Run production AI agents without running the infrastructure

Most teams that want an AI agent in production do not want an AI agent project. They want a thing that answers questions in Slack, or triages tickets, or reads a repository and writes a summary — and they would like it to still be working next month without anyone owning it.

The distance between those two is larger than it looks. A model provider account. A key nobody should have. Somewhere to run a container. A queue. A database for conversation state. Logs you can search at 2am. A way to know what it cost. Roughly a fortnight of setup before the agent does anything you asked for.

Build Your Future is that fortnight, already done.

What you actually get

1. A running agent, not a repository

   You pick a template and press deploy. What comes back is a URL with an agent behind it — container, volume, health checks, restarts and TLS included. No Dockerfile, no cloud account.

2. Model access without a key

   Every request goes through the inference gateway, which holds the provider credentials and meters usage per token. You never paste an API key into a config file, and no agent ever sees one.

3. One bill, in cents you can trace

   Compute and tokens land on the same statement, per agent, per day. Not a seat count, not a plan tier you have to translate into what you used.

4. An upgrade path that is not your problem

   Templates track their upstream projects. When a new version is released, the agent shows an update — you choose when, we handle the build.

Everything on one screen

The dashboard is the whole surface: which agents exist, whether they are healthy, and what each one has cost this month. Deploy, pause, redeploy and delete are all one click from here.

It follows your system theme, because half of you will read this at night.

Start from something that already works

Templates are maintained forks of real agent projects, not starter scaffolds. Each one is pinned to a known-good upstream release, patched where the managed environment needs it, and tested against a live container before it reaches the catalog.

Deploying one takes a name and a model:

Catalog → Hermes → Deploy
  Name    support-bot
  Model   claude-sonnet-5
  Region  auto

curl -X POST https://buildyourfuture.app/api/agents \
  -H "Authorization: Bearer $BYF_KEY" \
  -d '{"template":"hermes","name":"support-bot","model":"claude-sonnet-5"}'

Billing you can argue with

Usage is recorded per request and priced at the token. The usage page shows where the money went, by agent and by day, and the number on it is the number on the invoice.

Where your team already is

An agent is not much use if reaching it means opening a dashboard. Turn on Telegram and the platform creates a bot for that agent, wires the webhook and handles files and rich replies. Everything else is an HTTP endpoint with a token.

When one agent is not enough

Novas orchestrate other agents. A Nova holds the roster, decides which agent answers what, keeps shared memory across conversations, and can run on a schedule — so "summarise yesterday's tickets every morning at nine" is configuration rather than a cron job you maintain.

• Route a question to the agent that can answer it, or coordinate several that each hold part of it
• Persistent memory across sessions, not a context window that forgets on Monday
• Scheduled runs with a history you can read when one of them misbehaves

What it does not do

It is a managed control plane, so the tradeoffs are the ones managed platforms have:

• You do not choose the cloud. Agents run on our infrastructure. If your compliance posture requires a specific region or account, this is not the right fit.
• You do not get root. Agents are containers with a volume and an API, not machines you SSH into.
• Templates set the shape. You can configure an agent and extend it with skills, but the managed path expects something that looks broadly like the templates.

If those are dealbreakers, this is not for you. If they are not, you can have an agent answering questions before lunch.