The Deep Research node, in beta, sends a research agent powered by Parallel to the live web from a Default workflow and returns structured, typed fields.
The Deep Research node in Default workflows sends a research agent to the live web and
returns what it finds as structured fields. It is powered by Parallel, a
web research engine built for AI agents.Use it when the answer is missing from your CRM and from enrichment providers’ databases, for
example a company’s growth priorities, the roles on its careers page, or whether its site has a
live pricing page.
Add the node in the Workflows builder with Add Node.Deep Research is in beta. If you do not see it in the picker, contact support.
The AI Prompt node and the Deep Research node both take a prompt. They do
different jobs.
Node
What it works with
Use it to
AI Prompt
Data the workflow already has.
Summarize, classify, rewrite, or extract from trigger data and earlier nodes.
Deep Research
The live web.
Find net-new information that is not in your workflow data or your enrichment providers.
AI Prompt returns in a few seconds. Deep Research browses real web sources, so a run takes
longer: several seconds for a narrow question, and noticeably longer for a broad one. Plan
node order with that in mind.
Output variables are how the node returns data. Each one becomes a typed field in the result,
and a value later nodes can pick from the variable picker. The node requires at least 1.
1
Create a variable in the prompt
In Research prompt, type {{ and select Create output variable.
2
Name it and pick a type
Give it a clear name, such as fitVerdict, and a type: String, Number,
Boolean, URL, or Currency.
3
Tell the agent how to fill it
In the prompt, state exactly what the variable must contain. For a fixed set of answers,
list the permitted values: “Set fitVerdict to exactly 1 of: Strong Fit,
Disqualified, Human Check.”
4
Use it downstream
Later nodes pick each variable directly. Write it to a CRM field, include it in a Slack
message, or branch on it with a Multi-Branch node.
The result contains exactly the variables you declared. If the agent returns a declared field
with the wrong type, or misses one, the node reports a failed result instead of passing bad
data downstream.
A common pattern is a research verdict that routes the workflow. Declare a String variable for
the verdict, constrain it to a fixed set of labels in the prompt, and add a Multi-Branch
node after Deep Research with 1 branch per label.Keep the branch that matches nothing pointed at a safe path, such as human review. Research
output comes from a language model, so an unexpected value should route somewhere deliberate
rather than fall through.
Use Deep Research for niche data. Standard firmographic fields, such as funding stage,
employee count, LinkedIn URL, and founding year, come faster, cheaper, and more consistently from
an enrichment provider. Save the research agent for questions only
the live web can answer.Be specific. Research agents perform best on narrow, well-defined questions. Say what to
find, which sources count, and what shape the answer takes. A prompt that asks for 1 thing
precisely beats a prompt that asks for 20 things loosely.Declare more variables instead of a single text answer. Each typed variable pins down part
of the answer and leaves the agent less room to improvise. A single freeform summary invites
drift, and 10 narrow fields constrain it.Avoid ambiguous asks. A variable like “recent news” has many possible shapes and sizes.
Constrain it: which time window, what counts as news, how many sentences, what to return when
there is nothing.Standardize the format in the prompt. State character limits, permitted labels, and the
exact fallback value for “not found”. Results depend on publicly available web information, so
a workflow that needs a consistent downstream format needs a precise prompt, a defined output
schema, and a test with representative inputs.Test before you scale. Run the workflow’s Test mode on a handful of representative
records and read the results in the run logs before pointing real
traffic at it.Place it with latency in mind. In a form-triggered workflow that shows a scheduler, a
research node between the submission and Display Scheduler delays the booking page by the
length of the research run. If you research before scheduling to qualify or route, keep the
prompt narrow; otherwise run research after the meeting is booked.
If the research request fails, the node does not stop the workflow. It returns a failed result
and the run continues, the same way the AI Prompt node does. A later
Multi-Branch node can react to the failure.The run log records the result of each research run, including why a
run failed.
The Deep Research node in Default workflows sends a research agent to the live web and
returns what it finds as structured fields. It is powered by Parallel, a
web research engine built for AI agents.
Use it when the answer is missing from your CRM and from enrichment providers’ databases, for
example a company’s growth priorities, the roles on its careers page, or whether its site has a
live pricing page.
