Marketing and business leaders spend four to eight hours a week on competitive and prospect research that an AI agent can now handle in thirty minutes. This is the walkthrough for setting that up without writing code or hiring anyone.
By the end of this article, you will have a working setup that runs your competitor or prospect research on demand, returns a structured brief in minutes, and costs somewhere between nothing and thirty dollars a month depending on which path you take.
That outcome is worth pausing on. Not because the tools are impressive, but because of what it replaces. Four to eight hours a week is the range most marketing and business development leaders report spending on research tasks that feel like they should be automated but never quite were. Searching ten competitor websites for pricing changes. Pulling context on a prospect before a call. Scanning a handful of company pages to understand what a new market segment is saying. The work is not hard. It is just slow, repetitive, and easy to let slide when the week gets busy. An AI agent can run it for you, in the background, and return something you can actually use.
This is the walkthrough.
What an AI research agent actually does
The thing worth understanding before you set anything up is what is happening under the hood, stated simply enough to be useful.
An AI research agent is a combination of a language model and the ability to browse the web. You give it a task in plain English. It opens pages, reads them, follows relevant links, extracts the information you described, and returns a structured summary. It does not need you to specify which URLs to check. You tell it what you want to know, and it figures out where to look.
This is different from asking an AI chat tool a research question. When you type "what is [competitor] charging for their pro tier" into a chat window, the model answers from its training data, which may be months out of date. An agent actually visits the page. It reads what is there right now. The distinction matters for anything that changes, which is most of what you want to track.
According to research on how businesses are deploying these tools in 2026, one B2B company cut weekly competitive intelligence gathering from eight hours to thirty minutes using a browser agent. That is not a cherry-picked case. It is representative of what happens when you replace a recurring manual task with something that runs automatically against a defined set of sources.
Two paths: no-code and low-code
You have two realistic options here, and the right one depends on how much you want to configure upfront versus how flexible you need the output to be.
Path one: No-code agent builder (recommended starting point)
The fastest way to get this working without any technical setup is Lindy, a no-code AI agent platform that has become the go-to tool for this use case in 2026. Lindy offers pre-built research agent templates you can activate and customize in about twenty minutes. The competitive research template, for example, takes a list of competitor names, visits their websites and recent press releases, and returns a structured brief covering pricing, positioning changes, new feature announcements, and anything that looks like a strategic shift.
You do not write code. You open the template, tell it which companies to track, tell it how you want the output formatted, and connect it to whatever you want to receive the brief. Slack message, email, or Google Doc, your choice. Lindy integrates with over four thousand tools, so wherever your team already reads things, the brief can land there.
The free tier is usable for light weekly research. Paid plans start at around fifty dollars a month and support more frequent runs and longer output.
Path two: browser-use for custom workflows
If you have someone on your team who is comfortable running a Python script, even without being a full engineer, browser-use is the open-source library that makes AI agents genuinely good at this. It has 83,000 GitHub stars as of this week and is one of the fastest-moving open-source projects in the AI space right now.
The setup takes about thirty minutes. You install the library, connect your API key from any major model provider (Claude, GPT-4, Gemini, your call), write a task in plain English, and run it. Here is what a competitive pricing research task looks like:
from browser_use import Agent
from langchain_anthropic import ChatAnthropic
agent = Agent(
task="Go to the pricing pages of Notion, Coda, and Confluence. Extract the current pricing for each plan tier. Return a comparison table with plan name, monthly price, and key features included.",
llm=ChatAnthropic(model="claude-opus-4-8"),
)
import asyncio
asyncio.run(agent.run())That is the whole script. The agent opens each page, reads the pricing, and returns the table. You can make the task as specific or as broad as you want. You can tell it to also check the "what's new" page on each site. You can ask it to flag anything that looks like a price change from what it found last time if you store the previous run's output.
The cost here is just the API calls, which for a task like this will run somewhere between two and ten cents per research run depending on how many pages it visits.
What to actually research
The setup is the easy part. The more important question is what you point this at. Four tasks cover most of what business leaders automate once they have an agent running.
Competitor pricing and positioning. Weekly runs against your top three to five competitors. Track pricing page changes, headline messaging updates, and anything new in their "about" or use-case pages. Positioning shifts often appear there before any press release.
Prospect research before calls. The agent visits a prospect's website, recent press releases, and relevant news, and returns a one-page brief with company overview, recent announcements, and likely priorities. Lindy has a pre-built template for exactly this.
Job posting intelligence. A competitor's job postings signal where they are investing before any official announcement. An agent that checks their careers page weekly and flags new roles by department gives you a leading indicator that press releases never will.
News and mention monitoring. Point an agent at Google News for your brand, your competitors, and two or three relevant industry terms. Have it return a morning brief of anything that appeared in the last twenty-four hours. This replaces Google Alerts, which has never returned structured, actionable summaries.
The pitfall: too broad, no output format
The most common mistake in setting up a research agent is writing a task that is too vague and leaving the output format undefined.
"Research our competitors" is not a task. It is a category of work. The agent will produce something, but it will be formatted differently every time, at varying levels of depth, and probably longer than useful. You will spend more time editing it than you saved on research.
The better pattern is to write the task the way you would write a brief for a junior analyst on their first day. Name the specific companies. Name the specific questions. Describe the output format you want. "Return a table with three columns: company name, current pricing for the mid-tier plan, and any message I see on the pricing page about a free trial or recent promotion." When you are that specific, the agent produces something you can paste directly into a Slack message or slide and move on.
Research on agent performance in 2026 consistently shows that task specificity is the dominant variable in output quality. The model is capable enough. The instruction is almost always the bottleneck.
You can try this today
If you want to start with zero setup: open Claude or ChatGPT, enable the web browsing feature if it is not already on, and paste this task:
"Visit the pricing pages for [Competitor A], [Competitor B], and [Competitor C]. Return a comparison table with plan names, prices, and the key features listed at each tier. Note any free trial offers you see."
That is not an agent in the full sense. It is a web-browsing AI session. But it will return something in two to three minutes that would have taken you fifteen to twenty to compile manually, and it is a useful proof of concept before you invest in a more permanent setup.
If you want the permanent setup: create a free Lindy account, find the competitor research template in their library, and spend twenty minutes configuring it with your actual competitor list and your preferred output format. Schedule it to run weekly and deliver to your Slack channel. That is the whole project.
If you have a technical person available for thirty minutes: have them install browser-use, run the pricing comparison script above with your actual competitors, and look at what comes back. From that point, the task description is the only thing you need to modify to redirect the agent at any research problem you have.
What changes when you automate this
The compounding benefit of running research through an agent is not just time saved per run. It is that you actually run it consistently.
Most competitive intelligence efforts collapse within a few weeks because the manual work produces inconsistent value. You check competitor pricing when something prompts you to, not on a schedule. You prep for calls when you have time, not for every meeting. The gaps in coverage are where you get surprised.
An agent that runs on a schedule, against a defined list, and returns a brief in a predictable format changes that relationship entirely. It stops being a research task and becomes a standing briefing. The information is there when you need it because it was collected whether or not you had time to collect it.
As of this week, browser-use crossed 83,000 GitHub stars, Lindy has a growing library of no-code research templates, and 27.7% of enterprises are already running browser agents in production. The setup barrier is low enough now that not having this running is a choice, not a constraint.
The research your team keeps saying it will get to is the research your AI agent should already be running.