Some work is bigger than one prompt
A great agent run gets you a great deliverable. But some of the most valuable work in a team has stages — research, then draft, then critique, then a final polish — and trying to do all of it in one prompt produces uneven results. Each stage needs a different specialist with a different mindset.
That’s the work VDF AI Networks is built for.
A network is a guided workflow. Each step has one job. The output of one step becomes the input of the next. You watch the result build, stage by stage, until a polished result lands at the end.
Mental model. An agent is one specialist doing one job well. A network is a coordinated team of specialists doing a complete piece of work end-to-end — every time, the same way.
Who VDF AI Networks is for
Networks are most valuable to people who own recurring, multi-stage work — the kind of work where the steps are well understood but tedious to run by hand each time.
- Product leaders
- Strategy & corp dev
- Customer success
- Engineering managers
- Operations
- Research analysts
- Procurement
- Marketing leads
Good fits:
- A recurring report that always pulls from the same sources and follows the same shape.
- A multi-step review process — a draft, a critique, a final polish.
- An analysis that benefits from several perspectives before producing one recommendation.
- An onboarding or evaluation flow you run for every new customer, vendor, or initiative.
When to choose Networks (and when not to)
| Task shape | Best tool |
|---|---|
| Exploring a problem from scratch | VDF AI Chat |
| A single, focused deliverable in a known format | VDF AI Agents |
| A multi-stage workflow that combines specialists | VDF AI Networks |
| A document-heavy task that depends on your sources | VDF AI Data (often paired with Networks) |
A useful self-check: if you find yourself chaining two or three agents by hand for the same recurring task, that’s a network waiting to be built.
What you can do with a network
Coordinate specialists in sequence
A research step, a drafting step, a critique step, a final polish — each handled by the right specialist with the right tone.
Standardize a team process
The Monday report, the customer onboarding brief, the vendor evaluation — built once, run forever.
Combine several perspectives
Run a draft past a critical reader, an editor, and a fact-checker before producing one final version.
Pull from multiple sources in one run
Each stage can reach into different files, folders, or connected apps without losing the thread.
Watch the work build
See intermediate outputs at every stage. Stop the run if something's off — adjust and continue.
Reuse with confidence
Save a working network. Anyone on the team can run it with their own inputs and get the same shape of result.
How a network actually works
Two ways to think about it:
As a series of stages
A network breaks a big task into stages. Each stage is one focused step:
- Research — pull together what’s known.
- Draft — turn the research into a first version.
- Critique — read the draft like a tough reviewer.
- Final — fold the critique back into a clean output.
You see what comes out of each stage. If the research stage missed something, you can adjust before the draft stage even starts.
As a coordinated team
Each stage uses a specialist suited to that step. The research stage uses a research specialist. The draft stage uses a drafting specialist. The critique stage uses a critic. You don’t have to manage them individually — the network does.
You don't have to build a network to start using one. Most teams begin with templates — networks that the platform ships ready to use. You give the inputs, the network runs, you get the result. You can build your own later when you find a recurring pattern of your own.
Key concepts, in plain language
-
Network.
The whole workflow — the staged process that takes inputs in and produces a polished output.
-
Stage.
One focused step in the workflow — research, draft, critique, polish. Each stage has a clear job.
-
Inputs.
What you give the network at the start: a goal, source material, files, audience, deadline.
-
Intermediate outputs.
What each stage produces — visible to you as the network runs, so you can spot a problem early.
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Final result.
The polished output at the end. Usually a draft, a brief, a plan, a comparison, or a structured report.
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Run.
A single execution of a network from start to finish — with its own inputs, intermediate outputs, and result.
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Template.
A network the platform ships ready to use. You don't have to build anything — pick a template and run it.
How VDF AI Networks fits with the rest of the platform
- VDF AI Chat is where you usually explore the problem first. Once a shape is clear, run a network.
- VDF AI Agents is the right tool when only one specialist is needed. Networks coordinate several.
- VDF AI Data is where your sources live — networks often pull from Data stages to ground their work.
What to read next
- Getting started with networks — run your first network from a template.
- How networks work — stages, intermediate outputs, and reviewing a run.
- Node types — the building blocks every network is made of.
- Triggers and schedules — manual, scheduled, webhook, and event triggers.
- Tool catalog — the actions a network can take.
- Runs and monitoring — what you see while a network runs and after.
- Versions and templates — save, share, and roll back.
- Smart model routing — let VDF AI pick the right model per step.
- Policies and budgets — guardrails for shared workflows.
- Building workflows — when and how to turn a recurring team process into a saved network.
- Governance and admin — for workspace admins.
- Use cases — concrete worked examples.
- FAQ — common questions and choosing between Chat / Agents / Networks.