AutoGen Deep Research Agent Workflow

The target keyword for this post is AutoGen deep research agent workflow. AutoGen users often design multiple agents for planning, coding, reviewing, and writing. AutoSearch fits naturally as the research capability for one of those roles. It gives the research agent MCP-native access to 40 channels, including 10+ Chinese sources, while the AutoGen system decides how agents collaborate.

This keeps responsibility clear. The research agent gathers evidence. A planner chooses what matters. A writer or coder synthesizes the final output. AutoSearch remains LLM-decoupled, so the multi-agent system can change models without changing source access.

Agent roles

A deep research workflow should not let every agent query everything at random. Give one agent the research role. Its job is to turn a question into source-specific queries, call AutoSearch, and return evidence in a structured format.

Other agents can critique or use the evidence. For example, a product analyst agent may ask for competitor signals, while the research agent collects official pages, GitHub activity, Reddit discussion, Weibo reactions, and Xiaohongshu notes.

Research agent

The research agent prompt should include channel intent. Ask it to choose from the 40 channels based on the task. It should justify why a channel was used and avoid querying irrelevant sources.

For Chinese research, tell the agent to preserve source names and language context. Zhihu answers, WeChat articles, Bilibili videos, Weibo posts, and Xiaohongshu reviews all represent different evidence types. Flattening them into one summary makes the final answer weaker.

Tool contract

MCP gives the AutoGen system a clean contract. Follow MCP setup, expose AutoSearch, and have the research agent call it as a tool. The returned material should include source, channel, claim, and uncertainty.

This boundary also helps debugging. If the final response is poor, inspect whether the research agent queried the wrong channels, the evidence was weak, or the synthesis agent overreached.

Synthesis

After the research agent returns evidence, a synthesis agent can produce the final report, code recommendation, or decision memo. Require it to cite source categories and mention conflicts. If English sources say one thing and Chinese sources reveal another, that divergence is part of the answer.

Use examples for compact task patterns such as competitor scans, weekly paper digests, and similar project discovery.

Guardrails

Keep research bounded. Limit channels per subquestion. Ask for missing evidence. Run local verification for code tasks. Start with install and a small AutoGen experiment before automating a large workflow. AutoSearch gives AutoGen broader eyes; the agent design still decides whether those eyes are used carefully.

The easiest way to evaluate the workflow is to replay the same task with a fixed source plan. Keep the retrieved evidence, then let different AutoGen roles critique the same material. If the agents disagree, inspect whether the disagreement comes from interpretation or missing sources. That gives you a cleaner improvement path than changing every prompt at once. AutoSearch remains the retrieval component, so you can improve agent debate, report structure, or model choice without rebuilding the channel integrations that feed the workflow.