Build and host custom AI agents directly on Fronterio
Write a system prompt, pick tools, set guardrails, pick your model (Anthropic, OpenAI, or self-hosted Ollama on Enterprise), click Publish. Fronterio runs the agent — enforcing guardrails at runtime, not just hoping the model follows instructions in its system prompt.
Tool integration tiers (built-in, MCP, OpenAPI, webhook)
Guardrail enforcement (not just system prompt)
External platforms to wire up before shipping
From governed metadata to a running agent
Until now, Fronterio has been the governance and deployment layer for AI agents that run on external platforms — Azure AI Foundry, AWS Bedrock, Copilot Studio, and so on. Agent Studio changes that. Agents defined in Studio run natively on Fronterio, with every guardrail enforced at runtime instead of being injected into a system prompt and hoped for.
A Studio agent has a system prompt, a model selection, a set of tool bindings, and a guardrail config. When a customer-facing user sends a message, Fronterio calls our AI engine (with streaming), runs the tool-use loop, enforces every guardrail on every tool call, and streams the response back over Server-Sent Events. If a tool hits a block or requires human approval, the runtime pauses the session, writes a governance record, and surfaces an approval card to the right reviewer.
Four tool integration tiers cover the realistic surface: built-in tools (RAG, tasks, metrics, incidents), external MCP servers the customer already runs, any REST API via an OpenAPI spec, and webhook tools that pause mid-turn and wait for a customer-hosted executor to call back. All credentials flow through the same AES-256-GCM helper that the deployment connectors use.
Every published version is immutable. Rolling back to an older version fully restores the prompt, the model, the tool bindings, and the guardrails as they were at publish time. Sessions are locked to one active request at a time via a CAS-based advisory lock so two tabs never corrupt each other. The test chat panel streams tokens live so you can debug tool calls in real time.
Agent Studio is built for the Enterprise plan. It slots into the existing governance + compliance stack — every published version runs through the 7-gate EU AI Act compliance check, guardrail violations automatically log to ai_incidents, and the audit log captures every deployment. Studio is the difference between tracking agents on paper and actually shipping them.
Build, test, ship in one place
You are a customer support agent for Acme Corp. Help users with product questions and account issues. Log incidents when something goes wrong. Escalate refund requests to a human.
fronterio_log_incidentfronterio_create_taskslack__post_messagezendesk__create_ticketHITL
Building an AI agent with vs without Agent Studio
Without Agent Studio
- Wire up a separate platform (Azure, Bedrock, etc.) just to run the agent
- Guardrails are system prompt instructions the model might ignore
- Credentials, secrets, and telemetry spread across multiple dashboards
- HITL approval requires custom integration work for every tool
With Agent Studio
- Write a prompt, click Publish, endpoint is live on Fronterio
- Guardrails enforced in the runtime middleware on every tool call
- Credentials, telemetry, and incidents all in one governance record
- HITL approvals, webhook callbacks, and expiry cron built in
What you get
Runtime-Enforced Guardrails
Blocked actions, human approval gates, PII scrubbing, rate limits, and confidence thresholds — all enforced in the runtime middleware between every tool call. Violations auto-create ai_incidents rows.
Four Tool Integration Tiers
Built-in Fronterio tools (RAG, tasks, metrics, incidents), external MCP servers, any REST API via OpenAPI, and webhook tools that pause mid-turn for customer-hosted executors.
Real Token Streaming
Tokens stream live over Server-Sent Events as the AI generates them. The test chat panel shows you exactly what your agent does, step by step, in real time.
Immutable Versions + Rollback
Every publish creates an immutable version snapshot (prompt, model, provider, base URL, tools, guardrails). Rollback is a pointer flip. Guardrail + provider changes never affect running sessions until the next publish.
How it works
Write a system prompt
Open Studio, describe what your agent does. Guardrails are configured separately so your prompt can stay focused on the task.
Pick tools
Start with built-in tools, connect an external MCP server, paste an OpenAPI spec, or wire up a webhook tool for full custom control.
Test in the Studio chat
Stream tokens live. Watch tool calls, blocks, and approvals happen in real time. Debug without deploying.
Publish
Compliance gate runs. New immutable version is created. Endpoint goes live at /api/agents/{id}/chat. Telemetry flows back automatically.
“Agent Studio is the difference between tracking your AI agents on paper and actually shipping them.”
Enterprise only
Agent Studio is available exclusively on the Enterprise plan, alongside Deployment Infrastructure and the MCP Server.
Build your first hosted agent
Talk to us about Agent Studio for your organisation.