Give every team the power of AI agents — without giving up control.
Agentic Rails gives enterprises a practical way to deploy AI agents at scale while staying in control of cost, security, tools, models, and where data is stored.
AI agents move fast. Oversight has to keep up.
Teams are finding more ways to use AI agents every day — research, drafting, triage, automation, and more. But once agents have access to real tools and company data, things get complicated quickly.
Costs can be hard to track. Agents may try to use tools they shouldn’t. Sensitive data can end up where it doesn’t belong. And when something goes wrong, it may be difficult to tell exactly what the agent did.
A controlled environment for agentic work
Agentic Rails is a self-hosted platform for building, scheduling, and managing multi-stage AI pipelines. Every run is metered. Tools have to be explicitly granted. Data at rest is encrypted with a key you control. And each pipeline can use the models and storage locations your organization chooses.
Guardrails for every stage of an agent’s work
Spend Visibility — Know the cost before the invoice
Every pipeline run is metered and logged, with costs broken out by stage and model. Teams can see what they’re spending by pipeline and by group instead of waiting until the end of the month to find out.
Governed Tool Access — Agents only get the tools you give them
There’s no default toolset. Each pipeline stage can use only the tools explicitly assigned to it. File access stays inside the stage’s workspace, and if a model tries to call a tool it wasn’t given, Agentic Rails blocks the request at execution time.
Model Freedom — Use the model that makes sense for the job
Connect directly to Anthropic, OpenAI, or Azure OpenAI, or use any OpenAI-compatible endpoint, including OpenRouter and Requesty. Decision stages can call SystemOne-decision models directly. Models can run through a vendor API or on infrastructure you manage yourself.
Your Data, Your Storage — Workspaces on infrastructure you control
Agent workspaces can sync in both directions with the storage systems you already use, including Amazon S3, Google Cloud Storage, Azure Blob, and Google Drive. Connection credentials are encrypted at rest.
Email, On Your Terms — Work through the systems you already use
Connect AgentMail, REST-based providers such as Resend, Postmark, and Mailgun, Gmail through OAuth, generic SMTP/IMAP, or JMAP. Agents can read and send email through systems your organization already knows and trusts.
Built-in Audit & DLP — Check data before it reaches a model
Prompts and tool responses are checked locally against configurable rules before they’re sent to a model. Rules can look for credentials, secrets, PII, and patterns your organization defines. When a rule matches, the stage is blocked and the event is logged. Administrators control the rules and exceptions by pipeline and team.
Run Log & Replay — See exactly what happened during a run
Every prompt, tool call, and model response is recorded and protected with an encryption key unique to your installation. When you need to investigate a run, you can go back and see exactly what the agent did and what it said.
Enterprise Access Control — Apply your existing access policies
Single sign-on, invite-only accounts, and role-based access help keep AI usage aligned with your organization’s existing policies. Model access and spending limits can be set by team, while each team gets visibility into its own pipelines and workspaces.
One Pipeline, the Right Model for Every Step
A pipeline doesn’t need to use one model from start to finish. Break a larger task into stages and choose the model that fits each one — a fast, inexpensive model for extraction or formatting, and a frontier model reserved for the step that actually needs deeper reasoning.
You can assign models directly or let Criteria choose at runtime based on factors such as price, context length, and required capabilities, giving you more control over both performance and cost.
The Council: When One Model Isn’t Enough
Some decisions are important enough that you may not want to rely on a single model. Council mode runs the same pipeline independently across multiple Member models in parallel. A Chairman model then reviews their outputs, looks at where they agree and disagree, and produces a single response for your team.
You get the benefit of multiple perspectives without someone having to manually compare several separate answers.
Compose, run, watch, review
Compose
Build a pipeline from individual stages. Each stage defines its prompt, model, allowed tools, and turn limit, so every step operates inside its own controlled conversation.
Run
Start a pipeline when you need it, run it on a schedule, or trigger it from another system using a scoped webhook token.
Watch
Usage, cost, tool activity, and model exchanges are tracked as the pipeline runs. If something fails, Agentic Rails can automatically notify the right people.
Review
Go back through any run, all the way down to individual model exchanges and tool calls. See what happened, adjust the pipeline, and run it again.
Visibility, control, security, flexibility
Visibility
See AI cost, usage, and activity by pipeline and by team instead of discovering it later on a vendor invoice.
Control
Your organization decides which tools, models, and data destinations are available. Those choices aren’t dictated by a vendor’s defaults.
Security
Encryption, sandboxing, audit controls, and access management are part of the platform from the beginning.
Flexibility
Use the models and storage systems that fit your environment, and deploy Agentic Rails inside infrastructure you already control and trust.
Technical overview
| Deployment | Self-hosted inside your own VPC or data center using Docker Compose, your TLS certificate, and your database. |
|---|---|
| Identity & Access | Enterprise SSO with OIDC today, with SAML and additional identity providers on the roadmap. Accounts can be invite-only, with role-based access and per-team controls over model access and spending. |
| Encryption | Run logs and stored credentials are encrypted at rest using a key unique to each installation. Encryption and decryption happen inside your own deployment. |
| Model Support | Connect directly to Anthropic, OpenAI, and Azure OpenAI, or use any OpenAI-compatible endpoint, including OpenRouter and Requesty. Structured decision stages can use SystemOne-decision models, and you can also connect models running on your own infrastructure. |
| Email & Messaging | Supports AgentMail, REST providers including Resend, Postmark, and Mailgun, Gmail through OAuth, SMTP/IMAP, and JMAP. |
| Storage Connectors | Two-way synchronization with Amazon S3, Google Cloud Storage, Azure Blob, and Google Drive keeps agent workspaces in storage you control. |
| Audit & DLP | Prompts and tool responses can be scanned locally before reaching a model using rule-based and model-assisted checks. Policies can be configured by pipeline and team, with administrator-controlled exceptions and a complete audit log. |
| Orchestration | Build multi-stage pipelines with a different model for each step. Models can be selected directly or through cost/quality Criteria. Agentic Rails also supports Council mode, scheduled runs, webhook triggers, failure notifications, and complete per-run audit logs. |
| Integration | Model Context Protocol (MCP) servers can extend the available toolset while remaining under operator control. Pipeline editors never receive direct access to those servers. |
Ready to put guardrails around your AI rollout?
Talk with us about bringing Agentic Rails to your teams.