Guide
How to evaluate Rails AI agents
A demo chat is not an evaluation suite. Production agents need repeatable cases for correctness, policy, and cost before traffic grows.
Updated 2026-07-28 · Rails Agent · Tiny Bubble Company
Direct answer
Evaluate Rails AI agents with fixed scenarios that assert tool use, answer quality, escalation behavior, and spend. Rails Agent supports testing in the dashboard and production monitoring so regressions are caught before and after deploy.
Evaluation checklist
- Golden questions with expected tools or outcomes
- Refusal and escalation cases for policy
- Latency and cost budgets per scenario
- Human review samples for ambiguous answers
- Re-run after prompt, model, or tool changes
From evals to deploy
Promote agents only after critical cases pass. Keep monitoring live traffic—evals reduce risk; they do not remove the need for traces.
Frequently asked questions
What should a minimum eval set include?
At least happy-path business questions, a tool-failure case, a policy/escalation case, and a cost-sensitive long conversation or loop case.
Do evals replace monitoring?
No. Evals catch known regressions; monitoring catches novel failures and spend spikes in production.
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
Deploy AI agents in Rails →
Go-live path
Observability →
Traces and costs
Dashboard test tab →
Sandbox runs before promote
Structured outputs →
Testable agent results
Channels →
Where agents meet users
Background execution →
Queues and retries