# Build an agent platform without writing a single line of code

> Today I'll show you how to build your own agent platform without writing a single line of code.

- Published: 2026-07-27
- Author: Ashpreet Bedi
- Category: Engineering
- Canonical: https://agno-com-nine.vercel.app/articles/build-your-own-agent-platform
- Markdown: https://agno-com-nine.vercel.app/articles/build-your-own-agent-platform.md

> _The future is here, just not evenly distributed._

Today I'll show you how to build your own agent platform without writing a single line of code.

I know this sounds crazy, so I'll share videos along the way and encourage you to build along. All you need is a coding agent, docker, and an OpenAI API key.

But first, what is an agent platform and why is every company building one?

## What is an Agent Platform?

An agent platform is the system that runs your agents. It handles agent execution (i.e. running the agents), manages sessions and context, enforces security policies, logs traces, and connects your agents to frontends via API, MCP, or chat interfaces like Slack.

An agent platform provides a shared foundation that you can build your company's AI applications on. Without a shared platform, every agent becomes its own silo, each needing its own deployment, authentication, database, observability, and integrations.

Most companies build their own agent platform to own the learning loop. To retain full control over the agent's behavior, data and context. Which paves the way for reflective self improvement.

## Let's Get Started

The first thing we're going to do is set up our agent platform. I'm going to show how to do that with **one prompt**. I know this sounds mad, so here's a video of it in action.

<Video src="/videos/agent-platform-setup.mp4" controls preload="metadata" />

**Want to follow along?** Go to [os.agno.com](https://os.agno.com), get the prompt for your cloud provider (I'm using Railway) and hand it to your favorite coding agent (I'm using Claude Code).

For a one-shot experience like the video, make sure you have docker installed and running, and export your OpenAI API key using `export OPENAI_API_KEY=<your-api-key>`

Under the hood, the coding agent first clones an agent-platform template (built by yours truly) and then runs the `/setup-platform` skill. It ends by running the `/create-agent` skill, which I used to create an agent called `Radar`. Ten minutes from a fresh machine to a running platform.

## Skills for days

When I say "build without writing a single line of code", I mean _**you**_ don't write any code, the coding agent does. But how do we do that reliably? A unified platform + skills.

The agent platform we created above is structured so that agent code, logs, traces, evals and live service all live in one place, accessible to the coding agent doing the implementation. A complete paradigm shift because you're no longer stitching different services together.

Because the coding agent can make changes to the code, and probe the live running system, it's able to take full control of the agent development lifecycle. It can create agents (using `/create-agent`), reflect on usage and improve them (using `/improve-agent`), fix bugs, add new features (using `/extend-agent`), write and run evals (using `/create-evals` and `/eval-and-improve`), keep the codebase in sync (using `/review-and-improve`), and even deploy the platform to production.

All of this is possible because **a)** the gnarly wiring is handled by Agno, so the coding agent is just putting things together, and **b)** all information the coding agent needs is available using Agno's MCP server. So the coding agent is simply assembling a puzzle, with access to the manual via MCP.

I won't go into the details of each skill, but I will showcase the `/improve-agent` skill. Which covers the most important part of your agent platform: the learning loop.

`/improve-agent` takes an agent and runs N simulations (called probes) to evaluate the agent's performance. It derives these probes from the agent's own instructions and from its real usage (stored in the database). It then runs the probes against the live container, judges the responses, and improves the agent until they pass. It's rare for me to publish an agent without running this skill first.

Let's see it in action. `cd` into your agent platform and restart your coding agent so it picks up the new skills. Then run `/improve-agent` to improve your agent, my run took about 29 minutes so read the rest of the article before running this. It really does dig through the edge cases.

<Video
  src="/videos/agent-platform-improve-agent.mp4"
  controls
  preload="metadata"
  aria-label="Improving an agent using the /improve-agent skill"
/>

## Give your agent platform a UI

Every step so far has been in a terminal, next let's give our agent platform a UI. Head to [os.agno.com](https://os.agno.com) and connect your platform.

<Video
  src="/videos/agent-platform-ui.mp4"
  controls
  preload="metadata"
  aria-label="Chatting with agents and inspecting traces in the AgentOS UI"
/>

Here's the part I like. The AgentOS UI connects directly from your browser to your runtime. The flow of data is entirely between your browser and the running API. Sessions, traces and memory stay in your database and you retain full control and ownership of your data and context.

## Connect your agents to your frontends

You're not building an agent platform to replace Claude or ChatGPT. You're building an agent platform to power your product and operations with custom agents you own and control. Meaning building agents is only part of the puzzle, you also need to connect them to:

1. **Your product.** Use the **REST API** to call your agents from your product.
2. **Your AI apps.** Connect your agents to Claude and ChatGPT using the **MCP server**.
3. **Your chat apps.** Expose agents in Slack, Discord and Telegram using **Agno Interfaces**.

