AI Agent Market Research: From Question to Report in One Conversation

Market research used to mean hiring a consulting firm, waiting weeks, and paying five figures for a PDF. Then it meant subscribing to Statista, pulling data from three different government websites, reading analyst reports, and stitching it all together in a spreadsheet. Both approaches produce good research. Neither is fast.

The agent approach works differently. You describe a research question in plain language, and the agent pulls data from multiple sources, cross-references it, and produces a report. One conversation, one output, multiple data sources working together.

The Old Way vs the Agent Way

Consider the question: “How big is the home fitness equipment market in the US, and is it growing?”

The old way: Search Google for market reports. Find a Statista page behind a paywall. Look up Google Trends for “home gym equipment.” Search for industry news. Pull census data on household spending. Check FRED for consumer spending trends. Open five browser tabs, copy numbers into a spreadsheet, and write a summary.

The agent way: Ask the question. The agent searches Google Trends for demand signals, pulls news for industry context, checks census data for household demographics, and queries FRED for economic indicators. It synthesizes the results and hands you a structured report.

The difference is not intelligence. The difference is that the agent has direct access to the data sources and can query them in parallel.

What Data Sources Matter

Good market research draws from several categories of data:

Trend data shows demand direction. Google Trends reveals whether interest in a product category is growing, seasonal, or declining. It also shows geographic concentration and related queries that indicate adjacent opportunities.

News coverage provides context. Press articles, funding announcements, and industry reports explain why trends are moving in a particular direction. An uptick in search interest for “home gym” might correlate with a major brand launch or a shift in consumer behavior.

Demographic data from the Census Bureau tells you who your potential customers are. Household income, age distribution, geographic density, and spending patterns all shape market sizing.

Economic indicators from FRED (the Federal Reserve Economic Data service) add macro context. Consumer spending trends, interest rates, and employment numbers affect whether people are buying discretionary products.

Financial data from SEC filings shows how public companies in the space are performing. Revenue growth, margins, and management commentary in 10-K filings reveal market conditions that surveys and estimates miss.

Building the Workflow

Start with Google Trends to establish demand direction:

“Look up Google Trends data for ‘home gym equipment,’ ‘home fitness,’ and ‘Peloton’ over the past 5 years. Identify the trend direction and any seasonal patterns.”

The agent pulls trend data and identifies patterns. You might see that “home gym equipment” peaked during 2020-2021, declined, and has been slowly recovering since mid-2025.

News for Context

Next, add news coverage to explain the numbers:

“Search for recent news about the home fitness equipment market. Focus on industry reports, company earnings, and consumer behavior trends.”

The agent returns articles about new product launches, shifts in gym membership rates, and analyst predictions. This turns raw trend data into a narrative.

Census for Demographics

Demographics help you size the addressable market:

“Pull Census data on US household income distribution and spending on recreation and fitness. Focus on households earning $75K+ since they’re the target buyer.”

The agent queries census data and returns household counts, median income by geography, and relevant spending categories.

FRED for Economics

Economic context tells you whether the timing is right:

“Check FRED for the latest consumer spending data, particularly discretionary spending and durable goods. Also pull the consumer confidence index.”

The agent retrieves economic indicators and puts the market opportunity in a macro context. High consumer confidence and rising discretionary spending are tailwinds. The opposite means headwinds.

Financials for Validation

Public company data validates the picture:

“Search SEC filings for Peloton’s most recent 10-K. What was their revenue, growth rate, and what risks did they highlight?”

The agent pulls the filing and extracts financials, giving you ground-truth data from a major player in the space.

Example Conversation

Here’s what a single research session might look like:

You: “Research the US home fitness equipment market. I need market trend direction, recent news, target demographics, economic conditions, and public company performance. Produce a structured research report.”

The agent runs all five data pulls, cross-references the findings, and produces a report with sections for each data source plus a synthesis section that ties it all together.

The report might conclude: “Search interest is recovering but remains 35% below pandemic peaks. News coverage is mixed, with legacy brands struggling but connected fitness seeing renewed investment. The target demographic (households earning $75K+) represents 45M households, and consumer confidence is at a 2-year high. Peloton’s latest 10-K shows revenue stabilizing after two years of decline. The market is entering a recovery phase, with opportunity concentrated in connected and smart equipment rather than traditional hardware.”

Tools Involved

This workflow uses tools from AgentPatch:

  • google-trends for demand direction and search interest data
  • google-news for industry coverage and context
  • census-data for demographic and household spending data
  • fred-data for economic indicators
  • sec-search and sec-financials for public company filings and financial metrics

Setup

Connect AgentPatch to your AI agent:

The AgentPatch CLI is designed for AI agents to use via shell access. Install it, and your agent can discover and invoke any tool on the marketplace.

Install (zero dependencies, Python 3.10+):

pip install agentpatch

Set your API key:

export AGENTPATCH_API_KEY=your_api_key

Example commands your agent will use:

ap search "web search"
ap run google-search --input '{"query": "test"}'

Get your API key from the AgentPatch dashboard.

Claude Code

Install the AgentPatch skill — it teaches Claude Code when to use AgentPatch and how to use the CLI:

/plugin marketplace add fullthom/agentpatch-claude-skill
/plugin install agentpatch@agentpatch

MCP Server (Alternative)

If you prefer raw MCP tool access instead of the skill:

claude mcp add -s user --transport http agentpatch https://agentpatch.ai/mcp \
  --header "Authorization: Bearer YOUR_API_KEY"

Replace YOUR_API_KEY with your actual key from the AgentPatch dashboard.

OpenClaw

Install the AgentPatch skill from ClawHub — it teaches OpenClaw when to use AgentPatch and how to use the CLI:

clawhub install agentpatch

MCP Server (Alternative)

If you prefer raw MCP tool access instead of the skill, add AgentPatch to ~/.openclaw/openclaw.json:

{
  "mcp": {
    "servers": {
      "agentpatch": {
        "transport": "streamable-http",
        "url": "https://agentpatch.ai/mcp",
        "headers": {
          "Authorization": "Bearer YOUR_API_KEY"
        }
      }
    }
  }
}

Replace YOUR_API_KEY with your actual key from the AgentPatch dashboard. Restart OpenClaw and it discovers all AgentPatch tools automatically.

Wrapping Up

Market research is a data assembly problem. The hard part was always gathering information from scattered sources and stitching it into a coherent picture. An AI agent with access to trends, news, census, economic, and financial data handles the assembly. You focus on the questions and the decisions that follow. Visit agentpatch.ai to connect the tools and run your first research session.

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