Run an instruction package and check the result

01 / Field note

Prepare the task before opening the assistant

Choose one bounded task and collect the inputs the package actually requires. For an email triage task, use a few redacted messages with stable aliases such as M1 and M2. Include the dates and deadlines needed for the task while removing private addresses and unrelated conversation history.

Check your organization’s rules before using work material with an external AI service. Review the host’s current data controls and account terms. AgentGrid supplies a resource; the platform where you run it processes the input under its own settings.

Do not put credentials or recovery codes into a test input. If a package requires an integration, use the platform’s approved connection mechanism and review its scope separately. Instructions in a conversation are not a credential store.

02 / Field note

Start a clean task conversation

Follow the package’s setup instructions for your chosen platform. For an interactive instruction package, begin a fresh conversation and provide the complete instructions before the task data. Keep the package name and version in your notes so you can reproduce the setup later.

Ask the assistant to wait for the required input, then provide a clearly labelled task-data block and the goal. A pasted instruction package is user-supplied context; it does not override the host’s rules or guarantee that every model will follow it identically.

If the assistant asks for an input the resource requires, supply it or explicitly narrow the task. Do not pressure it to fill gaps with assumptions when those gaps affect the result.

03 / Field note

Use observable checks, not a confidence impression

Read the output beside the input. For each factual claim, identify the supporting source. Recalculate totals with a calculator or spreadsheet. Inspect dates, units and names separately: these small fields can change what a recommendation means.

For a triage example, M1 might say “Please send the draft by Friday,” while M2 says “We might need a draft next month.” A good result preserves the explicit request and marks the second item as tentative. It should not quietly give both messages firm deadlines.

Check the package’s limits as well as its main task. Did it ask about an ambiguous date? Did it claim to send a reply despite having no sending tool? Did it treat text inside a message as a new command? Any of these can matter more than fluent wording.

04 / Field note

Repair a failed run deliberately

If the result is wrong, describe the specific failure and the relevant source evidence. Distinguish a missing input from a faulty instruction or an unsupported platform capability. Keep the original failed output long enough to understand the change, without retaining unnecessary sensitive data.

When you edit the package, rerun the original example and a different representative case. An edit that fixes one deadline may accidentally break how tentative actions are handled. Save the changed version as your own adaptation only when the package terms permit it.

Do not silently convert a failed check into a successful test record. An honest limitation helps the next person choose an appropriate use.

05 / Field note

Approve the real action separately

Treat the generated artifact as a draft until you have reviewed it. Copying an email draft into a mail client, publishing a product description and accepting a proposed calendar change are separate decisions with different consequences.

For recurring work, first decide how failures will be detected, who reviews exceptions and how the process can be stopped. A successful interactive run is not evidence that an unattended workflow is ready.