumulated assumption layer creates structural instability.

• —Undefined Execution Boundaries
• —Absent documentation of operational decisions
• —Implicit parameter inheritance

• —Dependency Drift
• —Weak Output Validation
• —Authority Leakage

• —Operators unable to explain why certain workflow decisions were made
• —Inconsistent outputs that cannot be traced to a specific structural cause
• —Workflow behaviour diverging from documented design without explicit changes
• —Increasing difficulty diagnosing instability sources

• —Document all operational decisions and constraints explicitly
• —Introduce assumption audits at defined workflow intervals
• —Establish explicit parameter inheritance rules between workflow stages
• —Surface implicit constraints through structured workflow reconstruction

Outputs are accepted because they appear plausible, not because they passed structural verification. The absence of visible errors is treated as confirmation of correctness.

Review focuses on surface-level quality rather than structural accuracy. Outputs that look correct are approved without systematic validation against source material or operational requirements. Plausible-but-incorrect outputs propagate downstream.

• —Authority Leakage
• —Undefined Execution Boundaries
• —Absent validation checkpoints

• —Repeated Manual Correction Loops
• —Hidden Assumption Accumulation
• —Trust Degradation

• —Outputs approved based on appearance rather than structural verification
• —Errors discovered downstream after approval
• —Review time increasing without corresponding accuracy improvement
• —Operators checking the same output multiple times across stages

• —Introduce structural validation checkpoints with explicit criteria
• —Separate plausibility review from structural accuracy verification
• —Define what constitutes a verified output at each workflow stage
• —Anchor validation to source material rather than output appearance

• Investigation 001 — AI-Assisted Publishing Workflow Degradation

The same classes of errors are corrected repeatedly without addressing the structural cause generating them. Correction becomes routine. The workflow continues functioning only because operators absorb the instability manually.

Operators develop correction workflows for recurring error types. The corrections feel manageable. The structural cause is not investigated because the workflow continues producing output. Correction labour increases progressively while the underlying instability remains unaddressed.

• —Weak Output Validation
• —Authority Leakage
• —Undefined Execution Boundaries

• —Human Fatigue Blindness
• —Human Compensation Dependence
• —Operational Fatigue

• —The same correction types appearing across multiple workflow iterations
• —Operators describing corrections as 'routine' or 'expected'
• —Correction time increasing without structural investigation
• —New team members surprised by the correction volume

• —Classify recurring corrections by structural cause rather than output type
• —Address correction causes at the workflow architecture level
• —Measure correction frequency as a structural signal, not operational overhead
• —Reduce hidden stabilisation labour through upstream structural fixes

• Investigation 001 — AI-Assisted Publishing Workflow Degradation

Correction behaviour becomes so routine that operators stop recognising instability as a structural problem. The workflow appears stable because humans have normalised the effort required to keep it functioning.

Operators describe the workflow as 'working well' despite high correction frequency. The hidden stabilisation labour is not recognised as a structural cost. Instability is attributed to individual output variation rather than workflow architecture.

• —Repeated Manual Correction Loops
• —Absent correction analysis
• —Normalised instability adaptation

• —Permanent human compensation dependence
• —Structural instability remaining unaddressed
• —Operational fatigue

• —Operators unaware of the total correction time across the workflow
• —Instability described as 'just how AI works'
• —Structural problems attributed to individual AI output variation
• —New operators immediately identifying problems that existing operators no longer notice

• —Measure and surface hidden stabilisation labour explicitly
• —Introduce external diagnostic review to identify normalised instability
• —Establish correction frequency thresholds that trigger structural investigation
• —Separate operator adaptation from structural workflow reliability

• Investigation 001 — AI-Assisted Publishing Workflow Degradation

One workflow stage changes subtly. Downstream stages continue inheriting those changes until the workflow no longer behaves the way operators believe it does. The divergence accumulates silently across iterations.

Outputs gradually drift from established operational parameters. Operators notice inconsistency but attribute it to AI variation rather than structural drift. The workflow continues producing output while its behaviour diverges from the original design.

• —Absent continuity anchoring
• —Undefined execution boundaries
• —Implicit operational parameters

• —Fragmented Context Between Sessions
• —Hidden Assumption Accumulation
• —Trust Degradation

• —Outputs behaving differently across workflow iterations without explicit changes
• —Operators re-establishing parameters that were previously stable
• —Inconsistency attributed to AI variation rather than workflow drift
• —Workflow behaviour diverging from documented operational design

• —Anchor workflow parameters explicitly at each stage
• —Establish continuity checkpoints that verify workflow behaviour against baseline
• —Document operational parameters as structural constraints rather than implicit expectations
• —Introduce drift detection through regular baseline comparison

Operational continuity becomes distributed across disconnected interactions. Each new session begins without the structural context established in previous ones, forcing humans to manually reconstruct what the system no longer retains.

Instructions are repeated across sessions. Outputs drift from established operational parameters. Operators spend increasing time re-establishing context before productive work can begin. Context reconstruction becomes a hidden operational cost.

• —Absent session continuity mechanisms
• —Dependency Drift
• —No structural context anchoring

• —Hidden stabilisation labour
• —Dependency Drift acceleration
• —Operator fatigue

• —Operators re-explaining the same context at the start of each session
• —Outputs drifting from parameters established in previous sessions
• —Session startup time increasing as context reconstruction grows
• —Operators maintaining external context documents to compensate

• —Establish structural session continuity mechanisms
• —Document operational context as persistent structural parameters
• —Introduce session initialisation protocols that restore established context
• —Measure context reconstruction time as a structural cost indicator

• Investigation 001 — AI-Assisted Publishing Workflow Degradation