How an AI agent can select an external-data endpoint and produce a bounded research brief with request evidence
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 Treg context, 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 select an external-data endpoint and produce a bounded research brief with request evidence?
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.
Start with the question, not a provider name
A Treg research agent helps when a question needs external evidence but the team has not chosen a data provider. Define the question in operational terms: compare search results for five phrases in a named market, inspect public information about ten company domains, or retrieve content from an approved list of pages. These are separate jobs with different coverage and cost assumptions.
Treg’s catalog flow uses discovery, endpoint inspection, and execution as distinct steps. Ask the agent to search for the capability, inspect plausible candidates, and explain which endpoint fits. Do not assume that a familiar provider exposes the same fields through every endpoint or that a search result authorizes a paid call.
Inspect inputs, outputs, and spending boundaries
Prepare a comparison containing the endpoint identifier, required parameters, supported geography or language, response shape, available pricing information, and unresolved limitations. If freshness is not documented, say so. A response retrieved today may describe information collected earlier.
Set a small initial scope and a maximum budget. The agent should propose the concrete request for review, including pagination and optional enrichment. If it cannot establish the cost or whether retries incur charges, stop before execution and ask for a decision. The plugin approval guide explains the tool-call review boundary in LatchLoop.
Example starter prompt
Use Treg to plan external-data research for [question]. Limit the work to
[domains, URLs, or search terms], [market], and [time period].
Discover relevant endpoints and inspect the best candidates. Compare required
inputs, returned fields, coverage, and current pricing information. Recommend
one endpoint and show the exact sample request and expected output columns.
Do not execute paid requests yet. Ask for approval of the sample, budget,
pagination, and retry limits. After approval, retain request identifiers and
source links where returned. Distinguish observations from interpretations.
Do not contact companies or change records in another system.Turn a sample response into a decision
Check the sample before increasing the scope. Does it cover the intended geography? Does the returned entity match the requested domain? Are timestamps, source URLs, and units present? A successful response with the wrong market is still unsuitable evidence.
Record failures and partial results alongside usable rows. An empty response should not become a negative finding. If two sources disagree, preserve both observations and investigate definitions before choosing one. The final brief should include the question, endpoint selection reason, approved request scope, findings, exclusions, and follow-up checks. Link to the Notion dossier guide when the reviewed result needs a durable team home.
Questions this workflow answers
Which data endpoint should answer a research question when several providers look suitable?
Start by describing the evidence needed to make the decision. A ranking question needs a defined search engine, location, language, and observation window; a company lookup needs a reliable identity key and the specific business fields being researched. Those requirements eliminate endpoints that sound relevant but return a different unit of analysis. Compare the surviving candidates on documented output, missing-value behavior, freshness, and total request scope rather than the length of their feature lists.
The next step is a reviewed sample, not a full batch. Inspect a few known inputs whose answers can be checked independently. A sample reveals mismatched identities, incomplete geographic coverage, unexpected pagination, or fields that are present but unhelpful. Keep the request settings with the results so another researcher can explain why the sample was accepted. Only expand after the owner agrees that the response answers the original question and that the budget covers the proposed work. This makes endpoint selection a testable research choice rather than an assumption hidden inside a report.