Luckily, your agent platform is already set up to do all three.

### First, go live using the `/deploy-platform` skill

To connect your platform to AI apps like Claude and ChatGPT, or chat apps like Slack, Discord and Telegram, you need to deploy your platform so it is reachable from the internet.

So before we wire up the AI apps and chat apps, let's take our platform live. Run the `/deploy-platform` skill, which will deploy the platform to the cloud template you selected during setup (I'm using Railway).

<Video
  src="/videos/agent-platform-deploy.mp4"
  controls
  preload="metadata"
  aria-label="Deploying the agent platform to production"
/>

### Connect your agents to your AI apps using the MCP server

This is the part I'm most excited about. A large number of the custom agents and workflows you'll build will be used through AI frontends like Claude, ChatGPT, Claude Code and Codex. If you've ever heard anyone moan about shared context between AI apps, this is the solution.

And it's remarkably simple to do so. Your agent platform (built on AgentOS) exposes every agent, team, and workflow using an MCP server, so Claude and ChatGPT can connect to your AgentOS and run your agents.

Let's test it out by connecting **Chief** (the company mascot) to Claude.

In Claude, go to Settings → Connectors → Add custom connector and paste your platform's MCP URL: `https://<your-railway-domain>/mcp`. Leave the client ID and secret fields empty. Connect and it'll take you to a consent page asking for your connect secret: that's the `MCP_CONNECT_SECRET` the /deploy-platform skill generated and added to `.env.production` (it prints it in the deploy summary, copy it). Once you confirm, Claude can interact with your AgentOS using MCP.

First, tell Claude that whenever you ask for "Chief", you want to run the chief agent on AgentOS (it'll add it to its memory).

```
Whenever I ask for "Chief", you should run the chief agent on AgentOS.
```

Then tell Claude to interact with Chief.

```
Tell chief the surfer leo zapato from garaje was 🔥.
```

<Video
  src="/videos/agent-platform-mcp.mp4"
  controls
  preload="metadata"
  aria-label="Using Chief from Claude via MCP"
/>

### Connect chief to Slack

Next, let's make Chief available in Slack. You can connect your agents to any chat app using Agno Interfaces. Slack is the most popular, and comes pre-wired in the platform template.

Create a Slack app for your workspace (the [Agno docs](https://docs.agno.com/agent-os/interfaces/slack/setup#2-create-the-slack-app) have a step-by-step guide), point its Event Subscriptions URL at `https://<your-railway-domain>/slack/events`, and grab two values: the bot token and the signing secret. Set `SLACK_BOT_TOKEN` and `SLACK_SIGNING_SECRET` in `.env.production`, run `./scripts/railway/env-sync.sh` (or tell your coding agent to run it), and chief should be available over slack.

### Use your agents from your product using the REST API

This is the last one, and the easy one. Your agent platform comes with a complete API with 80+ endpoints. Run agents, stream responses, list sessions, manage knowledge. Use this API to build agentic products on top of your platform.

Go [localhost:8000/docs](http://localhost:8000/docs) for the full list of endpoints. Everything the AgentOS UI does, your product can do too. The AgentOS UI uses the exact same API.

## Wrapping up

Today we built a unified agent platform using nothing but a prompt, took it live, and connected it to Claude, ChatGPT, and Slack. End to end, this guide would probably take you ~30-40 minutes, which is pretty awesome! Now the fun part, build more agents, improve them, and connect them to your product, use them in your AI apps or chat apps. Here is the full set of skills, for reference:

- `/setup-platform` takes you from a fresh clone to a running platform
- `/create-agent` creates a new agent and tests it
- `/extend-agent` makes changes to existing agents, add features, fix bugs, etc.
- `/improve-agent` recursively improves the agent against its own instructions and usage patterns
- `/create-evals` creates new evals for the agent, lock in good behavior via tests
- `/eval-and-improve` runs the evals and improves the agent if they fail
- `/review-and-improve` keeps the full codebase in sync, docs, code, and config coherent.
- `/deploy-platform` deploys the platform to production

Total lines of code written by me: zero.

The future is here. Go to [os.agno.com](https://os.agno.com) and see for yourself. Links for reference:

- [Agno is open source, star the repo on GitHub](https://github.com/agno-agi/agno)
- [Read the Agno documentation](https://docs.agno.com)
- Connect with me on [X](https://x.com/ashpreetbedi) or [LinkedIn](https://linkedin.com/in/ashpreetbedi)