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Airtable AI works inside your base through configurable “field agents.” A field agent reads the record fields you reference, then retrieves, analyzes, or generates a value in an AI field. You control its instructions, inputs, model, and run settings. The agent uses only data the requesting collaborator is permitted to view, but any referenced values can become visible to people who can see the resulting AI field.
AI can also participate in automations: a trigger starts an action such as generating a summary, updating a field, or sending information to another service. It is an event-driven process, not a continuously watching scraper. For reliable results, use AI for variable or interpretive work, formulas for fixed rules, and human review for consequential outputs.
What does Airtable AI actually do with your records?
Airtable’s main record-level AI feature is the field agent. It operates at the cell level, using your instructions plus selected values from the current record (and, where configured, related context) to produce a result in an AI-enabled field. Depending on the field and configuration, it can generate text, analyze content, suggest linked records, help write formulas, extract information from attachments, or assist with workflow construction through Omni.
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A field agent is not a permanent database transformation. It runs at a particular point in time—when you run it, when an automatic-run setting fires, or when a referenced value changes. Changing the prompt, selected inputs, or model can change the answer for the same record, so treat the output as interpretive rather than deterministic.
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AI in fields requires workspace-level enablement and available AI credits. Feature availability also varies by field type, workspace settings, permissions, and plan.
How does a field agent use data in a record?
You define four things: the task, the context, the run behavior, and the model. The agent then sends the resulting prompt and referenced base values to the AI model configured for the workspace.
Instructions define the task
Custom instructions tell the agent what to produce and how to handle uncertainty. For example, an instruction can request a three-sentence customer summary, a classification label, or a draft response. Include the desired format, the acceptable evidence, and what to return when information is missing. If exact wording matters, explicitly request verbatim text instead of a summary.
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Select the fields the agent may use. A long-text agent can combine notes, status, and other record values. A linked-record field agent can use context to suggest records from another table. Supplying only necessary fields makes the result easier to audit and reduces the amount of data sent for processing.
Settings determine when and how it runs
Settings can include internet search, automatic runs, and the model available to the workspace. Automatic runs are useful when a source field changes, but every run consumes credits. If you change a model or prompt, run the revised configuration against the same sample records and compare the outputs before applying it broadly.
Omni can help build the configuration
Omni can draft or build a field agent and can take plain-language requests to build, update, or query base data. Treat its draft as a starting point: verify that the referenced fields, instructions, and permissions match the real task before enabling automatic execution.
What data can Airtable AI access, and who can see it?
Airtable’s “Using Airtable AI in fields” guidance, updated October 19, 2026, states that prompts and the base data referenced in those prompts are sent to the configured AI model. Access follows the requesting collaborator’s existing base and record permissions. An agent cannot use a record that collaborator is not allowed to view.
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That permission boundary does not automatically protect the output from overexposure. Anyone who can see the generated AI field may be able to see information that the agent incorporated from referenced fields. Design interfaces, views, and field visibility so that a summary or generated response does not disclose restricted values.
Internet search is optional, not a public data pipe
Enabling internet search allows an agent to retrieve web information. Airtable states that this setting does not itself publish or share your base data publicly. It does not, however, turn the agent into a controlled web crawler; prompts and referenced base values are still processed according to the configured model and your workspace permissions.
There is no field-level exclusion switch
The documentation says field-level exclusion from AI processing is not currently supported. Table- and base-level permissions are therefore the stated controls. If a column contains personal, regulated, or confidential information, review your organization’s policy on sending that information to an external AI vendor before referencing it in an agent.
Training of third-party models
Airtable states that it does not use user data to train third-party AI models. That statement addresses model training; it does not remove your responsibility to limit referenced fields, control who can view the resulting field, or review generated content.
How do you create and test a field agent?
The labels can differ by plan and workspace, but the configuration sequence is consistent. Build a small test set first; do not start with automatic runs across every record.
- Enable AI for the workspace. Confirm that an administrator has enabled AI features and that the workspace has available AI credits.
- Add an AI-enabled field. Choose the field-agent capability appropriate to the result you need, such as generated long text or linked-record suggestions.
