Chinese RFC Research Workflow for AI Agents

The long-tail keyword for this post is Chinese RFC research workflow AI agent. The intent is a serious one: a team is writing or reviewing an RFC and needs Chinese-language technical context, product signals, academic references, or policy discussion. AutoSearch gives the agent MCP-native access to 10+ Chinese sources within a 40-channel deep research system, while the host remains free to choose the LLM.

An RFC workflow should be evidence-first. The agent should collect context, classify claims, and show uncertainty before the team commits to a design.

RFC question

Start with a precise question. "Should we support this payment integration in China?" is too broad. "What technical, policy, and user-experience constraints affect this payment integration for Chinese SaaS buyers?" is better.

The agent can then map subquestions to source families. Technical explanation may need Zhihu and Bilibili. Industry essays may need WeChat Official Accounts. Consumer pain points may need Xiaohongshu. Fast reactions may need Weibo. Official docs and GitHub remain useful for implementation details.

Chinese source map

Use channels as a source map. Chinese research is not one channel. Zhihu tends toward long-form expert answers. WeChat can host company and analyst essays. Xiaohongshu captures user language. Weibo captures rapid public reaction. Bilibili captures video demos and technical education.

Ask the agent to name why each source was chosen. That prevents lazy collection and makes the final RFC easier to review.

Evidence table

Require a table with source, channel, original-language claim, English summary if needed, evidence strength, and RFC implication. This structure keeps the agent from turning complex material into a single confident paragraph.

Because AutoSearch is LLM-decoupled, the host model can translate, summarize, or reason while the retrieval boundary remains stable. If the model changes later, the source workflow can remain intact.

Translation notes

For Chinese RFC work, translation is part of interpretation. Ask the agent to preserve important terms in Chinese when they carry product or policy meaning. Do not collapse every term into an approximate English label.

The examples page can help shape prompts for evidence tables and decision memos. Pair that with MCP setup so the agent can call AutoSearch directly.

Review

Before accepting an RFC recommendation, ask what evidence is missing. Are official sources absent? Are social claims overrepresented? Are Chinese and English sources in conflict? Start with install, run a narrow RFC research task, and use the output as a review aid rather than an automatic decision.

For engineering RFCs, add a final "decision impact" column. It should say whether the evidence changes scope, risk, rollout, support burden, documentation, or localization. This keeps the research tied to the actual proposal. It also makes weak evidence visible. A repeated Xiaohongshu complaint may affect onboarding language, while a verified GitHub issue may affect implementation. AutoSearch gives the agent access to both kinds of signal, but the RFC should record how each signal changes the plan.

That record gives reviewers a concrete way to challenge the recommendation before it becomes roadmap.