Guide
Rails AI agent observability: traces, logs, and costs
Production agents fail quietly without traces. Observability should cover model calls, tool results, latency, errors, and spend in one place.
Updated 2026-07-28 · Rails Agent · Tiny Bubble Company
Direct answer
Rails Agent records traces for agent runs, including model interactions, tool calls, and costs, searchable in the /agents dashboard so teams can debug behavior without assembling a separate LLM observability stack.
What to capture
- Prompt and response metadata needed for debugging (with privacy policy in mind)
- Tool inputs/outputs and failures
- Latency and token/cost signals
- Retry and queue outcomes
- Evaluation scores when tests are attached
Cost control link
Traces without budgets still burn money. Pair observability with budget controls and guardrails so runaway loops surface early.
Frequently asked questions
Where do I view agent traces?
In the Rails Agent dashboard at /agents after you sync and run an agent. Each run exposes the steps needed to understand failures and cost.
Is observability only for cloud runs?
Local and test runs are designed to give developers feedback before promotion; production traffic is monitored in the hosted path.
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
How to evaluate Rails AI agents →
Evals guide
Budget controls and guardrails →
Spend safety
Human-in-the-loop approvals →
Gate risky actions
Guardrails docs →
Product documentation
Security →
BYOK and workspace controls