- Write the instruction. State the output format, evidence rules, handling of missing information, and whether the response must quote source text exactly.
- Select referenced inputs. Include only the fields and linked context required for the task. If web information is needed, enable internet search deliberately and define which sources the agent may rely on.
- Choose the model and run behavior. Select a model available to the workspace. Decide whether to run manually, on input changes, or through an automation.
- Test representative records. Include complete, incomplete, unusually long, and edge-case records. Check factual accuracy, formatting, permissions, and whether the output reveals data that should remain hidden.
- Review before scaling. Compare outputs from the same records after any prompt or model change. Only then enable automatic runs or connect the field to downstream actions.
When should you use a field agent instead of a formula?
Use a field agent when the input is inconsistent, incomplete, or requires interpretation or outside knowledge. Use a formula when the rule is stable and you need the same input to produce the same immediate result without consuming AI credits. Airtable’s own distinction is: “A formula follows the rules you write exactly. A field agent interprets what it’s given and can make a reasonable assumption.”
| Decision factor | Formula | Field agent |
|---|---|---|
| Input shape | Best when fields follow a consistent pattern. | Useful when text, labels, or formats vary. |
| Reasoning required | Applies explicit operators and conditions. | Interprets language, summarizes, classifies, or infers context. |
| External knowledge | Cannot natively interpret changing web information. | Can use internet search when enabled. |
| Determinism | Same inputs follow the written rules exactly. | May make a reasonable assumption and can vary with model or prompt changes. |
| Timing | Calculates immediately from formula dependencies. | Runs when triggered or when configured inputs change. |
| Cost | Does not use AI credits. | Each run uses a variable number of AI credits. |
A practical pattern is to use formulas for dates, arithmetic, IDs, and strict eligibility rules, then pass those stable fields into an agent that handles explanation, summarization, or categorization.
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Can Airtable AI run inside automations?
Yes. Airtable describes an automation as a trigger followed by one or more actions. A trigger can originate in Airtable or a connected application; actions can update records or work in an integrated service. AI-enabled actions can generate content that a later action stores, shares, or sends elsewhere.
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Common AI automation patterns
- Feedback digest: a new customer-feedback record triggers an AI summary, then an action shares the digest with the appropriate team.
- Editorial assistance: a status change triggers generation of a guide or draft for editors to review.
- Structured enrichment: an incoming record triggers classification or a suggested linked record, followed by a human approval step.
Airtable’s “Airtable automation actions: Generate with AI,” updated September 16, 2026, documents the AI-generation action. “Getting started with Airtable automations,” updated October 2, 2026, explains the broader trigger-and-action model. Omni can also construct or update automations from a plain-language request, but inspect every trigger, field mapping, and permission before turning the automation on.
Put a review gate before consequential actions
Do not let an unreviewed generated value automatically send legal, financial, medical, employment, or customer-facing commitments. Write the AI result to a draft field, have a person approve it, and let a second automation perform the external action only after approval.
How can you keep an AI field fresh?
A field agent does not continuously watch a web page. It runs in response to an event or a configured input change. If a source changes without any Airtable field changing, the existing result remains unchanged.
Use a scheduled recheck for changing web information
For periodic monitoring, pair an internet-enabled agent with a scheduled automation. The automation updates an input field—often a timestamp or refresh token—which causes the agent to run again. Choose the interval based on how often the source changes and how stale the result may safely become.
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|---|---|---|
| Source changes only when a record is edited | Run on referenced-field changes. | Lower credit use; no check when the external source changes alone. |
| Source changes several times a day | Scheduled automation at a matching interval. | Fresher data, with credit use multiplied by records and runs. |
| Source changes rarely or unpredictably | Manual refresh or an infrequent schedule. | Less cost, but a greater chance of stale information. |
Estimate credits before enabling a schedule: number of records multiplied by runs per period, adjusted for the input size, model, and output length. Airtable describes credit use as variable rather than a fixed price per run.
What are Airtable AI’s accuracy and extraction limits?
Airtable warns that AI output can contain inaccuracies or bias and is intended for users to review and edit. An agent that reads a URL is not a scraper: it may return information that is not present on the supplied page and may paraphrase by default. To constrain it, instruct the agent to rely only on the specified page, state what to return when the information is absent, and quote verbatim when exact wording is required.
