Research workflow guide / 2026

AI Agent Research Workflows That Stay Source-Linked in 2026

Research agents are most useful when their steps are legible: a query, a shortlist of sources, readable evidence, and a final answer with links.

Quick answer

Search broadly, read selectively, answer narrowly.

Start with a concise query, choose relevant URLs, extract pages that support the task, and keep only the evidence needed for the final answer.

curl --get 'https://extractor.sh/api/extract' \
  --data-urlencode 'url=https://example.com/' \
  --data-urlencode 'format=json'

Available data

What you can extract

Ordered discovery results
Readable source content
Canonical source links
Typed response data for orchestration

AI workflows

Where normalized data helps

Competitive research
Briefing preparation
Documentation Q&A
Public market research

AI-ready output

Markdown for models. JSON for systems.

Raw HTML consumes tokens on navigation, scripts, styling, and interface labels. Clean Markdown keeps the readable hierarchy for LLM prompts and RAG chunks. Normalized JSON is better when your application needs an explicit semantic type, source, author, publication date, media, attributes, and collection items.

Always retain the canonical URL from the response. AI-generated summaries should remain traceable to the public source, especially when the underlying page can change.

Boundaries

Public data only

Agents need stopping rules and evaluation criteria.
Search ranking is not fact-checking.
Public results are not confidential primary data.

extractor.sh does not bypass CAPTCHAs, login walls, paywalls, access controls, or regional restrictions. Review the source’s terms and applicable law before collecting or reusing data.