Gemini Deep Research Agent API: Build a Reviewable Workflow

Gemini Deep Research runs a multi-step research job: it breaks a question into sub-questions, searches the web for sources, and produces a structured report with cited links. The feature is available in Gemini Notebook (formerly NotebookLM, rebranded July 16, 2026) and in the Gemini interface. Developer access to the same capability through the Google AI API is more limited than the consumer interface — check Google's documentation for current availability.

A reviewable workflow means the research question, the source links, and the decisions about which sources to trust are kept outside any single API run. Tools like fenbs hold that record independently, so the research question is available to review before the run and the sources are available to check after it.

Developer access options

Google's Gemini API (via Google AI Studio or Vertex AI) provides access to Gemini models. Grounding with Google Search is available as a tool in the API, which lets a model search the web before answering. Deep Research as a distinct agentic workflow is available in the Gemini interface; its availability as a separate API endpoint changes with product releases.

For the current state of developer access, check Google AI Studio's documentation and the Vertex AI documentation directly. Product capabilities for agentic research features change faster than any third-party guide can track.

• Gemini API with grounding: the model can search the web during a call. Available now in the API.
• Deep Research workflow: multi-step autonomous research with a structured report. Available in the Gemini interface; check Google's API docs for programmatic access.
• Vertex AI Agent Builder: build custom research agents with tool use and grounding on Google Cloud.

What a research job produces

A Deep Research run produces a report: a structured document with a summary, sections on sub-questions, and citations linking to source pages. The citations are what make the output reviewable. You can check each source, verify that the report represents it accurately, and note any sources the report missed.

The report is a draft. AI research tools search what is publicly available and apply the model's judgment about what is relevant. They can miss authoritative sources, misrepresent a source's position, or treat a secondary source as if it were primary. Review is not optional.

How to write a research brief

The quality of a Deep Research run depends on how precisely the research question is defined. A vague question produces a broad report that covers nothing in depth. A specific question with defined scope produces something reviewable.

1. State the question in one sentence. What decision or document will this research inform?
2. Define the scope: what time period, what geography, what industry segment.
3. List what must be included: specific sources to check, specific claims to verify.
4. List what must be excluded: off-topic areas, speculative content, sources known to be unreliable.
5. Specify the output format: a structured report, a list of sources with summaries, a comparison table.

Verifying sources from a research run

Open each cited source and read the relevant passage. Check whether the report's summary of the source is accurate. Look for: claims the report attributes to a source that are not in the source, nuances the report dropped, and dates or figures that may have changed since the source was published.

• Verify dates: research tools may cite older sources for facts that have since changed.
• Check primary vs secondary: a news article about a study is not the same as the study.
• Note contradictions: if two sources disagree, the report may have picked one without acknowledging the other.
• Flag paywalled sources: a research tool may have limited access to the full text.

When to check the research report against primary sources

Some questions need primary source verification more than others. Claims about regulatory requirements, statistics, or product capabilities should always go back to the original source. Background context about a well-documented topic carries less risk. Decide the verification level before the run so the reviewer knows what to check and what to accept as written.

Building a reviewable workflow

A reviewable research workflow has three stages: brief, run, and review. Each stage produces a record that the next stage can check against.

Brief: write the research question, scope, and required sources before running. Run: trigger the research job and save the full report including citations. Review: check each citation, mark the ones that are verified, note the ones that are wrong or missing, and produce a reviewed version of the report.

Keep the three records together. A report without its brief is hard to evaluate. A brief without its source list is hard to improve. The record is what turns a one-time run into a repeatable process.

How to do this with fenbs

1. Create a free account and add a project for the research workflow.
2. Write the research brief as a task: the question, scope, required sources, and output format.
3. After each run, add the source links to the project's Links and listings, with a verification status for each.
4. Record the decisions about source reliability in Decisions and rules: which sources are trusted for this topic, which are excluded.
5. Connect your research assistant to fenbs from Integrations so it reads the research question and source decisions before the next run.