> ## 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.

# Next Steps

> Go beyond: teams, workflows, scheduled tasks, and interfaces.

You've made it. You now have a deployed agent platform with evals, JWT auth, and a set of Claude Code prompts that cover the full ADLC.

The sections below cover the next level: teams and workflows for multi-step logic, scheduled tasks for proactive runs, and interfaces that put your agents where your users are.

## Going beyond agents

| Pattern      | Use it when                                                                | Reference                                 |
| ------------ | -------------------------------------------------------------------------- | ----------------------------------------- |
| **Agent**    | A single LLM with tools and instructions can handle the request.           | [Agents overview](/agents/overview)       |
| **Team**     | The right specialist isn't known up front. A leader routes or coordinates. | [Teams overview](/teams/overview)         |
| **Workflow** | The process needs to run the same way every time. Determinism matters.     | [Workflows overview](/workflows/overview) |

Teams come in three modes:

| Mode           | Behavior                                                           |
| -------------- | ------------------------------------------------------------------ |
| **Coordinate** | A leader plans the work, calls the right specialists, synthesizes. |
| **Route**      | A router picks one specialist to handle the request.               |
| **Broadcast**  | Every specialist runs in parallel; you aggregate.                  |

## Scheduled tasks

The scheduler is on by default in `app/main.py`. Schedule any agent or workflow on a cron:

| Use case              | Example                                                            |
| --------------------- | ------------------------------------------------------------------ |
| **Maintenance**       | Purge sessions older than 90 days. Vacuum Postgres tables.         |
| **Proactive runs**    | Every weekday morning, summarize overnight news and post to Slack. |
| **Catch regressions** | Run `python -m evals` weekly against production agents.            |

See [scheduling](/features/scheduling) for the cron API.

## Connect to interfaces

Your agents should be available where your users are. Slack threads. Discord channels. Telegram for the field team. Or a custom UI inside your product.

Expose the agent via an interface in `app/main.py`:

```python theme={null}
from agno.os.interfaces.slack import Slack

interfaces: list = []
if SLACK_BOT_TOKEN and SLACK_SIGNING_SECRET:
    interfaces.append(
        Slack(
            agent=code_search,
            streaming=True,
            token=SLACK_BOT_TOKEN,
            signing_secret=SLACK_SIGNING_SECRET,
            resolve_user_identity=True,
        )
    )

agent_os = AgentOS(
    ...
    interfaces=interfaces,
)
```

| Interface         | Reference                                                        |
| ----------------- | ---------------------------------------------------------------- |
| Slack             | [Slack interface](/agent-os/interfaces/slack/introduction)       |
| Telegram          | [Telegram interface](/agent-os/interfaces/telegram/introduction) |
| WhatsApp          | [WhatsApp interface](/agent-os/interfaces/whatsapp/introduction) |
| Custom UI / AG-UI | [AG-UI interface](/agent-os/interfaces/ag-ui/introduction)       |
| All interfaces    | [Interfaces overview](/agent-os/interfaces/overview)             |

## Keep the repo coherent

As you ship more agents, configuration drifts, env vars rot, and new agents miss imports. The template ships a fifth Claude Code prompt for the recurring sweep:

```
Run docs/review-and-improve.md
```

It auto-fixes mechanical drift (stale paths, missing `example.env` entries, agents on disk not registered in `app/main.py`) and surfaces the rest as a punch list. Best run before public releases and periodically during active development.

## You're done

You now have an agent platform that:

* Runs locally and on Railway, with JWT auth built-in.
* Stores sessions, memory, knowledge, and traces in a Postgres database.
* Manages and improves itself through five Claude Code prompts.