Guide
Deploy AI agents in Ruby on Rails
Deploying a Rails AI agent means more than shipping Ruby code. You need a reliable runtime, credential handling, queues, retries, and visibility after go-live.
Updated 2026-07-28 · Rails Agent · Tiny Bubble Company
Direct answer
Rails Agent deploys agents from your app/agents/ directory through a hosted production runtime. You keep agent logic in Rails; the platform handles execution, scaling, retries, and traces once you promote an agent from Test to Production.
What production deploy requires
- Versioned agent code in the Rails app
- BYOK provider credentials attached to the workspace
- Background execution for long or tool-heavy runs
- Health checks, retries, and failure surfaces
- Traces and cost visibility after traffic starts
Deploy workflow
Build and test locally or in the Test tab. Attach provider keys under Settings. Promote the agent when behavior and evals pass. Monitor runs in /agents without bolting on a separate observability stack.
Honest limitation
Teams with strict air-gapped or fully self-hosted runtime requirements should evaluate whether the hosted path fits policy. The gem still supports local development and Rails-native patterns either way.
Frequently asked questions
Do I need to provision my own agent servers?
For the standard Rails Agent path, no. The cloud runtime executes production runs. You still run your Rails app as usual for local development and app hosting.
How do credentials work in production?
Attach encrypted bring-your-own provider keys in the workspace. Model spend bills your OpenAI, Anthropic, or other configured provider account.
Ship production Rails AI agents
Install rails-agent-stack, open /agents, and go from scaffold to deploy with Playbooks, memory, guardrails, and monitoring included.
Keep reading
Ruby on Rails AI agents →
Category pillar
Build AI agents in Ruby on Rails →
Implementation guide
Getting started docs →
Install and open /agents
Pricing →
Plans and runtime
Rails AI agent observability →
Traces, logs, costs
Playbooks →
Start from a working pattern
Channels →
Where agents meet users
Background execution →
Queues and retries
Budgets and guardrails →
Spend and policy controls
Human-in-the-loop approvals →
Gate risky actions
Guardrails docs →
Product documentation
Security →
BYOK and workspace controls