Five stages. One pipeline. Every workflow runs through it.
Axonity isn't a grab-bag of disconnected automation tools. Every workflow — simple or complex — moves through the same five stages, which is exactly what makes a three-month-old automation still debuggable today.
Trigger
A workflow starts from something real — a new form submission, an incoming email, a database change, a webhook, or a schedule. No polling, no delay waiting for the next sync.
- Event-based triggers fire in real time
- Scheduled triggers down to the minute
- Manual triggers for one-off runs
Connect
Once triggered, a workflow can reach into any connected system — pull a record, check a condition, fetch context an AI agent will need to make a decision later in the pipeline.
- Hundreds of pre-built app connections
- Generic REST and webhook connectors for anything else
- Credentials encrypted and scoped per workspace
Decide
This is where Axonity differs from a basic automation tool. An AI agent can read the context gathered so far and make an actual judgment call — not just pass data through unchanged.
- Agents configured with your own rules and examples
- Confidence thresholds that route to a human when unsure
- Every decision logged with the reasoning behind it
Act
The workflow takes the action — sending a message, updating a record, creating a ticket, calling an API — in whichever connected system needs to receive it.
- Multiple parallel actions from a single decision
- Conditional branching based on the outcome
- Built-in retries for transient failures
Observe
Every run is recorded — trigger, context, decision, action, and timing — so when something looks wrong, you're debugging with real data instead of guessing.
- Full run history, searchable by workflow or outcome
- Replay any past run against an updated workflow
- Alerts routed to Slack, email, or a webhook on failure
What a real pipeline looks like
New lead synced to HubSpot
Website form → CRM
Invoice reminder sent
Billing → Email
Support ticket triaged by agent
Inbox → AI Agent → Slack
Nightly report generation
Scheduled → Sheets
<400ms
Median trigger-to-run latency
10k+
Concurrent workflow runs per workspace
3x
Automatic retries on transient failures
99.95%
Trigger reliability, last 90 days
Rule-based automation vs. an Axonity agent
Most automation tools are good at the “Connect” stage and stop there. The difference shows up the moment a workflow needs to decide something, not just move it.
| Rule-based tools | Axonity agents | |
|---|---|---|
| Moves data between systems | ||
| Follows a fixed if-this-then-that path | ||
| Makes a judgment call from context | ||
| Breaks silently when the input looks different | ||
| Hands off to a human when it isn't confident | ||
| Explains the reasoning behind its decision |
Autonomy with a leash you control
Giving an AI agent the ability to act is only safe if you can see what it did, stop it from doing the wrong thing, and hand off the moment it isn't sure.
Confidence thresholds you set
Every agent step has a configurable confidence floor. Below it, the workflow pauses for a human instead of guessing and moving on.
Human approval on anything sensitive
Mark any step — sending an email, charging a card, deleting a record — as requiring sign-off before it runs, no matter how confident the agent is.
Full audit trail, not a summary
Every decision an agent makes is logged with the exact input it saw and the reasoning it produced, searchable months later.
Visual by default, code when you need it
Most workflows never need a line of code. When one does — a transform the visual builder can't express, a call to an internal service — the platform doesn't fight you on it.
- REST API for triggering, pausing, and inspecting any workflow
- Custom code steps in JavaScript or Python when a visual step isn't enough
- Signed webhooks with replay protection for inbound triggers
- Sandbox workspace for testing workflow changes before they go live
Before you wire up something complex
What happens if a step in the middle of a workflow fails?+
The workflow retries automatically up to three times with backoff. If it still fails, the run is marked failed, logged in full, and an alert goes to whichever channel you've configured — nothing fails silently.
Can an AI agent step call our own internal systems?+
Yes, through the REST and webhook connectors, or a custom code step if you need something more specific than a standard HTTP call.
How do you prevent an agent from doing something irreversible by mistake?+
Confidence thresholds and human-approval steps are the two main controls. You can also run any workflow in a sandbox workspace against real data without it touching production systems.
Is there a limit to how complex a workflow can get?+
No hard limit on steps or branches. In practice, most workflows that get too complex to reason about are better split into two connected ones — the platform supports that pattern directly.
Want to see your own workflow mapped out?
Tell us the systems you're trying to connect and we'll sketch the pipeline before you commit to anything.