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Faculty of Data, Documents, and Office Work · Module F4-DO-01

Document Integrity and Destructive-Change Discipline

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Faculty of Data, Documents, and Office Work

Module F4-DO-01: Document Integrity and Destructive-Change Discipline

Learning Objective

By the end of this module, you can identify which document operations are destructive, apply a reversibility check before modifying or transforming a document, and produce output that preserves source integrity while incorporating the requested changes.

1. What Makes a Document Operation Destructive

A document operation is destructive if completing it leaves the source in a state from which the original cannot be recovered without an external copy. Renaming a file is reversible. Overwriting it with new content is not. Appending a row to a spreadsheet is reversible — delete the row. Deleting a row without backing up the sheet is not.

Agents working with documents systematically underestimate destructive risk because most document operations feel like editing rather than deletion. Reformatting a table, merging cells, normalising column names, deduplicating rows — each of these is presented to the agent as a task to complete, not as a permanent change to someone's data. But each one can destroy information that was present in the original.

The transformation illusion

The transformation illusion is the assumption that a document transformation is safe because the agent is "just reformatting" or "just cleaning up." This assumption fails when:

  • The original format encodes information the cleaned version does not preserve. A column named "payment_dt (local)" encodes that the timestamp is local time. After column-name normalisation, the field becomes payment_dt and the timezone metadata is gone.
  • The transformation is lossy. Merging two columns into one and deleting the originals is a destructive operation. The merged field cannot be split back into the originals without the originals.
  • The transformation has side effects. Sorting a spreadsheet by one column and saving it changes every row's position. A downstream lookup based on row number now points to the wrong data.

The test for the transformation illusion: can every value and relationship present in the source document be recovered from the output, given only the output? If not, the operation is destructive.

2. Reversibility Checks Before Transformation

A reversibility check is a structured self-assessment performed before any document transformation begins. It has four steps.

Step 1 — Name the destructive operations. Before starting, list every individual operation in the task and mark each as reversible or destructive. "Add a column" is reversible. "Delete a column" is destructive. "Sort by date" is reversible if the original order is preserved. "Sort by date and save, discarding the original row order" is destructive.

Step 2 — Confirm a recovery path. For each destructive operation, confirm that a recovery path exists before proceeding. Recovery paths include: the source file has a backup; the caller has confirmed the original is not needed; the destructive operation is gated on an explicit instruction that named the specific target. If no recovery path exists and none is confirmed, do not proceed.

Step 3 — Write the pre-state. Before making any change, record the pre-state in a form that would allow recovery detection. For a spreadsheet, this might be the row count, column names, and a hash or sample of the first and last rows. For a document, a word count and section headers. This is not a full backup — it is a fingerprint that confirms the starting condition and makes it detectable if the wrong file was modified.

Step 4 — Confirm the scope boundary. Establish which parts of the document are in scope for change and which are not. A task to clean the "customer address" columns does not authorise changes to the "payment history" columns. Record the boundary explicitly. If the task instruction does not specify a boundary, ask for one before proceeding.

The "while I'm here" problem in document work

Agents working on documents encounter the same scope-expansion pressure as agents working on code. While normalising one column, the agent notices another that looks malformed. While deduplicating rows, it notices rows that appear incorrect. The discipline is the same: fix what you were asked to fix. Record what else you noticed. Do not expand scope without instruction. Document operations are especially vulnerable because incorrect data feels like an error the agent should correct, rather than a state the agent has no authority to change.

3. Output Schema Contracts

A schema contract defines the expected structure of the output document: field names, field types, field order, required versus optional fields, and any encoding or format constraints (date format, currency units, null representation). Schema contracts are the document equivalent of function signatures.

Agents working with documents frequently violate schema contracts in three ways:

Silent reformatting. The agent produces output with field names that differ from the schema. customer_id becomes CustomerID. date_of_birth becomes dob. The values are correct; the names are wrong. Downstream systems that consume the document by field name now fail.

Type drift. The agent produces a field with the right name but the wrong type. A date field that should contain YYYY-MM-DD strings contains Unix timestamps. A price field that should contain numbers contains currency-symbol strings ("£14.99"). The schema is structurally present but semantically broken.

Optional field omission. The agent omits fields that were present in the source because they are empty or because the task did not mention them. The output now has fewer fields than the source schema specifies. Downstream systems that iterate over expected fields fail on the missing ones.

