Rescue Agents From Prototype Purgatory: Announcing Agent Router Enterprise for Builder Teams
It's the best of times and the worst of times for builders. The promise of AI agents is at an all-time high, business appetite for investment is strong, and prototyping agents has never been easier. Yet in the rush to start, builders face a new challenge: how do you know your agent is ready for action in an enterprise environment?
Arif Setiawan, John Landa, David Wang 
Agents might look like software, but their path to maturity is fundamentally different. Traditional software is deterministic and verifiable. Agents are probabilistic and hard to supervise. The challenge isn’t creating agents (they appear to work almost immediately). The challenge is gaining confidence they’re ready to take business-critical actions.
Your team cannot consider their work done until agents graduate from prototype. How do you rescue your agents from prototype purgatory?
Today, we’re announcing the general availability of Agent Router Enterprise, a managed solution that lets your team establish consistent LLM routing, MCP connectivity, and supervision to graduate prototypes to production with confidence. Agent Router Enterprise runs on the battle-hardened Envoy AI Gateway, managed by Tetrate as its co-creator and driving force, deployed as a dedicated instance with enterprise security.
Connect with us to schedule a personalized demo, or read on for more details on how Agent Router Enterprise enables teams to graduate agents to completion.
Developing Agents Consistently
LLM Gateway For Consistently Simple Model Access
If your team is building agents at scale, you’ve seen this: developers managing API keys for OpenAI, Anthropic, Gemini, and a dozen other providers. Each service has its own endpoint and failure modes. Different engineers hardcode different base URLs, and when you want to standardize rate limiting or switch providers, changes ripple through multiple codebases.
Agent Router Enterprise gives you a centralized Model Catalog showing every model across all providers. Filter by capability, toggle models on or off with one click, and every application respects that change immediately.
For your developers: one API base URL. Agent Router Enterprise routes requests automatically. New engineers onboard faster because there’s only one integration pattern. Your team stops fragmenting across provider SDKs, and you get centralized control without bottlenecking builders.
If your team has negotiated contracts with OpenAI, Anthropic, or Google, you shouldn’t need to abandon them for centralized routing. Agent Router Enterprise supports Bring Your Own Key (BYOK) so you can maintain existing provider relationships while establishing consistent access patterns.
Add your provider API keys and traffic routes through your accounts automatically. Your developers still connect through the same unified endpoint. Your team gets standardized LLM access while you preserve volume discounts and existing commercial terms.
When a model hits rate limits or goes down, your applications keep running. Agent Router Enterprise automatically falls back across models and providers. Configure chains like Claude Opus 4-5 to GPT 5.1, or BYOK accounts to managed endpoints. Fallbacks work with any key configuration.
MCP Gateway For Tool Curation and Filtering
Inconsistent LLM access is just one problem. Ungoverned MCP tool access creates security risks and wasted effort. Agent Router Enterprise includes a curated MCP Catalog with managed servers for Slack, GitHub, Salesforce, and Google Workspace. MCP Profiles let you bundle specific capabilities from multiple servers into a single endpoint. Each profile gets its own URL path with OAuth or API key authentication, giving you precise control over which tools your agents can access.
LLM fragmentation is one problem. Ungoverned MCP tool access creates security risks and reduces model performance. Agent Router Enterprise allows you to curate a MCP Catalog of publicly available or your own MCP servers. MCP Profiles let you bundle specific capabilities from multiple servers into a single endpoint. Each profile gets its own URL path with OAuth or API key authentication, giving you precise control over which tools your agents can access.
Administrators control which MCP servers are available to your organization. Enable or disable individual servers from the dashboard, which shows how many are active versus disabled. This gives you a governed set of approved integrations without exposing the entire catalog.
A Single Control Point
Agent Router Enterprise routes all LLM and MCP requests through a single control point. You can enforce consistent best practices across every agent your team builds. No more discovering that one team burned through your monthly API budget in a weekend, or that another team accidentally exposed sensitive data through an ungoverned MCP integration.
This foundation of consistent development practices sets the stage for confident graduation to production.
Graduate Agents Confidently
Getting agents to run isn’t hard. Most agents work against production data from day one. The hard part is answering the question: when is this agent “ready” and how to keep it in a ready state?
Agent Router Enterprise gives you a measurable answer through behavioral metrics and guardrail scoring. Instead of relying on intuition about whether an agent is ready, you get dashboards that track output quality, consistency, and reliability over time. This establishes a shared definition of “complete” across your organization.
Tetrate extended the FINOS AI Governance Framework to cover agentic AI risks. Agent Router Enterprise comes pre-built with these FINOS controls and continuously supervises agent behavior to enforce them in production. The framework includes controls like data filtering from external knowledge bases, user/app/model firewalling, system acceptance testing, and data quality classification. Your team gets a defensible baseline for securing agentic AI without building governance infrastructure from scratch.
Guardrails enforce this framework by blocking banned topics, detecting bias, preventing prompt injections, and redacting PII before requests leave your network. Small Language Models run alongside the gateway to evaluate requests in real time, stopping problems before they reach external APIs.
Custom evaluators judge response quality continuously. When the factual consistency evaluator detects responses that don’t align with provided context or RAG data, it flags likely hallucinations and either retries with a different model or blocks the response. Your team needs visibility into transactions to troubleshoot and verify agent behavior. Every LLM request and MCP tool call generates OpenTelemetry-compatible traces and logs. Export to your existing observability stack: Jaeger, Grafana, Datadog, or any OTLP destination. Configure your endpoint in the dashboard and transaction data flows automatically.
Here’s the critical difference from traditional software: supervision doesn’t stop after deployment. Probabilistic agents drift over time. Their behavior changes as models update, context shifts, and usage patterns evolve. Agent Router Enterprise provides continuous measurement to track this drift and keep agents aligned with business intent. You’re not certifying once and walking away. You’re establishing ongoing proof that agents remain complete.
From Purgatory to Readiness
Agent Router Enterprise solves the two problems keeping your agents stuck: inconsistent development practices and no clear definition of done. Centralized LLM and MCP gateways give your team uniform patterns for building agents. Behavioral metrics, guardrails, and continuous supervision give you proof they’re ready for production. The result is faster releases, higher quality, and the ability to scale agents across your organization with confidence.
Schedule a demo to see how Agent Router Enterprise can establish agent readiness standards for your team. We’ll walk through your specific use cases and share best practices for moving agents from prototype to production.
Arif Setiawan, John Landa, David Wang 
