AI Investment Research: How Agents Gather and Analyze Company Data

Investment research is mostly reading. Annual reports, 10-K and 10-Q filings, earnings call transcripts, news coverage, regulatory filings, competitor financials, industry publications. Before you form a view on a company, you’ve probably read hundreds of pages of documents, and most of that reading is retrieval: finding the numbers, locating the disclosures, collecting what exists before you can start interpreting any of it.

That retrieval work is where AI agents help most.

What Investment Research Actually Involves

Breaking down the research process makes it clearer where automation fits and where it doesn’t.

The first stage is data gathering: pulling financial statements, reading annual and quarterly filings, finding the relevant disclosures buried in footnotes. This is time-intensive but not analytically demanding. The second stage is comparison: how does this company’s revenue growth compare to peers? How have margins trended over the past four years? Where does this quarter’s guidance sit relative to analyst estimates? The third stage is synthesis: taking everything gathered and forming a view — is this a good investment, at what price, under what assumptions?

An agent handles the first two stages well. The third requires judgment.

What an Agent Can Do

An agent can pull a company’s recent SEC filings, extract key financial metrics, and organize them into a consistent format. Give it a ticker and it can return the last four quarters of revenue, operating income, and free cash flow with the specific line items sourced from the actual filings.

It can also handle the comparison work. Run the same data pull on five companies in the same sector and you have a peer set. The agent doesn’t interpret whether the differences matter; it produces the table. An analyst reads the table.

News monitoring is another clear use case. An agent can check recent news coverage for a company, surface any regulatory filings or material disclosures from the past week, and flag anything that has changed since the last time you looked. This kind of standing watch — run daily or weekly — keeps a portfolio under continuous light coverage without requiring someone to read every headline.

Earnings preparation is where the time savings compound the most. Before an earnings call, an analyst typically re-reads the last quarterly filing, reviews consensus estimates, checks recent news, and pulls together a comparison against prior quarters. An agent can do that preparation in minutes, returning a structured briefing document: current consensus, prior-quarter actuals, year-over-year comparisons, and any notable recent news items. The analyst reads the briefing instead of assembling it.

What Agents Don’t Do

An agent won’t tell you whether a stock is worth buying. That judgment requires things agents don’t have: a view on valuation, an opinion about management quality, a sense of what the market is missing, a thesis about why the next two years will be different from the past two.

What an agent produces is organized information. It reduces the time between “I want to research this company” and “I have the facts in front of me.” The analysis that follows is still yours.

There’s also a reliability consideration. Financial data sourced from public filings is generally reliable, but always worth verifying. An agent can misread a table, extract a number from the wrong period, or pull from an amended filing rather than the original. Treat agent-generated financial summaries as a starting point for your own reading, not a replacement for it.

Building a Research Workflow

A practical investment research workflow using agents looks like this:

  1. Initial filing pull: retrieve the most recent 10-K and last two 10-Qs for a company, extract key financial metrics across periods.
  2. Peer comparison: run the same pull on 3–5 comparable companies, build a comparison table of revenue, margins, and growth rates.
  3. News sweep: search for news coverage over the past 30–90 days, surface any material disclosures or significant headlines.
  4. Earnings brief: before quarterly results, pull consensus estimates alongside the most recent quarter’s actuals for side-by-side context.
  5. Ongoing monitoring: run a lighter version of steps 1 and 3 weekly for positions you hold, flagging anything material that changed.

Each step is information retrieval and organization. The research synthesis happens after.

AgentPatch

AgentPatch provides the tool layer for this workflow. The sec-company-financials tool pulls financial statements directly from SEC EDGAR — income statements, balance sheets, cash flow statements across reporting periods. The sec-search tool finds company filings by name or ticker. The google-news tool returns recent news coverage for any company or topic with timestamps and sources. The google-search tool handles broader research: finding analyst commentary, industry reports, regulatory context.

One API key connects all of these to your agent, with no separate credentials per data source.

Setup

Connect AgentPatch to your AI agent to get access to the tools:

Claude Code

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

OpenClaw

Add AgentPatch to ~/.openclaw/openclaw.json:

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

Get your API key at agentpatch.ai.

Wrapping Up

The bottleneck in investment research isn’t usually analysis — it’s the time spent gathering data before analysis can start. An agent that handles the retrieval step returns that time. The analyst still forms the view; the agent handles the reading. If you want to build that workflow, agentpatch.ai has the data tools to connect.

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