Workflow

Build a Research Agent

A source-backed research agent workflow with question scoping, source capture, claim extraction, synthesis, verification, and reviewer approval gates.

Use case

A research agent is useful for research briefs, market scans, competitor summaries, literature triage, and source-backed analysis where the user needs claims tied to evidence. The goal is not to automate judgment away. The goal is to collect sources, extract claims, show gaps, and make the review process faster.

This workflow should be used when the question is important enough that unsupported synthesis would create risk. If the task is a light brainstorm, a simple prompt may be enough. If the output will influence strategy, content, product planning, or public claims, the agent needs the verification discipline described in the source-backed AI writing guide.

Inputs and outputs

Inputs should include the research question, allowed source types, excluded source types, time horizon, geography if relevant, output format, and decision context. The user should also specify whether the agent may use web search, internal documents, uploaded PDFs, databases, or only a curated source set.

Outputs should include a source table, claim table, synthesis, uncertainty notes, rejected sources, and next research tasks. The source table records title, URL or internal ID, date, author or publisher when available, and why the source was included. The claim table maps each important claim to the sources that support it. The synthesis should be shorter than the evidence tables, not a replacement for them.

Tool stack

A practical stack includes search or source retrieval, source capture, a markdown or spreadsheet claim table, LLM-assisted extraction, citation checking, and reviewer notes. For internal research, add access controls and source labels. For public research, add freshness checks and clear treatment of primary versus secondary sources.

The workflow can use the same trace approach as the agent observability guide: input, plan, sources, tool calls, extraction, synthesis, and review outcome. Without a trace, a research agent becomes a narrative generator with unknown evidence.

Step-by-step method

First, scope the question. Rewrite broad prompts into answerable research tasks. “What is happening in AI search?” is too broad. “What claims do vendors make about AI search visibility measurement, and what evidence supports them?” is easier to verify.

Second, collect sources before synthesizing. The agent should capture source metadata and short excerpts relevant to the question. It should not draft the answer while still searching unless the workflow clearly marks the draft as provisional.

Third, extract claims. Each claim should be atomic: one statement that can be supported, contradicted, or marked unknown. Claims that combine several ideas are harder to verify and easier to overstate.

Fourth, synthesize with caveats. The final brief should say what is well supported, what is uncertain, what sources disagree about, and what additional evidence would change the conclusion. Use the LLM output verification guide to review the final answer against its sources.

Verification gate

Every important claim must map to a captured source before publication. If a claim lacks support, the agent should remove it, label it as an inference, or ask for more research. The reviewer should be able to click from the synthesis to the claim table and from the claim table to the source.

The gate should also check freshness, source quality, and scope. A dated source may still be useful for history, but it should not be used as current evidence. A low-quality source may identify a lead, but it should not carry a conclusion.

Human review point

Human review belongs after the source and claim tables are complete but before the final brief is used. The reviewer checks source quality, missing caveats, unsupported claims, and whether the synthesis overstates weak evidence. The human-in-the-loop AI workflows guide explains how to build a review packet for this handoff.

Failure modes

Research agents fail by over-synthesizing, using stale sources, treating marketing claims as facts, ignoring contradictory evidence, and hiding uncertainty. They can also fail by producing too much text and too little source structure. The fix is not a longer prompt; it is better source capture, claim mapping, and review.

Frequently asked questions

What should a research agent output?

A research agent should output a source table, claim table, synthesis, open questions, and reviewer notes rather than only a polished narrative.

When should a research agent refuse to answer?

A research agent should refuse or escalate when sources are missing, contradictory, stale, out of scope, or too weak for the requested conclusion.

Reusable template CTA

Use the AI Workflow Planning Template to define the research question, source boundaries, review gate, and final evidence packet before building the agent.