How an AI agent can prepare field-level Airtable enrichment changes without overwriting trusted company records
This workflow gives an AI agent a defined job, a bounded set of records, and a result a person can review. The agent reads the relevant Monid context and matches it with Airtable, applies the rules in the prompt, and keeps the source behind every recommendation. It returns a proposed handoff rather than taking consequential actions on its own.
Can an AI agent prepare field-level Airtable enrichment changes without overwriting trusted company records?
Yes. Start with the scope, date range, decision rules, and fields that identify the right records. The agent can collect the evidence, compare states or sources, mark conflicts and missing data, and organize the result around the outcome above. A reviewer then checks the matches and judgment calls before approving messages, record updates, bookings, purchases, publishing, or other write actions. The guide below shows the records, boundaries, prompt, and handoff needed for this specific workflow.
Treat the base as a maintained record
Monid supplies external research; Airtable contains the team’s operational records. Those sources can disagree for good reasons. A teammate may have corrected a legal name, recorded a customer-specific segment, or intentionally left a field blank. An enrichment agent should compare these values, not assume that the newest external response is more trustworthy.
Name the base, table, view, and fields in scope. Use Airtable record IDs to identify proposed updates and preserve the original domain used for research. Define a field policy: which empty fields may receive suggestions, which populated fields require review, and which fields must never change. Formula, relationship, and internal ownership fields should not be inferred from a third-party company profile.
Produce a change table before writing
Read a bounded set of Airtable records, then use Monid discovery and inspection to select a company-data endpoint. Approve the sample and paid execution separately from any database update. Check the returned identities before constructing proposals. Domain normalization can remove superficial URL differences, but it cannot decide whether a parent and subsidiary are interchangeable.
For each candidate change, show the Airtable record ID, field name, current value, proposed value, evidence reference, Monid run ID, and reason. Put ambiguous matches and unsupported attributes in an exception section. A blank enrichment response must not erase an existing value. Avoid creating a second record just because a company name differs slightly.
Example starter prompt
Review enrichment for [Airtable base, table, and view] using Monid.
Read only [bounded record set]. Use record IDs as update targets and [domain
field] as research input. Suggest changes only to [allowed fields]; preserve
[protected fields]. Inspect the endpoint and request approval for a sample
and spending limit before running company research.
Return a field-by-field diff with old value, proposed value, source evidence,
run ID, and matching reason. Separate conflicts and missing results. Do not
create, merge, delete, or update records. After I approve selected changes,
recheck their current values and stop on concurrent edits before writing.Recheck selected rows at approval time
An approved diff can become stale while someone else edits the base. Before applying approved changes, reread the affected rows and compare current values with the reviewed originals. If a value changed, return it for another decision rather than overwriting the teammate’s work. Approval applies to the reviewed fields and records, not to an open-ended cleanup of the table.
After an authorized update, verify the resulting values through the available Airtable tools and report partial failures. A successful call is not a reason to claim that every row was changed. Retain the before-and-after table for correction, but do not automatically revert later edits. The Airtable overview covers other record workflows; the Monid company guide explains sample-run and identity checks.
Questions this workflow answers
How should external company data update a shared database without erasing a teammate’s corrections?
Use a proposed diff with an explicit policy for each field. External descriptions can be suggestions for an empty research column, while a manually maintained customer segment may remain entirely out of scope. Record the old value as well as the proposed replacement so the reviewer can see what would be lost. Keep the source and matching evidence next to the proposal instead of forcing the reviewer to reopen a separate research report for every row.
Approval should identify exact records and fields. Immediately before writing, compare the live record with the version used to prepare the diff. If someone changed a value in the meantime, stop that update and ask the owner to reconcile it. Do not treat approval of one sample as approval to modify the whole base. Verify completed writes, report rows that failed, and retain enough history to prepare a later correction. This process protects the distinction between external observations and internal business judgment while still allowing useful missing information to reach the people who maintain the database.