Market Research Agent Workflow Teardown: Brief to Evidence

A dependable market research agent should not “research a market” in one prompt. It should turn the request into explicit questions, build a source plan, collect evidence into a ledger, challenge contradictions, calculate only from traceable inputs, and write a decision memo where every important claim points back to a source.

This teardown shows the working system behind the output. The reference flow is:

Research brief
  -> question tree
  -> source plan
  -> evidence collection
  -> evidence ledger
  -> contradiction and gap review
  -> analysis
  -> decision memo
  -> human approval

If you want the broader step-by-step build, read how to build an AI agent for market research. This page focuses on the quality-control architecture that stops a fast agent from becoming a confident rumor machine.

The research request used in this teardown

Assume a small automation agency is considering a productized service for independent dental practices in the United States. The owner asks:

Is there enough demand for an AI-assisted patient recall service, who already sells it, what do buyers care about, and should we test an offer in the next 30 days?

That sentence contains at least five different jobs:

1. define the customer segment;
2. find demand signals;
3. map competitors and substitutes;
4. identify buyer pains, objections, and decision criteria;
5. recommend a test with a measurable stop condition.

A weak agent immediately searches for “dental AI market size” and writes a report. A strong workflow first converts the request into a research contract.

Stage 1: Normalize the brief

The agent turns the request into a structured record:

{
  "decision": "Whether to run a 30-day service validation test",
  "offer": "AI-assisted patient recall for independent dental practices",
  "customer": "US dental practices with 1-5 locations",
  "geography": "United States",
  "timeHorizon": "Current market and next 12 months",
  "questions": [
    "What observable demand signals exist?",
    "Which vendors and agencies already solve this?",
    "What outcomes do practices buy?",
    "What prevents adoption?",
    "What small experiment can validate willingness to pay?"
  ],
  "evidenceStandard": "Primary sources preferred; current dated sources required",
  "output": "Two-page decision memo and evidence appendix"
}

The critical field is decision. Without it, research expands until the time budget expires. With it, the agent can reject interesting facts that do not affect whether to run the test.

Brief quality gate

Stop before search if any of these are missing:

• a concrete decision;
• customer definition;
• geography;
• time horizon;
• evidence standard;
• output format;
• deadline.

Do not let the agent silently invent missing scope. If the workflow must run unattended, use saved defaults and label them as assumptions in the final memo.

Stage 2: Build a question tree

The planner expands each research question into observable evidence.

Each leaf in the question tree should produce either evidence, a calculation, or a clearly labeled gap.

Stage 3: Create a source plan

The agent uses different sources for different claims.

Tier 1: Primary evidence

• official product and pricing pages;
• documentation and help centers;
• government data;
• regulatory text and agency guidance;
• company filings and earnings materials;
• original research papers and datasets;
• direct customer interviews and internal CRM data.

Tier 2: Credible interpretation

• respected trade publications;
• analyst research with transparent methodology;
• specialist media;
• conference talks from named operators;
• independent product reviews with disclosed testing.

Tier 3: Discovery signals

• forums and social posts;
• search snippets;
• vendor directories;
• listicles;
• unattributed statistics;
• AI-generated summaries.

Tier 3 helps the agent discover vocabulary, vendors, complaints, and sources. It should rarely carry a major claim by itself.

The source plan also assigns freshness. Current pricing may need a page retrieved today. A stable definition can use an older authoritative source. A fast-changing feature comparison needs current documentation from both products.

Stage 4: Collect into an evidence ledger

The evidence ledger is the center of the workflow. It exists outside the model's narrative context so the agent can inspect, filter, and cite it.

Use one row per claim-source relationship:

{
  "claimId": "demand-07",
  "question": "What outcomes do practices buy?",
  "claim": "Automated recall is sold as a patient reactivation and schedule utilization workflow",
  "sourceUrl": "https://vendor.example/product",
  "sourceTitle": "Patient Recall",
  "publisher": "Vendor",
  "publishedAt": null,
  "retrievedAt": "2026-08-13",
  "sourceTier": 1,
  "sourceType": "official product page",
  "geography": "United States",
  "evidence": "Short paraphrase or compliant excerpt",
  "supports": true,
  "confidence": 0.82,
  "limitations": "Vendor describes intended value, not independently measured ROI"
}

Never store only a source URL. Future reviewers need to know which claim the page supported, when it was retrieved, and what limitation applied.

Evidence quality gate

Reject or quarantine a row when:

• the link does not resolve;
• the source does not support the claim;
• the date or geography is incompatible;
• a secondary article is the only source for a consequential number;
• units cannot be reconciled;
• the evidence is circular, with several pages repeating one unsourced claim.

