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Airtable AI works inside your base by running configurable “field agents” against the records and fields a collaborator is permitted to access. An agent can interpret text, generate content, suggest linked records, extract information from attachments, or help construct an automation. It sends your prompt and the referenced base data to the AI model selected for the workspace, then writes the result back to a field or passes it to an automation action. The result is useful for variable, judgment-based work, but it is not deterministic and should be reviewed. Airtable AI also uses credits, so the design of your prompts, triggers, and schedules affects both reliability and cost.
What is Airtable AI, and what does it do with a record?
Airtable calls many of its AI-enabled fields field agents. A field agent operates at the record-cell level: it reads the inputs you reference, follows custom instructions, and returns a generated value in that field. A long-text agent might turn a set of notes into a customer-ready summary. A linked-record agent can suggest records from another table when the surrounding context matches. Other AI features help write formulas, extract text from documents, or build workflows through Omni.
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- Instructions: what the agent should produce, how it should format the result, and what to do when information is missing.
- Referenced inputs: fields, linked records, or attachments from the current record that the model may use.
- Run settings: options such as internet search and whether to run automatically when inputs change.
- A workspace-available model: the model choices and AI enablement exposed by your plan and workspace settings.
Omni can draft or build a field agent from a plain-language request, but its draft is a starting point. Compare the proposed instructions with the fields the agent will actually receive; a prompt that sounds right can still expose the wrong column or omit a required input. If you change the model or prompt, compare outputs on the same records because an agent interprets context rather than applying a fixed calculation.
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How does Airtable AI access my data?
Airtable says the prompt and the base data referenced by that prompt are sent to the configured AI model. Access follows the requesting collaborator’s existing base and record permissions: the request cannot read records that person is not allowed to view. Airtable also says it does not use user data to train third-party AI models.
Permissions do not end when the model returns an answer. Anyone who can see the resulting AI field may be able to see information that was included in the prompt, even if that information came from a field they cannot normally open. Design the interface, views, and field visibility so the generated text does not become an unintended disclosure channel.
Internet search is an optional agent setting. Turning it on lets the agent retrieve web information; Airtable says this does not itself publish or share your base publicly. It does mean the agent can combine external results with your referenced record data, so decide whether that is acceptable for the task and your organization’s data policy.
Airtable’s documentation currently describes no field-level exclusion switch for AI processing. Table- and base-level permissions are therefore the stated controls. Before sending personal, confidential, regulated, or customer-provided information to an external model service, apply your own legal, security, and vendor-review requirements.
How do I set up a field agent?
The exact labels can vary with field type, plan, and workspace settings, but the setup sequence is consistent. You need an AI-enabled workspace and available AI credits.
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- Add or select an AI-capable field. Choose the field type appropriate to the output, such as long text for a summary or a linked-record field for suggestions.
- Write the instruction. State the job, audience, output format, length, and a rule for missing information. For example: “Summarize the feedback in three bullets, quote no more than 15 words, and return ‘Not stated’ when the record contains no evidence.”
- Select referenced fields. Include only the columns the task needs. Check linked records and attachments carefully because they can add substantial context and credit usage.
- Choose run options. Decide whether the agent may use internet search and whether it runs automatically when referenced values change. Automatic runs are convenient but can consume credits whenever upstream data is edited.
- Test on representative records. Use short, complete, messy, and empty examples. Look for unsupported assumptions, formatting drift, and accidental inclusion of confidential values.
- Set review and visibility rules. Decide who can edit the prompt, who can see the generated field, and which downstream automations may act on it.
When Omni creates the agent, inspect every referenced field and instruction before enabling automatic execution. A generated configuration can be syntactically valid while using a field that contains more personal information than the task requires.
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Which Airtable AI features use existing records?
Generated text in long-text fields
A long-text field agent can combine instructions with values from other fields on the same record. Common uses include summaries, classifications with an explanation, first-draft replies, and normalization of inconsistent notes. The output is generated text, not a guaranteed transcription of the inputs, so store the source fields and preserve a human approval step when the result is customer-facing or consequential.
