> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Serve and embed

> Put the data agent behind an API so a dashboard, a Slack channel, or a product widget can ask it questions.

A data agent that only runs in a notebook helps one analyst. Behind an API, it answers questions from a Slack channel, a BI dashboard's natural-language box, or an "explain this metric" widget in your product. `AgentOS` turns the agent into that API.

```python theme={null}
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIResponses
from agno.os import AgentOS
from agno.tools.sql import SQLTools

agent = Agent(
    id="data-agent",
    model=OpenAIResponses(id="gpt-5.5"),
    db=PostgresDb(db_url="postgresql+psycopg://ai:ai@localhost:5532/ai"),
    tools=[SQLTools(db_url="postgresql+psycopg://readonly@warehouse/analytics")],
    learning=True,
)

agent_os = AgentOS(agents=[agent])
app = agent_os.get_app()

if __name__ == "__main__":
    agent_os.serve(app="data_agent:app", port=7777)
```

Any surface calls the same endpoint:

```bash theme={null}
curl -X POST http://localhost:7777/agents/data-agent/runs \
  -F 'message=MRR by plan, last 6 months' \
  -F 'user_id=ab@acme.com' \
  -F 'session_id=q4-review' \
  -F 'stream=false'
```

## Surfaces a data agent lives on

| Surface          | Shape                                                                   |
| ---------------- | ----------------------------------------------------------------------- |
| Slack channel    | An interface maps a thread to a session; the team asks in plain English |
| Dashboard NL box | A widget posts the question and renders the answer plus its SQL         |
| Scheduled digest | A cron job runs the agent and posts "yesterday's numbers" every morning |
| Backend check    | A pipeline calls the agent to sanity-check a metric before publishing   |

The serving model is identical to a [product agent](/use-cases/product-agents/serve-as-an-api). The difference is what the agent does, not how it is served. Sessions still scope per user, so each analyst's thread and learnings stay separate.

## Learnings are shared, sessions are not

A data agent's value compounds when corrections are shared across the team. Scope conversation threads per user with `session_id`, but keep learnings in a shared namespace so a fix one analyst's question triggered helps everyone.

| State                         | Scope                          |
| ----------------------------- | ------------------------------ |
| Conversation thread           | Per `user_id` and `session_id` |
| Learnings about the warehouse | Shared namespace across users  |

## Next steps

| Task                           | Guide                                              |
| ------------------------------ | -------------------------------------------------- |
| Add Slack or a browser surface | [Interfaces](/use-cases/product-agents/interfaces) |
| Lock down the endpoints        | [Security and auth](/features/security-and-auth)   |

## Developer Resources

* [Serve as an API](/features/api)
* [Product agents: serving](/use-cases/product-agents/serve-as-an-api)