AI Agent 2 min read

Search Once, Then Read Selected Sources With Bounded Parallelism

After a search, read only the sources selected for the question and retain a disposition for every selected URL.

(Updated: ) • 280 words

After a search, read only the sources selected for the question and retain a disposition for every selected URL. Discovery, source selection and reading are separate stages. A fast partial read should not be presented as complete evidence.

Build a source-reading manifest

Field Purpose
Discovery query and market Identify how the candidate was found
Candidate URL and selection reason Make the reading choice reviewable
Normalized URL and identity Avoid scheduling the same source twice
Read status and access time Retain failures and source age
Required passage check Decide whether the text completes the task
Partial-evidence decision Stop, continue within budget or return unresolved

Schedule selected work inside an account budget

Select from a validated SERP envelope, deduplicate source identities under an explicit URL policy and cap the number of URLs admitted to the reading stage. Use a worker count within account capacity, a per-read deadline and a total work budget.

The accompanying three-worker fixture retains one successful input and one explicit timeout while deduplicating a repeated input. That validates the local partial-failure contract; it is not a provider latency or completeness benchmark. The Python executor implementation has its own guide.

Decide what partial evidence permits

This page completes the source-selection and read-disposition task. It does not repeat a broad RAG architecture, a Python thread-pool tutorial or a generic parallel-search performance promise.

  • Do not count a failed read as evidence against the source’s factual claim.
  • Check whether the remaining sources support the required answer.
  • Spend another read only when it fits the defined budget and adds relevant evidence.
  • If a required claim remains unsupported, omit it or return an unresolved result.

Reference documentation: www.searchcans.com — /google-search-api; www.searchcans.com — /reader-api.

Tags:

AI Agent RAG LLM API Development Integration Web Scraping
SearchCans Team

SearchCans Team

SERP API & Reader API Experts

The SearchCans engineering team builds high-performance search APIs serving developers worldwide. We share practical tutorials, best practices, and insights on SERP data, web scraping, RAG pipelines, and AI integration.

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