Automation should make evidence easier to inspect, not make uncertainty disappear.
Define the report contract
Specify the question set, date range, product surfaces, markets, measures, and intended reader. Document the definitions and exclusions. The workflow should reproduce those choices rather than silently substituting defaults when information is missing.
Use only documented capabilities available in the systems you have selected. An MCP connection or API is an interface, not a guarantee that every desired dataset or action exists. Verify the actual response shape before building the report around it.
Retain provenance
Store identifiers and timestamps that connect a summary back to collected answers and cited URLs. Include collection failures and incomplete fields. Keep the original data accessible to authorized reviewers under your retention policy.
If an assistant writes the summary, require it to distinguish observed results from hypotheses. Check material claims against the source data. Generated prose must not invent a reason for a movement merely because the chart changed.
Keep access and action separate
Give the workflow the permissions needed for its defined task. A reporting job usually does not need permission to publish content or message clients. Review credentials, scope, and retention through your normal security process.
Add a human review point before consequential outbound actions. A factual-error flag can create a draft correction task; it should not automatically contact a publisher or change commercial terms without an approved process.
Test failure modes
Test an empty result, a partial collection, a changed field, a duplicate run, and an unavailable source. The report should fail visibly or clearly label the gap. A confident weekly summary generated from missing data is worse than a reported failure.
Version the workflow and keep a small run log. Automation succeeds when it saves routine effort while preserving accountability for the conclusions and actions the team takes.
