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
Team	The right specialist isn’t known up front. A leader routes or coordinates.	Teams overview
Workflow	The process needs to run the same way every time. Determinism matters.	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 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:

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
Telegram	Telegram interface
WhatsApp	WhatsApp interface
Custom UI / AG-UI	AG-UI interface
All interfaces	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.