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Agent example / Research & reporting

AI reporting automation with sources you can check

A reporting agent can gather approved inputs and prepare an update for review. The work begins with the reporting period, source systems, and metric definitions. If two systems disagree, the draft should flag the difference rather than quietly choose a number.

This is an illustrative implementation pattern, not a client result or a promise that AI will make the underlying data accurate.

Define the report before automating it

Choose one recurring report and the person who approves it. Identify each metric, its source, the reporting cutoff, and any exclusions. Decide which source governs a number when systems use different definitions.

For example, a record created this week and a payment received this week describe different events. An agent should not treat them as interchangeable simply because both appear in a weekly dashboard.

Give the agent a bounded preparation task

A proposed workflow could retrieve authorized exports, check which inputs have arrived, and assemble a consistent draft. Calculations should run in code or another controlled calculation layer using agreed definitions. The agent can help explain the result, but its prose is not the calculation record.

Keep source references, reporting dates, and missing-input flags beside the draft. The reviewer should be able to trace a statement back to the information that supports it.

Example: one source is late

Suppose a weekly update requires a CRM export and an approved finance report. The CRM export is current, but the finance report still covers the previous period.

The workflow should mark the finance input as stale and avoid presenting a complete current-period total. Depending on the agreed policy, it can prepare a clearly labeled partial draft or stop until the missing source arrives. A person decides whether the partial report is useful enough to share.

Review before publication

The reviewer resolves source conflicts, checks the narrative against the numbers, and approves distribution. The agent should not invent a reason for a change that the inputs do not explain.

An increase may be observable without its cause being known. Keep unsupported explanations out of the report or label them as questions for the responsible team.

Test more than a complete dataset

Test a missing file, a changed column name, an unexpected reporting period, a duplicate export, and a source that returns no rows. Define whether each condition permits a labeled partial draft or requires the workflow to stop.

Also test access boundaries: permission to read one team's report does not imply permission to read every team's records.

Measure the preparation and review work

Track preparation time, validation time, corrections, missing inputs, and whether the report is ready when needed. Count manual cleanup and review effort when comparing the new process with the old one.

Discuss the workflow

Discuss a reporting implementation, see implementation scope, or compare agents with ordinary automation.

Common questions

Does AI reconcile every disagreement automatically?

No. Some differences can be resolved by agreed rules; others require an owner to clarify definitions or fix the source. Unresolved discrepancies should remain visible.

Can the report be sent automatically?

Sending is a separate permission from preparing a draft. This example keeps publication with a reviewer. Any broader authority needs explicit scope and testing.

Is this a replacement for our reporting platform?

Not necessarily. A native scheduled report or a simpler data pipeline may be enough. Review what your current software supports before commissioning an agent.

Keep exploring

Next step

What would you put an agent to work on?

Tell us about the work, the tools involved, and the decisions that need a person.