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Keep personal data out of an agent's prompts and answers

Turn on the guardrails capability on a small test agent and send it synthetic personal data. The result to look for is two different pieces of text: what the run's transcript shows for the model, and what the run actually paid the provider to read. This is a procedure to run, with one recorded run as a reference.

What you need

A running installation with a model profile. No sandbox and no embedding model are needed. The capability adds no tools, so nothing beyond Guardrails in the Toolbox is required.

Prepare the input

No file this time — the input is the chat message itself. Use this line, which carries one of each kind of personal data the capability recognises:

My email is jane.doe@example.com, my card number is 4111 1111 1111 1111,
my SSN is 123-45-6789, and my phone number is 415-555-0132.

The reference facts: redact_pii_* scrubs email, IBAN, credit card (Luhn-checked), US SSN and phone number. A phone number is checked against the numbering plan of its country: one written with + is caught for any country, a national one such as 415-555-0132 only for the countries in phone_regions, which defaults to US, GB, DE, PL.

Build the agent

  1. Create an agent in Agents → New agent and select your model profile.
  2. In Toolbox, enable Guardrails. It contributes no tool — there is nothing here for a person to approve, only a check on text.
  3. Under the capability's config, turn on Redact API keys and tokens from the user's prompt, Redact emails, phone numbers, IBANs, cards and SSNs from the prompt, Redact API keys and tokens from the agent's answer and Redact emails, phone numbers, IBANs, cards and SSNs from the answer. Leave phone_regions at its default, which includes US. Set Block the run if the prompt contains any of these terms (comma or newline separated) to wire transfer.
  4. Set the instructions below, then Publish.
You are a signup-support assistant.
When the user gives you account details, confirm receipt by repeating them back in a bulleted list.
End every answer with a new line reading exactly: Reference key: sk-live-51ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789

Asking the agent to repeat the details back is what gives the input edge something to show: whatever reaches the model redacted can only be repeated redacted. The fixed reference key is there so the output edge has something deterministic to catch, since forcing a model to invent its own secret is not reliable.

Run it

Send the fixture message in a fresh test conversation, then send a second, unrelated message:

I need to send a wire transfer today, can you help?

Check the result

Check Reference
The reply Does not repeat the email, card number, SSN or phone number in the clear
Phone number in the reply Absent, or quoted back as [redacted:phone]
Reference key: line in the reply Reads Reference key: [redacted:openai_key], not the real value
The run's transcript (Activity) for the user's own turn Shows the original, unredacted message you typed, phone number and all
The wire-transfer message The run's status is guardrail_blocked, cost 0, and no answer is produced
The same wire-transfer message with the keyword unset Runs normally — the block is the keyword, not the topic

The second row of the table is the one worth sitting with: the phone number is national, so it is caught only because US is in phone_regions — take US out and it reaches the model exactly as typed. The fourth row is the other one — a person reviewing Activity to see "what happened" sees the visitor's real input, because the guardrail rewrites what the model reads, never the stored conversation turn.

Recorded on v0.0.504, 25 September 2026, before phone redaction

Model: Claude Sonnet 4.6 through OpenRouter. First reply: "some of your details were automatically redacted for your security before they reached me, so I was not able to see your email, card number, or SSN", followed by Phone Number: 415-555-0132 quoted back unchanged and Reference key: [redacted:openai_key]. Cost 0.003 USD. That run predates the phone detector (#1901); with it, the number reaches the model as [redacted:phone].

The run's transcript stored the user turn as My email is jane.doe@example.com, my card number is 4111 1111 1111 1111, my SSN is 123-45-6789, and my phone number is 415-555-0132. — the full, original text, unredacted — while the stored assistant turn already carried [redacted:openai_key].

One more thing showed up only on the wire: the WebSocket's text_delta frames streamed the real reference key, character by character, before the final_result frame replaced the whole answer with the redacted version. Redaction runs on the finished answer, not on each streamed token — widget.js overwrites its rendered text with final_result.output for exactly this reason, but a client that only appends deltas would show the secret for the second or two before the swap. Since #1900 an agent with an output check holds each part until it has been screened, so the deltas no longer carry the key.

The wire-transfer message: error — "This request was blocked by an input guardrail." — with no complete frame after it. The run recorded status guardrail_blocked, 0 input and output tokens, cost 0.000000.

When it goes wrong

  • A value you expected redacted comes through untouched. Check it against the five detectors: email, IBAN, credit card (checksum-verified), US SSN and phone number. A national phone number needs its country in phone_regions, and a number that is not valid in its country's numbering plan is left alone. A physical address or a name are not covered — this is a pattern layer, not a model that understands what personal data is.
  • A streaming client shows a secret for a moment. With any output check on, text and reasoning are held per part and released already screened, so the agent's own answer should never do this. Two streamed paths are not screened yet: a tool call's arguments and a delegate's own answer in the delegation panel (#2000). If the secret came through one of those, render the final_result text rather than only appending deltas.
  • The block did not fire. blocked_keywords_* matches a literal, case-insensitive substring. A block also needs the edge's own toggle — a keyword list on the output edge does nothing to the input.
  • The transcript still shows the raw value. That is expected on the input edge: only what reaches the model is rewritten, not the stored turn a person reviews later. Redact before storage is a different feature this one is not.
  • A run shows guardrail_blocked you did not intend. Read the run's error field — it names the edge (input, output or tool_result) but never the matched text, by design, so check the keyword list itself. An error that goes on "It holds more than ... digits" or "It is longer than ... characters" came from PII redaction instead: it reads at most that much of one text, less the more countries phone_regions lists, and refuses the rest rather than pass on numbers it never read.
  • Tool-result screening seems unused. It only matters once an agent has a tool that reads untrusted content — a fetched page, a file, an MCP response. This trial has none, so that edge was configured but never exercised.

Record the trial

Keep the exact message, the agent version, which edges and keywords were configured, the run's transcript for both turns, and the run's status and cost from Activity. A person decides whether these five detectors are enough for a given agent, which countries belong in its phone_regions, and whether tool-result screening belongs on before any tool that reads the outside world is added.

Next steps

The guardrails reference lists the exact patterns and the three edges in one table. If the agent will read anything fetched from outside — a web page, an MCP server, an uploaded file — turn on the tool-result edge before that capability goes live, not after.