What is PromptRails?

See how PromptRails helps teams define, run, observe, and improve production AI agents from one workspace.

Best for

New teams, product owners, and engineers evaluating the workflow

PromptRails is a workspace for teams that ship AI agents in production. You use it to define agents and prompts, connect tools and data, run the agent from your product, and inspect what happened after every run.

The product sits between your application and model providers. That gives engineers and product teams one place to manage prompt changes, credentials, traces, approvals, quality checks, and cost without scattering that logic across application code.

The Product Loop

PromptRails is organized around the way production agent work actually happens:

Build the agent

Use Studio to define the agent, prompt, model settings, tools, data sources, memory, and approval points. Product teams can review the behavior in the product; engineers can wire the same version into an application or backend job.

Connect access

Add provider credentials, use PromptRails-hosted models through the LLM Gateway, attach MCP tools, connect databases, and configure data masking. Secrets stay in the workspace instead of being copied into agent code.

Run it

Run from Studio while building, from chat when testing conversation behavior, from triggers when the agent should react to events, or from SDKs and APIs when your product needs to call it.

Inspect what happened

Every run leaves an execution record and a trace. You can see rendered prompts, model calls, tool calls, data source queries, guardrail scans, approvals, token usage, cost, latency, errors, and scores.

Improve and ship

Use traces and failures to create eval sets, compare new versions against baselines, review quality gates, and promote or roll back agents and prompts without redeploying application code.

Product Areas

• Studio -- Build agents, prompts, data sources, MCP tools, memory, guardrails, approvals, and version history.

• Runtime controls -- Manage credentials, hosted model usage, data masking, and safety policies.

• Observability -- Inspect executions, traces, sessions, evaluations, approvals, scores, and cost.

• Integrations -- Call agents from APIs, SDKs, triggers, deployed UIs, virtual files, the CLI, or MCP-compatible tools.

• Administration -- Control workspace members, roles, billing, security, and API access.

The built-in PromptRails Assistant helps you draft, inspect, and update workspace resources from inside the product.

Getting Started

The fastest path is to get one useful agent run, then use the trace to decide what to improve next:

• Quickstart -- Create or select an agent, run it, and inspect the trace.
• Agents -- Understand how agents are organized in Studio.
• Tracing -- Learn how to debug one execution step by step.
• Evaluations -- Turn failures and scores into repeatable quality checks.