"AI Native" is a term gaining currency, and everyone seems to mean something different by it. This page offers a workable definition, a test to apply, and a five-question self-assessment.
AI Native describes how business processes run, not how many AI tools a team has bought. To judge whether a team is AI Native, check the three behaviors below.
Business processes run automatically on schedules or events; people no longer drive each step — they approve critical actions and handle exceptions.
What each step did, its inputs and outputs, and what it cost can all be reviewed after the fact — when something breaks, you can pin it to the exact step.
The process is written as a shared definition that a new teammate can read and take over; it isn't locked inside someone's chat history.
Run both hypotheticals and compare which hurts the business more.
Suppose you take away the chat account of your most AI-savvy teammate. The usual result: their productivity drops, and the business itself carries on as before.
Suppose the workflow platform your team relies on goes down for a day. If the morning report doesn't go out, pending changes pile up unapproved, and scheduled jobs all stall, your business processes are already running on AI.
The gap between the two experiments is the gap between "using AI" and AI Native: a chat account is a personal tool — taking it away affects one person's productivity; a workflow platform carries business processes — stopping it affects the whole team's output.
The more "yes" answers, the closer you are to AI Native; mostly "no" means AI is still stuck at the personal-tool stage.
If the automation platform your team uses went down for a day, would any business process be affected?
Can you look up exactly how much AI spent on a given process last month?
Before AI changes budgets, publishes content, or writes production data, is there a mandatory human approval step?
When an AI run produces a wrong result, can you locate the failing step and see its inputs and outputs at the time?
If the person who built a process takes two weeks off, can others read it and confidently change it?
How the same task is handled under the two ways of working.
| Dimension | a Team Using AI | an AI Native Team |
|---|---|---|
| Capability entry point | Everyone's own chat window | A shared team workflow platform, where every process has a name and a definition |
| Preserving Know-how | Good prompts live in personal bookmarks | Prompts are written into the workflow definition; the whole team uses the same version |
| Failure Handling | Rerun by hand and try rephrasing the question | Check the run record, locate the failing step, and rerun from there |
| Audit | Dig through chat history when something goes wrong | Every run has logs; critical changes have approval records |
| Cost Accounting | One aggregate subscription bill at month end | Token usage and cost recorded for every single run |
| How you scale | Hire another person who's good with AI | Clone a workflow, swap the parameters, and run a second line of business |
Under each one is the corresponding mechanism in Braidrun — you can verify every item after signing up.
A process that exists only in one person's habits can't be reviewed or handed over. It needs a definition the whole team can open.
Each workflow is a YAML definition, with canvas and code in two-way sync; 8 step types cover single agent, code, classifier, multi-agent discussion, state machine, and sub-workflows.
AI gets things wrong. Actions like changing budgets, publishing content, or writing production data should pause for human confirmation before executing — and the confirmation step has to live in the process definition, or it will get skipped.
A manual_approval step pauses the run and notifies approvers in-app, by email, or via API; on approval it continues, on rejection or timeout it stops and nothing changes in production. Values in the approval form can be edited directly — say, lowering an AI-suggested bid before approving.
AI output can differ from run to run. When something goes wrong you need answers: which step failed, what were its inputs and outputs, and what did this run cost.
Every run has a timeline recording logs, token usage, and cost per step, and can be exported as JSON or YAML; after a failure you can rerun from a chosen step, and completed LLM steps aren't billed again.
Models move fast — today's right choice may change in six months. Keep process definitions separate from models: swapping a model shouldn't mean rewriting the process.
Bring your own model API Key — 10+ providers plus local Ollama and LM Studio are supported, and each agent in a workflow can use a different model; credentials are stored with AES-256-GCM encryption, and self-hosted deployment is supported.
See how the platform implements the four requirements on this page: process definitions, approval gates, run records, and model choice.
A subscription app team manages Apple Search Ads with AI agent workflows: an agent reviews up to 5,000 keywords one by one, and humans only approve bid changes.
The free tier starts from templates; demo mode runs your first process without connecting credentials.