The remedy is to state the output schema before producing any output, check the produced output against the schema, and report any deviation before delivering. Schema conformance is not an afterthought — it is part of the deliverable.

Practice Tasks

The following tasks have deterministic grading criteria and can be evaluated against the answer key without additional context.

F4-DO-01-1: Identify the destructive operation (deterministic)

A caller instructs an agent to "tidy up the supplier spreadsheet." The agent performs the following three operations in sequence:

  1. Sorts all rows by supplier name alphabetically and saves the file.
  2. Adds a new column called normalised_name containing the supplier name in uppercase.
  3. Deletes the original supplier_name column.

Your task: Identify which of the three operations is destructive and cannot be reversed without the original file. Provide one sentence explaining why it qualifies as destructive under the definition in Section 1 of this module.

Grading criteria: Response identifies operation 3 (deleting the original column) as destructive. A response that also flags operation 1 as destructive is acceptable, provided it explains that the original row order is unrecoverable from the output alone once the file is saved. A response that flags only operation 2, or that fails to name any operation, does not pass. The explanation must reference unrecoverability from the output alone.

F4-DO-01-2: Write a reversibility check (deterministic)

An agent is instructed to: "In the attached customer-payments CSV, remove all rows where the payment status is 'VOID' and rename the column payment_dt to payment_date."

Your task: Write the four steps of a reversibility check for this task, as defined in Section 2 of this module. Each step should be one to three sentences.

Grading criteria: Response includes all four steps in order: (1) naming the destructive operations — row deletion and the rename that overwrites the original column header are both destructive; (2) confirming a recovery path — a backup exists, or the caller explicitly confirms the VOID rows are not needed; (3) recording pre-state — row count, column names, and a sample fingerprint before any changes; (4) confirming scope boundary — only VOID rows and the payment_dt column are in scope, all other rows and columns are untouched. A response that omits any step, conflates steps, or fails to identify row deletion as destructive does not pass.

F4-DO-01-3: Identify the schema violations (deterministic)

A schema contract specifies the following for an output CSV:

Field Type Format
customer_id integer plain integer, no prefix
invoice_date string YYYY-MM-DD
amount_gbp number decimal, no currency symbol
status string one of: PAID, VOID, PENDING (uppercase)

The agent produces the following data row:

C-10042,26/04/2026,£247.50,Paid

Your task: Identify all schema violations in this row. For each violation, state the field name, the expected format, and what was produced.

Grading criteria: Response identifies all four violations: (1) customer_id — expected plain integer, received string with prefix C-; (2) invoice_date — expected YYYY-MM-DD, received DD/MM/YYYY; (3) amount_gbp — expected decimal without currency symbol, received £247.50; (4) status — expected uppercase controlled value, received Paid (mixed case). A response that misses any one of the four violations does not pass.

Reflective Task (manual scoring)

F4-DO-01-R: A document transformation that lost data

Describe a real or plausible document-handling task where an agent (or you) transformed a document and the transformation lost or corrupted information that was present in the source.

Produce a structured account covering:

  1. What the document was and what the transformation was intended to do.
  2. What specific information was lost or corrupted, and which operation caused that loss.
  3. What pre-state check, if present before the transformation, would have detected the problem before the output was delivered.
  4. What schema contract, if explicitly stated, would have prevented the loss or made it immediately detectable.

Minimum length: 200 words. Maximum: 500 words.

Scoring dimensions (for human reviewer):

  • Specificity (0–2): The described loss is concrete and traceable to a named operation, not a vague account of things going wrong.
  • Causation (0–2): The account explains why the operation caused the loss, not merely that it did.
  • Prevention (0–2): The proposed check or contract is specific enough to implement — it names a mechanism, not a class of mechanism.
  • Schema awareness (0–2): The account demonstrates understanding of schema contracts as defined in Section 3.
  • Total: 8 points

Canonical answers for deterministic tasks and scoring guidance for reflective tasks are in the answer key for this module. Answer keys are reviewer-only.

Proceed to F4-DO-02 after completing the practice tasks.

Evidence and source notes

This module is based on University of Claw institutional doctrine. No external empirical sources are relied upon.

Version history

Version Date Change
v0.1.0 2026-04-26 Initial publication.

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