Stage 5: Run contradiction and gap checks

Before writing conclusions, a critic step groups evidence by question and asks:

• Which credible sources disagree?
• Are two numbers measuring the same population and period?
• Does a vendor feature page conflict with its pricing or help center?
• Is a benefit supported only by the company selling it?
• Which competitor categories are missing?
• Which recommendation depends on an unverified assumption?

Contradictions are output, not workflow errors. A useful memo may say public pricing is inconsistent, buyer evidence is thin, or two datasets define the market differently.

Create a gap record for every unanswered high-priority question. The final memo should distinguish “no evidence found” from “evidence that the answer is no.”

Stage 6: Perform analysis with explicit math

The agent can organize assumptions and explain results, but calculations should use a visible formula and stored values.

For a bottom-up opportunity estimate:

reachable accounts
x expected contact rate
x qualified-problem rate
x pilot acceptance rate
x monthly price
= initial monthly revenue scenario

Run low, base, and high cases. Label every assumption. Do not convert a global market-size forecast into a revenue forecast for a five-person agency.

For competitor analysis, score dimensions buyers can observe:

• EHR and CRM integration;
• recall channel coverage;
• consent and opt-out controls;
• staff review and handoff;
• reporting tied to booked and attended appointments;
• onboarding time;
• contract and pricing unit;
• data-processing terms.

The purpose is not to declare one universal winner. It is to find an underserved combination of customer, outcome, and delivery model.

Stage 7: Produce a decision memo

The final deliverable is short because the evidence appendix is separate.

Recommended memo structure

1. Decision: test, do not test, or test only after one missing fact is resolved.
2. Why now: three evidence-backed signals.
3. Customer and pain: the narrow segment and costly workflow.
4. Market alternatives: direct, adjacent, and manual substitutes.
5. Proposed offer: scope, exclusions, price hypothesis, and delivery method.
6. 30-day test: target list, outreach, interviews, pilot ask, and metrics.
7. Risks: evidence gaps, operational constraints, compliance, and dependency risk.
8. Stop conditions: numbers that would invalidate the thesis.

A credible recommendation might be:

Run a 30-day validation test with 50 independent practices. Do not build custom software. Sell a narrowly scoped recall audit and two-week supervised pilot using the client's existing systems. Continue only if at least eight qualified owners take a discovery call, three share workflow data, and one pays for the pilot.

That recommendation is useful because it converts uncertainty into a bounded experiment.

Stage 8: Human approval

The human reviewer checks:

• the top ten decision-driving claims against their sources;
• every price, percentage, and market number;
• competitor omissions;
• assumptions in calculations;
• whether contradictory evidence was represented fairly;
• whether the proposed test could create legal, privacy, or reputational risk;
• whether the recommendation follows from the evidence.

The agent can prepare the work. Accountability for the decision stays with the operator.

Workflow timing and cost

A lean run can fit inside 60 to 90 minutes:

Deep research can take days, but the architecture stays the same. More time should create more verified evidence, not merely a longer narrative.

Metrics that reveal whether the agent is improving

Track workflow quality, not word count:

• percentage of decision-driving claims with a valid primary source;
• citation-support accuracy from a manual sample;
• duplicate evidence rate;
• unresolved contradiction count;
• stale-source rate;
• calculation error rate;
• human corrections per memo;
• time from brief to approved decision;
• percentage of recommendations that become a real test;
• percentage of tests that produce a clear continue or stop result.

An agent that writes faster but increases source corrections has not improved.

Common failure modes

One giant research prompt

The model searches, evaluates, calculates, and writes in one opaque pass. Split the stages and persist their outputs.

Search snippets used as evidence

Snippets are discovery aids. Open the underlying page and verify the claim.

Competitors defined too narrowly

Include spreadsheets, staff labor, agencies, bundled EHR features, and doing nothing—not only similar AI startups.

Sources without claim mapping

A bibliography does not prove the report. Map each important claim to the exact supporting source.

No stopping rule

The agent produces a recommendation that cannot fail. Every go-to-market suggestion needs a time box, success threshold, and stop condition.

Final recommendation

The best market research agent is not the one that reads the most pages. It is the one whose reasoning can be audited from decision back to question, claim, evidence, source, and assumption.

Build the evidence ledger first. Add a contradiction pass before synthesis. Keep calculations explicit. Force the final output into a decision memo with a small experiment and stop conditions. That structure turns AI research from an impressive summary into an operating tool.

Related Guides

• How to Build an AI Agent for Market Research
• How to Use AI for Small Business Market Research
• How to Build an AI Competitor Analysis Workflow
• Best AI Tools for Competitive Analysis

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