Acceptance Test Generation

Challenges Acceptance Test Generation addresses

Done by hand, sales engineering means gathering agreed requirements and test settings, working through 3 separate passes over the same material, then producing requirement coverage, test cases and test plan note. None of it is difficult and all of it is exacting, which is the combination people are worst at holding. The errors that matter are the ones a tired reader does not notice, and they surface later — in a reconciliation, or in somebody’s reply. It waits until someone remembers it, which is usually the point at which it has become urgent. As volume grows the work does not get harder, only longer, and the first thing to go is the checking.

Acceptance Test Generation runs that same sequence end to end and returns the result as structured artefacts. What it cannot settle it hands over rather than guesses at, and your correction is kept: it asks “Would the customer sign off against these?” after every run, and those answers become the set it is measured against. Nothing that moves money, alters a contract or reaches a customer executes without human approval, and every action is written to an audit log. The gain is in the volume that no longer has to be read, not in removing the judgement.

How it works

Step 1: Reading the requirements

First of 3. It works from agreed requirements and test settings and feeds the step after it.

Key Tasks:

• Locating the material: It works from agreed requirements and test settings, so nothing has to be forwarded, re-keyed or renamed first.
• Handling the format it arrives in: Scanned pages, native documents, spreadsheets and message bodies are all read the same way, including layouts where the relevant figure sits inside a table rather than a labelled field.
• Pulling the fields that matter: Only the fields the rest of the run needs are extracted. What cannot be read confidently is recorded as unread rather than filled in with a best guess.

Outcome:

• Fields extracted: The fields are available to the steps that follow, with anything unreadable listed rather than silently defaulted — which is what stops a bad extraction becoming a confident wrong answer three steps later.

Step 2: Writing the test cases

Step 2 of 3. It takes what step 1 produced and hands its result to step 3.

Key Tasks:

• Running the checks in order: Every rule for sales engineering is applied to every record, in the same order each run. A record is not skipped because it looks routine.
• Recording evidence, not verdicts: Each check stores what was expected and what was found, so a failure can be understood without re-running anything.
• Separating clear from unclear: A check the agent cannot settle is marked unresolved rather than passed, which keeps "checked" meaning checked.

Outcome:

• All checks pass: The record clears with its evidence attached, available if anyone asks later.
• A check fails: The record is held with the failing checks named and the rest shown as passed, so a reviewer sees the scope of the problem rather than only that there is one.

Step 3: Writing the test plan note

Last of 3. It takes what step 2 produced and produces requirement coverage and test cases.

Key Tasks:

• Testing the test plan note: The rules applied here are the ones that govern the test plan note, rather than a general validity check that would pass anything well-formed.
• Failing loudly, not quietly: A rule that cannot be evaluated is reported as unevaluated. A check that silently passes when it could not run is worse than no check.
• Running the checks in order: Every rule for sales engineering is applied to every record, in the same order each run. A record is not skipped because it looks routine.

Outcome:

• Within policy: The item satisfies every rule that governs it and continues without review.
• Outside policy: The failing rules are named alongside the ones that passed, so a reviewer sees the scope of the problem rather than only that there is one.

Step 4: Your review, and what it changes

The run ends with a person, not with a result being filed.

Key Tasks:

• Asking a specific question: It asks “Would the customer sign off against these?” rather than for a rating. A question about this run is answerable; a score out of five is not.
• Keeping the correction: What you change is recorded against the case that produced it, so the disagreement is retrievable rather than absorbed.
• Building the evaluation set: Those cases become what the agent is measured on. It is scored against your judgement rather than against a general benchmark.

Outcome:

• A measured agent, not an assumed one: The cases Acceptance Test Generation handles well and the cases it does not are both visible, and the second list is the one that decides what changes. Nothing is retrained silently on the back of a single correction.

Why use Acceptance Test Generation?

• Scored, with the working shown: Scores arrive with their component criteria rather than as a single number, so you can disagree with a criterion instead of only with the total — and two items with the same profile score the same on every run.
• A batch is one run, not a hundred: It works the whole set in a single pass and returns a row per item with its verdict, so the volume that needs no attention never has to be opened.
• Takes documents as they arrive: Scanned pages, native files and awkward layouts are read as they are. Nothing has to be renamed, re-keyed or converted into a template before a run.
• Corrected by the people using it: After each run it asks “Would the customer sign off against these?”. Those answers become the evaluation set, which means it is measured against your judgement rather than ours.
• Reads and reports, does not act: It returns a result for review rather than writing changes back on its own. Anything that moves money, alters a contract or reaches a customer needs human approval first.