Next Steps

Your platform now runs locally and on Railway with persisted state, authentication, traces, evals, and a coding-agent development loop. Extend it with teams, workflows, schedules, and the interfaces your users already use.

Add teams and workflows

Pattern	Use it when	Reference
Agent	A single LLM with tools and instructions can handle the request.	Agents overview
Team	Multiple specialists should route, coordinate, or collaborate.	Teams overview
Workflow	The process needs explicit steps, branches, loops, or parallel execution.	Workflows overview

Teams come in four 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; the leader synthesizes.
Tasks	A leader breaks the goal into a task list, delegates tasks to members, and loops until every task is complete.

Scheduled tasks

The scheduler is on by default in app/main.py, and the template prepares two workflows for scheduled runs:

Workflow	What it does when enabled	Toggle
Deployment check	Checks daily that AgentOS is wired correctly.	`ENABLE_DEPLOY_CHECK` (on by default)
Run evals	Runs the `smoke`-tagged eval cases daily.	`ENABLE_SCHEDULED_EVALS` (off by default)

Schedule your own agents and workflows the same way:

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.

See scheduling for the cron API.

Connect to interfaces

Connect agents to Slack, Telegram, WhatsApp, or a custom UI inside your product. Expose the agent via an interface in app/main.py:

interfaces: list = []
if SLACK_BOT_TOKEN and SLACK_SIGNING_SECRET:
    from agno.os.interfaces.slack import Slack

    interfaces.append(
        Slack(
            agent=agent_builder,
            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
Telegram	Telegram interface
WhatsApp	WhatsApp interface
Custom UI / AG-UI	AG-UI interface
MCP clients	AgentOS MCP interface
All interfaces	Interfaces overview

Keep the repo coherent

As the platform grows, the same agent needs to stay aligned across its source file, app/main.py, app/config.yaml, environment variables, and documentation. The review-and-improve skill checks those files together:

Run the review-and-improve skill in .agents/skills.

It fixes straightforward issues such as stale paths, missing example.env entries, and agents that exist on disk but are not registered in app/main.py. It returns a list of changes that need your judgment. Run it before a public release or after a refactor.

What you have built

Capability	What is in place
Runtime	AgentOS running locally and on Railway
State	Postgres for sessions, memory, knowledge, traces, and eval history
Access	REST, MCP, the AgentOS UI, and optional chat interfaces
Security	JWT authorization configured for Railway, with per-user isolation available as an opt-in
Development loop	Six coding-agent skills for setup, creation, extension, improvement, evaluation, and maintenance

Welcome

Get Started

Features

Use Cases

Add teams and workflows

Scheduled tasks

Connect to interfaces

Keep the repo coherent

What you have built

​Add teams and workflows

​Scheduled tasks

​Connect to interfaces

​Keep the repo coherent

​What you have built

Add teams and workflows

Scheduled tasks

Connect to interfaces

Keep the repo coherent

What you have built