Suggested linked records
A linked-record agent can recommend records from a linked table based on the current record’s context. This is useful when names, tags, or descriptions vary, but suggestions should be treated as candidates. Require a person or a deterministic rule to confirm the link when a wrong association would affect billing, access, or reporting.
Formula help and Omni
AI can help draft a formula or explain why one fails, while Omni can help build, update, and query base data in plain language. The resulting formula still follows normal formula semantics, and an Omni-built change should be checked in the same way as a manually configured field or automation.
Document extraction
Document extraction reads supported attachment formats and returns text or selected information. The available formats, file limits, image-analysis capability, and Labs settings depend on the current documentation and plan. The described extractor extracts document text; images embedded inside those documents are not supported by that extractor. Confirm current entitlements before designing a process around a particular file type or size.
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Should I use a formula or a field agent?
Use a formula when the rule is stable and the same input must always produce the same result. Use a field agent when the input is inconsistent, incomplete, interpretive, or dependent on knowledge outside the base. Airtable summarizes the distinction this way: “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 pattern | Consistent fields and predictable types | Variable formats, prose, or missing values |
| Reasoning required | Arithmetic, logical tests, text functions, and exact transformations | Interpretation, classification, summarization, or outside knowledge |
| Determinism | Same inputs follow the written rules exactly | May make a reasonable assumption and can vary with model or prompt changes |
| Timing | Immediate recalculation without an AI run | Runs when triggered or when referenced values change |
| Cost | Does not use AI credits | Consumes credits based on input, model, and output |
| Review burden | Validate the formula once, then monitor for data-type errors | Review prompts and outputs, especially for edge cases and external claims |
A practical pattern is to use both: a formula for a strict eligibility or date rule, then an agent to explain the result in plain language. Keep the machine-checkable value separate from the generated explanation so a wording change cannot silently change the business rule.
Can Airtable AI run inside automations?
Yes. An Airtable automation consists of a trigger followed by one or more actions. The trigger can come from Airtable or a connected application; actions can update records, generate AI output, or send data to an integrated service. Airtable documents examples such as summarizing customer feedback and sharing the summary, or generating an editorial guide for reviewers.
To build a controlled AI automation:
- Choose a narrow trigger. For example, run when a record enters a review view or when an approval field changes, rather than on every keystroke.
- Capture the required context. Pass only the fields the AI action needs and include a record identifier so the result can be traced back.
- Add the AI action. Specify the output format, limits, and missing-information behavior. If the output will be parsed by another action, require a stable structure such as labeled lines or JSON-like text.
- Insert a review gate where risk warrants it. Write the generated result to a pending field or queue before sending an email, changing a status, or updating an external system.
- Test success and failure paths. Test an empty input, an unusually long input, a permission-limited record, and an AI response that does not follow the requested format.
- Monitor runs and credit use. Keep an audit field with the run time, model or prompt version, and reviewer decision when the output affects customers or operations.
Omni can also accept plain-language requests to construct or query base workflows. Treat an Omni-generated automation as a configuration to inspect, not as an authorization to change production data without review.
Does a field agent continuously monitor a website?
No. An agent run is an event, not a persistent web-page watcher. It runs at its configured point in time, such as when a referenced value changes or an automation invokes it. If a web page changes while no run occurs, the field remains unchanged.
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For periodic rechecks, Airtable documents pairing an internet-enabled agent with a scheduled automation. The schedule updates an input field, which causes the agent to run again. Choose the interval by balancing three variables:
- Source update frequency: checking a page every hour is wasteful if it changes weekly.
- Acceptable staleness: operational alerts may need frequent checks, while a monthly reference list may not.
- Record volume and credit budget: a schedule that touches 5,000 records can consume far more credits than one that checks a single record.
Use a last-checked timestamp and a change-detection field when possible. That lets the automation skip unchanged content instead of regenerating every record on every schedule.
