Collect structured web data
Run configured Apify actors when a research task needs repeatable web data collection.
All AI employees Inspectable
Research
Research Agent
Run structured research that keeps the source context, turns findings into reusable data and gives other AI employees a better evidence base for their next action.
Research run
Research Agent: inspectable by design
- 1
Frame the question
The next step runs only with the tools and permissions assigned to this agent.
- 2
Collect source data
The next step runs only with the tools and permissions assigned to this agent.
- 3
Separate evidence from inference
The next step runs only with the tools and permissions assigned to this agent.
- 4
Structure the result
The result and tool activity remain available in the run history.
Tools are scoped
Writes can require approval
Run history retained
Best fit
Who this role is built for
- Market research teams
- Founders validating a market
- Sales and marketing teams that need source-backed briefs
Jobs to be done
What the Research Agent actually handles
Specific operational work, tied to the tools and controls available in Enverif — not a generic chatbot job description.
Research companies and places
Combine available business, place and public-source context without filling gaps with invented facts.
Build comparison briefs
Turn gathered evidence into a concise comparison or decision brief.
Preserve reusable findings
Write structured outputs to Sheets and retain the run context for later workflows.
Workflow
A run you can follow from goal to outcome
Each stage stays inspectable, and external writes can stop at an approval gate before anything changes outside Enverif.
- Step 1
Frame the question
Define what would count as evidence and what sources or tools the agent is allowed to use.
- Step 2
Collect source data
Use assigned research connectors such as Apify or Google Maps where relevant.
- Step 3
Separate evidence from inference
Keep unknowns explicit and avoid converting assumptions into facts.
- Step 4
Structure the result
Produce a table, brief or reusable record with the source context retained in the run.
- Step 5
Hand off the evidence
Give downstream employees the research rather than forcing them to repeat the collection step.
Example tasks
Give it a real objective
- “Research these companies and build a comparison table using only evidence you can source in this run.”
- “Collect the relevant market data with the configured Apify actor and summarize the patterns without inventing missing fields.”
- “Find local competitors in these locations using Google Maps and prepare a source-backed market brief.”
Extensions
Use the tools this role actually needs
Built-in integrations used by this role
MCP when the built-in catalog stops
If a research source is not built in, a compatible MCP server can expose it to the workspace while preserving the same scoped-tool model.
How Enverif uses MCPControl
Autonomy is a policy, not an assumption
Enverif records the run and lets you govern external writes through per-agent approval policy. Start with review required, inspect the history, then deliberately grant more autonomy only where it makes sense.
FAQ
Research Agent questions
Does the Research Agent cite every statement automatically?
The product should retain source context available to the run, but the site does not claim a universal citation guarantee for every model output.
Can it scrape the web?
Apify is a built-in connector that can run configured actors for structured web data collection.
Can the research be reused?
Yes. Results can be structured into connected data stores such as Google Sheets and retained in Enverif run/knowledge workflows.
Put your first AI employee to work this week.
Start on Enverif Cloud, or clone the repository and self-host. Approvals are on by default either way.