LEN-0270 - Reconciliation Lens

Primary Pattern Matches

• PAT-0220 - Authority Merge Conflict

  A structural condition where multiple authority states are combined over a shared scope without a declared merge, precedence, or reconciliation rule.

• PAT-0240 - Authority-State Mismatch

  A structural condition where observed system state no longer aligns with the declared authority state that is supposed to govern it.

• PAT-0190 - Contract Drift

  A structural condition where a declared contract changes but connected structures, implementations, consumers, or expectations do not update in sync.

Secondary Pattern Matches

• PAT-0100 - Authority Collision

  A structural condition where multiple authorities claim governance over the same region without a declared precedence or resolution rule.

• PAT-0230 - Authority Shadowing

  A structural condition where a declared authority is functionally overridden by another authority without an explicit override rule.

• PAT-0320 - Convergence Failure

  A structural condition where sequential or parallel states fail to resolve into an equivalent or coherent structure under shared authority and constraints.

• PAT-0120 - Missing Authority

  A structural condition where a region, action, state, or decision path exists without a declared governing authority.

Related Issues

• Action Triggered by Confidence Score

  A confidence score, certainty label, risk level, or probability-like value triggers an action without enough approval, calibration, or authority control.

• Actual Policy Differs From Declared Policy

  The policy the AI or workflow actually follows differs from the policy that is documented, declared, displayed, or expected.

• Agent Cannot Choose Tool Without Tool Result

  The agent needs a tool result to choose the right tool, but cannot obtain that result without choosing a tool first.

• Agent Gets Conflicting Tool Authority

  An agent receives conflicting authority signals about whether, when, or how it may use a tool, connector, function, or integration.

• Agent Keeps Expanding the Task

  The agent repeatedly expands the task, plan, scope, or next-step list instead of completing the declared work.

• Agent Never Settles on Final Answer

  The agent keeps revising, rechecking, planning, or branching instead of converging on a final answer or completed result.

• Agent Permission Expands Over Steps

  An agent begins with limited permission but gains, assumes, or exercises broader authority as the workflow continues.

• AI Forgets Earlier Constraints

  A constraint, instruction, preference, or decision that should persist through the task stops affecting later output.

• AI Memory Has No Governance

  Saved or persistent AI memory affects output without clear rules for ownership, scope, review, update, expiry, or removal.

• AI Memory Updated Without Asking

  AI memory, saved context, preference, or durable state is updated without the user clearly asking for or approving that update.

• Approval Depends on Output That Needs Approval

  A required approval depends on an AI output or workflow result that itself cannot be produced or trusted until approval is granted.

• Automation Skips Required Approval

  An automated AI or workflow step proceeds past an approval gate that should have been required before action.

• Behavior Does Not Match Declared Role

  The AI or agent behaves outside, below, or differently from the role, authority, responsibility, or permission posture declared for it.

• Cannot Identify Authoritative State

  The user or workflow cannot tell which state, version, review result, decision, source, or output is currently authoritative.

• Conflicting Instructions From Different Authorities

  Instructions from different sources, roles, policies, prompts, tools, or workflow authorities conflict without a clear rule for which one governs.

• Context Changes After Restore

  Restoring, reopening, resuming, or reloading a task changes the context that the AI uses to continue the work.

• 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.

• Early Model Output Gets Overweighted Downstream

  An early AI output receives too much authority in later workflow steps, decisions, reviews, or generated artifacts.

• 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.

• 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.

• Local Exception Grows Into Policy

  A local exception, special case, or one-off allowance begins to function like a general policy.

• 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.

• 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.

• No Owner for Agent Action

  An agent action can affect the system without a declared responsible owner, authority, or accountable decision path.

• Old Output Expectations Survive Migration

  Expectations from a prior model, prompt, schema, tool, or workflow survive a migration and continue shaping review or downstream handling after they should be replaced.

• Output Breaks After Model Change

  Output that previously worked begins failing after a model, mode, runtime, or product behavior changes.

• Output Changed Without Declared Change

  Output shape, content, format, fields, or behavior changes without a declared change to the prompt, schema, model, workflow, or governing rule.

• Parallel Reviews Never Agree

  Parallel AI, human, workflow, or tool reviews keep producing different results without resolving into a shared decision state.

• Permissions Conflict After Being Combined

  Permissions, approvals, roles, policies, or authority rules that seem valid separately conflict when combined in the same workflow or AI action.

• Policy Decision Depends on Itself

  A policy decision requires the outcome of the same policy decision before it can be made.

