B2B research often begins with a simple question: which companies fit our target market, and what public signals suggest that now is a relevant time to speak with them? BrowserAct Agent is built for that kind of prompt-shaped research question. Answering it manually can mean moving between search results, company pages, public posts, job pages, and websites. BrowserAct can turn those repeated browser steps into a tested Workflow, so researchers do not rebuild the same account-research spreadshe

BrowserAct fit: Agent, Workflow, or CLI?

What a useful prospect research workflow should answer

A good B2B dataset is not simply a long list of names. BrowserAct Agent should be used to build a relevance table with source evidence, not an indiscriminate people-harvesting tool.

The product split matters here: use BrowserAct Agent to test whether the research brief can be answered from allowed public pages, BrowserAct Workflow to reuse the approved fields across segments, and BrowserAct CLI when RevOps needs the same run inside a controlled data process.

Depending on your permitted data sources and purpose, useful questions may include:

• Can BrowserAct identify whether the company operates in the target industry or geography?
• Can BrowserAct capture whether its public company description matches the target use case?
• Can the BrowserAct Workflow record the company size range that is publicly displayed?
• Can BrowserAct collect public hiring or initiative signals related to the problem you solve?
• Can BrowserAct preserve the company URL and source page that support each field?

For person-level research, configure BrowserAct with the minimum information needed for a legitimate business purpose. Avoid sensitive traits, private contact details, inferred attributes, or fields that are not necessary for the workflow.

Example prompt for company and account research

For BrowserAct, the prompt is both the build brief and the safety boundary. It tells BrowserAct Agent what to test, gives the future Workflow its stable inputs, and gives CLI-triggered runs a clear contract to follow.

Start from the authorized public search or list page I provide.
 
Find companies that match these inputs:
- industry: B2B software
- region: United States
- displayed company size: 11-200 employees
- result limit: 50 companies
 
For each company, return only publicly displayed business information:
- company name
- company page URL
- public website URL
- industry
- headquarters or location
- displayed company size
- public company description
- latest public post date, when visible
- source URL for each record
 
Open the company detail page when needed.
Do not collect personal email addresses, phone numbers, sensitive traits,
or information that is not publicly accessible.
 
Return one structured row per company.

This prompt separates filters from fields and adds an explicit data-minimization rule. If the BrowserAct Workflow later needs a different region, industry, or company-size band, those values can become Bot inputs.

How Agent-built turns the prompt into a reusable Bot

After you submit the request, BrowserAct Agent confirms the plan and interprets the website, conditions, and required fields. It can explore supported filters, tabs, scrolling, pagination, and detail pages in a real browser, then test the extraction against the live site.

During construction, BrowserAct Preview lets you inspect the browser operation. If the Agent requests more information, provide the missing page, condition, field, or expected result. You can also pause construction when you need to add context.

After a successful test, the Bot becomes a reusable BrowserAct Workflow. The next run can use different input parameters while preserving the same research schema; technical teams can later call that workflow through BrowserAct CLI.

A practical B2B research schema

Treat this schema as the contract between BrowserAct Agent, the saved BrowserAct Workflow, and any later BrowserAct CLI run. If a field is not needed for review, leave it out before the workflow becomes repeatable.

Three workflows you can build from the same foundation

Once BrowserAct Agent has tested the browser path, the same foundation can become several BrowserAct Workflows. Choose the shape based on how often the research repeats and whether the output needs to enter another system through BrowserAct CLI.

1. Target-account discovery

Run the BrowserAct Workflow for a defined industry, region, and size band. Send the structured output to an approved review queue. A person can then validate fit before any outreach.

2. Account refresh

Use a known list of public company URLs as inputs. Re-run the same BrowserAct fields periodically to identify material public changes, such as a revised company description or a newly visible hiring initiative.

3. Market mapping

Collect a consistent set of public business fields across several categories. BrowserAct helps compare how companies position themselves, which terms appear repeatedly, and where the market looks crowded or underserved.

These BrowserAct workflows are more defensible than indiscriminate harvesting because the scope, purpose, fields, and stopping conditions are defined before the Bot runs.

Cloud runs and downstream integrations

A tested Bot can run from the BrowserAct Dashboard as a repeatable Workflow. Choose the browser mode and proxy region appropriate for the target market, adjust input values, start the run, and review the structured result. BrowserAct run history provides a record for checking previous executions.

When the workflow is ready to connect to another approved system, BrowserAct supports:

• BrowserAct Agent: for teams that want to describe new B2B research tasks in plain English.
• BrowserAct Workflow: for repeatable company refreshes, market maps, and account-research runs.
• BrowserAct API / BrowserAct CLI: for custom applications, backend services, scripts, and code-controlled integrations.
• Make, n8n, or Zapier: for visual workflows with little or no custom code.

For example, a BrowserAct Workflow could populate an internal research table. Keep human review between BrowserAct data collection and any consequential outreach, scoring, or account decision.

Why prompt-built research is different from a fixed template

A fixed template is fast when your use case matches its predefined fields. BrowserAct Agent is useful when the research task includes custom filters, multiple pages, or a schema specific to your team.

BrowserAct Agent-built can turn a precise request into a tested Workflow while still giving you a reusable result. You do not need to translate every field change into selectors or browser code.

That flexibility matters when:

• A segment uses different filters, and BrowserAct Agent needs to test them before reuse.
• A field appears only on a detail page, and BrowserAct must preserve the source URL.
• The research process combines list and company-page information inside one BrowserAct Workflow.
• The same task must run for several regions through Dashboard, Workflow, or CLI.
• The website changes and BrowserAct needs a tested update.

Responsible professional-data research

Professional platforms have strict rules and evolving access controls. Before running BrowserAct automation, confirm that the collection method, account, pages, purpose, and fields comply with the platform’s current terms, robots rules, privacy requirements, and applicable law.

Put those boundaries directly into the BrowserAct Agent prompt, keep them in the saved Workflow, and make sure any BrowserAct CLI integration preserves the same allowed fields and human-review step.

Use BrowserAct for authorized public data only. Do not bypass authentication or access controls. Do not collect private contact information, sensitive personal data, or unnecessary person-level fields. Do not use BrowserAct output for discriminatory decisions, surveillance, spam, or automated high-impact profiling.