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Document extraction is text-oriented
Document extraction supports particular attachment formats and can depend on your plan and Labs settings. The documented extractor extracts text; images embedded inside documents are not supported by the described extraction behavior. Check the current Airtable documentation for the file types, size limits, and image-analysis entitlement on your plan before designing a production workflow.
Review failure modes, not just successful examples
- Missing values can lead the agent to infer an answer unless your instruction requires an explicit “not found” response.
- Long or conflicting fields can produce a plausible but wrong synthesis; identify which field is authoritative.
- A prompt that requests a concise result may omit qualifying details; specify required exceptions and units.
- Changing the model or prompt can alter prior outputs; retain a sample set for regression checks.
How do Airtable AI credits and prices work?
AI actions consume a variable number of credits based on the input data, selected model, and output. Workspaces receive plan-dependent monthly credits, and Airtable lists additional credit packs. The following examples were listed on Airtable’s billing page accessed October 25, 2026; commercial terms and included credits can change, so verify the live page and your plan before budgeting.
| Credit pack example | Monthly price | Annual price | Qualification |
|---|---|---|---|
| 10,000 credits | $20 per month | $200 per year | Published billing-page example; volatile terms. |
| 50,000 credits | $100 per month | $1,000 per year | Published billing-page example; volatile terms. |
| Larger packs | Not stated | Not stated | Availability and pricing depend on the live billing page. |
Because consumption depends on payload and model, a credit pack is not a guaranteed number of records. Test a representative run, observe actual usage, and multiply by the schedule and record count before committing to a large-scale automation.
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Use permissions, field design, and review controls together. A technically correct prompt can still create a disclosure if the generated field is visible to a broader audience than the source fields.
- Map which collaborators can view each source field and the generated field.
- Keep sensitive columns out of referenced inputs unless your policy permits vendor processing.
- Separate draft AI output from approved or published output.
- Record the model, prompt version, and test date so changes are explainable.
- Set an explicit missing-information response such as “No supporting value found.”
- Use formulas for hard business rules and reserve AI for interpretation.
- Monitor credit consumption after enabling automatic or scheduled runs.
- Re-test after changing a model, prompt, linked-table structure, or permission.
What should you check when an AI workflow fails?
The field is blank or did not update
Confirm that AI is enabled for the workspace, credits remain, the requesting collaborator can view every referenced field, and the run condition actually occurred. For an automation, inspect the trigger record and each action’s field mapping.
The result is stale
Check whether a referenced input changed. An external web-page change alone does not trigger a run; add a scheduled automation that updates an input field if periodic rechecking is required.
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The result contains unsupported or invented detail
Narrow the referenced inputs, instruct the agent to use only the named source, and require an explicit response when evidence is absent. For URL-based tasks, request verbatim excerpts and review the output before sharing it.
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Inspect schedule frequency, record volume, input size, model choice, and output length. Disable automatic runs while testing and run the agent on a small representative set before scaling.
Frequently Asked Questions
Does Airtable AI remember previous runs for the same record?
Treat each run as a new interpretation of the configured inputs. If continuity matters, write the approved result or a concise state field back into the record and reference that field explicitly on the next run.
Can I prevent an AI field from revealing a hidden source column?
There is no documented field-level exclusion control for AI processing. Keep restricted information out of the agent’s referenced inputs and use table-, base-, interface-, and view-level permissions to control exposure.
Why do two runs give different wording from identical records?
Model behavior is interpretive, and outputs can change after a prompt or model configuration changes. Preserve a fixed test set, compare outputs after every change, and use formulas where exact repeatability is essential.
What is the safest way to publish AI-generated customer text?
Store the generated text in a draft field and require a human approval flag before any automation sends or publishes it. Include the source fields or links needed for the reviewer to verify claims.
Can document extraction read a chart or photo inside a PDF?
The documented extractor is text-oriented and does not support images embedded within documents. Image analysis can depend on plan and Labs settings, so verify current attachment and image capabilities before relying on visual content.
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