Ops team receives AI task completion alerts
When an AI employee finishes a workflow, it fires a webhook to the team's internal system, triggering the next step automatically.
Outbound Webhooks
Connect a webhook URL, your own backend, a Zapier or Make scenario, a Slack incoming webhook, anything that accepts an HTTP POST, and your employee gets a tool it can call on its own during a task. It sends a JSON payload to that URL, so the rest of your stack finds out the moment the work is done instead of you checking manually. Because the call sends data outside Sistava, each send waits for your approval by default, the same as an outbound email.
Your employee pushes events to your systems in real time. When a task completes, your project management tool gets notified. When an approval is needed, your Slack channel gets a message. When a scheduled execution finishes, your data pipeline picks up the output.
Configure which events trigger webhooks and where they go. Route task completions to one endpoint, approval requests to another, and error notifications to a third. Each webhook includes a full payload with context: what happened, which employee did it, and what the output was.
Webhooks are especially valuable for building automation pipelines. Employee researches leads, webhook notifies your CRM, CRM triggers an email sequence, email results webhook back to the employee for follow-up. Closed-loop automation with your AI employee at the center.
Push Events From Your AI Employee to External Systems
Sistava supports outbound webhooks, meaning your AI employee can notify external systems when key events happen. When a task completes, a document is created, or an approval is needed, the agent fires a webhook to the URL you configure.
This turns your AI employee into a live trigger in your automation stack. Connect it to Zapier, Make, n8n, your own backend, or any system that accepts webhooks, and let the agent kick off downstream workflows without any manual polling or checking.
Configurable Payloads and Event Types
You define which events trigger a webhook and what data is included in the payload. Whether you want a summary of the completed task, a structured JSON object with specific fields, or a raw output from a tool call, the agent sends exactly what you configure.
Custom payloads mean your downstream systems receive data in the format they expect, not a generic dump. This reduces the need for transformation logic on the receiving end and makes webhook-driven workflows more reliable.
Bridge Your AI Workforce Into Any Automation Stack
Many teams already have automation infrastructure built around webhooks: workflow platforms, internal event buses, monitoring systems. Outbound webhooks let your AI employees participate in those existing systems without requiring a full architectural change.
An autonomous agent that completes a research brief can fire a webhook to create a Notion page. An agent that processes an invoice can trigger a payment workflow. The webhook is the handoff point between your AI workforce and the rest of your operations.
How It Works
Your AI agent sends structured event payloads to external URLs whenever defined triggers occur, keeping your systems in sync without polling.
Define outbound webhooks by selecting a trigger event, task completed, approval requested, message sent, credit threshold crossed, and providing a destination URL. When the trigger fires, the agent assembles a structured JSON payload and POSTs it to your endpoint immediately. You control which events fire webhooks and what each payload includes.
This is how AI agents become first-class participants in your event-driven architecture. Your internal systems do not need to query Sista for updates. They receive them. A task completion triggers a record update in your database. An approval request sends a formatted alert to your on-call channel. An agent reaching its credit limit triggers an automated recharge or escalation. Outbound webhooks make the AI agent a live data source, not a system you have to check manually.
Use Cases
CRM team syncs AI outputs in real time
Each time the AI agent completes a lead enrichment, an outbound webhook pushes the result directly into the CRM, no polling required.
Dev team monitors agent errors in their own stack
When an AI agent hits a failure, an outbound webhook fires to the team's alerting system, creating an incident ticket immediately.
Finance team updates records as AI closes tasks
Each invoice processed by the AI employee fires a webhook to the accounting system, keeping records in sync without manual exports.
Comparison
| Before | After |
|---|---|
| External systems have to poll the platform to detect completed tasks. | Outbound webhooks push results the moment the agent finishes. |
| AI outputs sit in the platform until someone exports them. | Results flow automatically to any downstream system via webhook. |
| Connecting AI outputs to internal tools requires a middleware layer. | Outbound webhooks connect directly to any endpoint, no middleware. |
| Teams build custom polling scripts to detect agent completions. | Webhooks eliminate polling entirely, events are pushed, not pulled. |
FAQ
When does the AI employee send a webhook?
Webhooks are sent when configured trigger events occur, such as task completion, a specific tool being used, or a message being sent. You define the trigger conditions and the destination URL when setting up the integration.
What format is the webhook payload in?
Payloads are sent as JSON over HTTPS. You can configure the structure of the payload to match what your receiving system expects, including specific fields, task outputs, and metadata.
Can I use webhooks to connect my AI employee to Zapier or Make?
Yes. Both platforms accept inbound webhooks as triggers. Configure your AI employee to send a webhook to your Zapier or Make webhook URL, and any downstream automation in that platform will fire when the agent completes its work.
Is there retry logic if the webhook destination is unavailable?
Yes. Failed webhook deliveries are retried automatically with exponential backoff. You can also view webhook delivery history and manually retry failed events from the integration settings.
Can my AI agent notify external systems when it finishes a task?
Yes, outbound webhooks let your AI employee push events to any URL when specific actions complete, such as finishing a task, updating a record, or generating a report. This makes it easy to chain Sistava into existing workflows.
Every time our AI agent finishes a research brief, it fires a webhook that drops the result straight into our Notion database. Zero manual handoff.
Where Outbound Webhooks fits
Outbound Webhooks is part of How you plug them into your systems.
Build on your AI workforce with a full REST API, client SDKs, and webhooks in both directions. Expose any employee as an MCP server for Claude, Cursor, and Windsurf, or as an A2A agent for cross-platform agent collaboration. Everything the UI can do, the API can do.
- How you plug them into your systems: Programmatic access to your workforce.
Read the guide
More in Developer API
- REST API: Every AI employee is reachable over a REST API: send a prompt to POST /api/v1/chat with the employee's ID and get their response back as JSON, running through the same execution pipeline as web chat with full access to that employee's skills, tools, and memory. Requests are synchronous, blocking for up to five minutes, and authenticate with a Bearer API key shared across every programmatic channel (REST API, MCP Server, A2A, Webhooks, Email). The channel is off by default and must be enabled from Settings > Technical > Channels before it accepts requests.
- Webhook Inbound: Send a POST request to a single endpoint whenever something happens in one of your other systems, and your AI employee picks it up and acts on it in the background. You get a 202 response immediately; there is no waiting for a reply. The same employee that answers you in chat can react to a failed deploy, a new order, or a support ticket the moment it fires.
- Let Other AI Clients Use Your Employee: Connect Claude Desktop, Claude Code, Cursor, Windsurf, ChatGPT, or any Model Context Protocol client directly to your AI employees. Generate an API key, add one config block, and your coding tools can send prompts to any employee by ID and get a real response back. No custom integration code required.
- Let Other AI Agents Work with Your Employee: Turn your workforce into an agent that other AI agents can call directly. Any client that speaks Google's A2A protocol can discover your employees, hand them a task, and get the result back, all without touching your web UI or writing REST API glue code.
- Build with the Sistava SDK: A planned client library for JavaScript, Python, and other runtimes that wraps the existing employee REST API. It is on the roadmap, not published: today you call employees from code through the REST API guide directly, and the SDK would only make that same integration point faster to reach.