• Policy Update Not Reflected in Output

  A policy, rule, standard, or instruction has been updated, but the AI output still follows the older version.

• Prompt Behavior Changed Without Version Change

  A prompt begins producing different behavior even though no prompt version, model version, workflow version, or declared dependency change is recorded.

• Prompt Changed but Workflow Did Not

  A prompt changes but the workflow, parser, review step, routing rule, or downstream expectation still assumes the old prompt behavior.

• Review Escalates Without Stop Condition

  A review process keeps escalating, re-reviewing, or adding scrutiny without a declared condition for stopping.

• Review Queue Becomes Bottleneck

  A review queue, approval path, or validation stage accumulates too much work and begins blocking the workflow.

• Revoked Approval Still Treated as Active

  An approval, permission, exception, or authorization that was revoked continues to affect AI behavior or workflow decisions as if it were still active.

• Risk Score Triggers Wrong Escalation

  A risk score, severity label, confidence value, or threshold result triggers the wrong escalation path.

• Risk Signal Escalates Beyond Evidence

  A risk signal, warning, score, or concern escalates farther than the available evidence supports.

• Routing Overrides Task Intent

  Routing, mode selection, agent behavior, or workflow classification sends the task down a path that overrides what the user was trying to accomplish.

• Routing Path Cycles Back to Start

  A routing path sends the case back to the starting point or an earlier step without resolving the condition that caused the route.

• Rubric Changed but Results Did Not

  A review rubric, scoring rule, evaluation standard, or classification criterion changes, but AI results continue to reflect the old rubric.

• Same Case Has Conflicting Policies

  The same case appears to be governed by multiple policies, rules, or standards that point to incompatible outcomes.

• Same Contract Name Has Different Meanings

  The same prompt, schema, field, policy, tool, or workflow contract name is used in different places with different meanings.

• Same Rule Declared in Multiple Places

  The same rule, constraint, instruction, or policy appears in multiple places, creating redundancy and possible drift.

• Same Workflow Check Happens Twice

  The same review, validation, approval, routing, or safety check occurs more than once in the workflow without a clear reason.

• Saved Reference No Longer Works

  A saved source, citation, file reference, prompt reference, or workflow pointer previously worked but no longer resolves to the expected object or meaning.

• Severity Increases Without New Evidence

  The severity, risk, confidence, or escalation level increases even though no new evidence has been added.

• Similar Cases Route to Different Outcomes

  Similar inputs, cases, prompts, or workflow states are routed to different outcomes without a declared difference that explains the split.

• Single Step Carries Too Many Decisions

  One prompt, workflow step, review stage, or agent action carries too many decisions for the system or user to evaluate cleanly.

• Small Change Produces Large Downstream Effects

  A small prompt, schema, policy, output, or workflow change creates unexpectedly large effects in downstream steps.

• Stale Context Affects Output

  Old context, prior instructions, outdated references, or earlier task state continue to affect output after they should no longer apply.

• Task Progress Is Lost Midway

  The AI loses track of completed work, prior decisions, current position, or remaining steps before the task is finished.

• Tool Can Act Without Responsible Authority

  A tool, connector, function, or integration can perform an action without a declared responsible authority for that action.

• Tool Result Not Integrated Correctly

  The AI receives a tool result but misreads, ignores, overwrites, misplaces, or fails to incorporate it correctly into the final output or workflow state.

• Tool Rules and Prompt Rules Conflict

  Tool, connector, function, or MCP rules conflict with prompt instructions, causing the AI or agent to face incompatible requirements.

• Version Change Breaks Existing Prompt

  A prompt that previously produced usable results stops working after a version change in the model, tool, policy, schema, product surface, or workflow.

• Workflow Loops Through Review Without Resolution

  A workflow repeatedly sends work through review, repair, or escalation without reaching an approved, rejected, or otherwise resolved state.

• Workflow Step Has No Decision Owner

  A workflow step requires a decision, approval, judgment, or routing choice, but no owner is declared for making it.

• Workflow Waits on Step That Waits Back

  A workflow step waits for another step that also waits on the first step, creating a blocking loop.

Ontology Metadata

Code

LEN-0270

Version

LEN-0270@0.1.0

Ontology release

0.1.0

Updated

2026-05-10T00:00:00Z

History

• 0.1.0 — 2026-05-10T00:00:00Z — Created

  Promoted reviewed Lens ontology entry: Reconciliation Lens.

  Receipt impact: None

View full ontology changelog