How should I prompt an agent that reads a URL or document?
A field agent reading a URL is not a conventional scraper. Airtable warns that it may return information not present on the supplied page and may paraphrase by default. If accuracy depends on one source, make the constraints explicit: instruct the agent to rely only on the specified page, identify the page or section used, state what to return when the answer is absent, and quote verbatim when exact wording matters.
For documents, define what counts as evidence and what to do with unreadable or missing text. Ask for a “not found” response rather than an inferred value, and preserve the original attachment alongside the extracted result. When an image contains the key information, do not assume the document extractor will interpret it; use a supported image-analysis capability if your plan and workspace settings provide one, or route the file for manual review.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How much do Airtable AI credits cost?
AI actions consume a variable number of credits based on the amount of input data, the selected model, and the output. Workspaces receive plan-dependent monthly credits, and Airtable offers additional credit packs. The billing page examples accessed October 25, 2026 list the following commercial options:
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| Credit pack example | Monthly price | Annual price | Qualification |
|---|---|---|---|
| 10,000 credits | $20 per month | $200 per year | Example listed on Airtable’s current AI billing page; terms and entitlements can change |
| 50,000 credits | $100 per month | $1,000 per year | Example listed on Airtable’s current AI billing page; terms and entitlements can change |
These are credit-pack examples, not a universal per-run price. A short classification over a few fields can use fewer credits than a long prompt containing linked records and attachments. Before enabling a scheduled automation, estimate records per run, runs per month, and the largest input so a harmless edit does not create an unexpectedly expensive loop. Check the live billing page and your workspace plan for current allowances.
What are the main failure modes, and how do I fix them?
- The answer invents a value. Add a rule to return “Not stated” when the referenced fields contain no evidence, and test an intentionally empty record.
- The answer cites the wrong web page. Disable internet search when it is unnecessary; otherwise require the specified page only and ask for a verbatim quote or section reference.
- Private details appear in a shared field. Reduce referenced inputs, restrict field and interface visibility, and review which collaborators can see the generated result.
- Outputs change after a prompt or model update. Re-run a fixed test set, compare old and new values, and record the prompt or model version before deploying the change.
- Credits disappear faster than expected. Remove unused fields and attachments, narrow triggers, batch work where practical, and lengthen schedules to match the source’s real update rate.
- An automation acts on malformed output. Add a validation step or approval field before downstream actions, and keep the raw AI response for troubleshooting.
- A document result is incomplete. Check the attachment format and plan support, confirm that the needed text is not embedded only as an image, and send exceptions to manual review.
What should a safe rollout checklist include?
Before turning on automatic runs, verify that the workspace has AI enabled and enough credits, the requester’s permissions match the intended data boundary, and generated fields are visible only to the right audience. Test representative records, including missing and unusually long inputs. Decide which outputs require human approval, document how prompt or model changes are tested, and set a schedule that reflects both source freshness and credit limits. Keep formulas for rules that must remain exact, and reserve agents for the interpretive work they are suited to perform.
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Can Airtable AI see records I cannot open?
A request is evaluated with the requesting collaborator’s base and record permissions, so it cannot retrieve records that person is not allowed to view. However, a generated field can reveal referenced values to anyone who can see that field, which is why field and interface visibility need separate review.
Will Airtable use my base data to train an outside AI model?
Airtable states that it does not use user data to train third-party AI models. Your prompt and the referenced data are still sent to the configured model to produce the requested result, so your organization should apply its own privacy and vendor policies.
Why did an AI field change when none of my source text changed?
Model availability, prompt settings, internet results, or an automatic rerun can change an interpretive output even when the visible source is unchanged. Keep a test set and compare outputs after any model or instruction change.
Can I make an Airtable AI result update every hour?
Not by leaving the agent running continuously. Use a scheduled automation to update an input or trigger field, which starts a new agent run; choose the interval based on source changes, acceptable staleness, record count, and credit consumption.
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