Add AI agents to
your existing product
Your customers are asking for AI features. Ship them this sprint instead of next quarter. Embed agents into your SaaS with the skills your team already has — Python, YAML, and Git — so you skip the ML hire and the rebuild.
Read the quickstartConnectors
inbound & outboundall healthy
The common shapes of an AI feature
Most embedded AI work shows up in one of these three forms. Each is a connector plus an agent — no new infrastructure to stand up.
A user uploads a document. The S3 connector fires an agent that extracts the key fields, writes the result into the knowledge base, and hands structured JSON back to your app — so your customer sees results in seconds, not after an overnight batch.
Shoppers type the way they would say it out loud. The WebSocket connector streams the conversation to an agent that searches your product catalog and streams matches back as it thinks — replacing dead-end keyword search with a guided buying flow.
A cron connector wakes an agent every Monday. It queries your database with built-in tools, drafts a clean exec summary, and sends it through the outbound email connector — replacing the report someone used to assemble in a spreadsheet every week.
Connectors do the plumbing
Your backend triggers an agent from a webhook, an S3 upload, a Postgres change, or a cron — and the result comes back through the same channel.
- LLM integration: SDK setup, error handling, retries
- Tool execution: Framework code, schema definitions
- Deployment: Docker, Kubernetes, CI/CD pipelines
- Scaling: Auto-scaling config, load balancing
- Observability: Logging, tracing, custom dashboards
- Knowledge base: Vector store, embeddings, chunking
Browse the connector library or jump straight into the quickstart.
Spend your engineering budget on features
Every hour your team spends on infrastructure is an hour they're not shipping the AI feature your customer asked for.
Write a YAML config, add a few Python tools, push to Git. That's the deploy. No Docker, no Kubernetes, no platform team standing up clusters.
If your team can write Python and use Git, they can ship AI features today — no ML hire, no DevOps headcount to add.
Every run is captured as a full trace: LLM reasoning, tool calls, timing, cost. When something goes wrong, you find the root cause in seconds.
Agents live in your repo. Changes go through PRs, deploys ship on push, rollbacks are one click — same workflow your team uses for the rest of your code.
Variables are scoped per environment. Sensitive values stay masked in dashboards and logs. Every action lands in the project audit log.
Runs execute on managed infrastructure that scales with your traffic. No idle clusters, no surprise bills — set spend alerts and hard limits per agent or environment.
Production-ready, not proof-of-concept
The controls security and finance ask for before AI ships to real customers
Every connector ships with authentication and encryption. Webhook connectors verify a shared secret on each request, and the Bridge connector reaches services in your VPC over an outbound tunnel — no inbound ports to open.
Set spend alerts and hard limits per agent or environment so a runaway prompt cannot bill you out of a quarter. Anomaly detection flags unusual spikes the moment they happen.
Every run is logged with a full trace. DPA available, EU AI Act support, and SOC 2 Type II on Enterprise plans — so legal and security can sign off without a custom build.