AI Governance for Production Agents — Cortega
Production AgentsPlans for AI in production →

AI governance for production agents.

Agents that behave in production the way they did in testing, with routing, security, and model management on infrastructure you control.

AI agent quality and security readinessAutomated routing for performance, latency and costAutomated model management
Cortega AI Border Gateway · in production
YOUR AGENTS & APPS Support agent Workflow agent Coding agent App backends CORTEGA AGENT QUALITY & SECURITY turn 4: "send refund to new acct" ✕ blocked AUTOMATED ROUTING p95 latency over 2 s → fail over to next provider team budget at 90% → route to lower-cost model AUTOMATED MODEL MANAGEMENT model deprecated → study on your traffic → upgrade ✓ switched, no code change routed Models frontier · open-weight · local MCP servers tools · data · APIs multi-gateway · multi-region
The problem

What goes wrong

01

Production isn’t testing

Agent performance, quality, latency, and errors differ from what developers tested.

02

Agents under attack

Social engineering and fraud from LLMs and hackers, spread across requests and conversations.

03

Policy violations

Violations happen regularly in production.

04

Models keep changing

Deprecations, upgrades, and new alternatives never stop.

05

Scaling gets complex

Multiple LLM and MCP gateways to run and scale.

06

Insights are hard

Hard to thread context across calls and agents: which agents call which MCP servers, what apps exist, what each task costs.

In the console

One console

Cortega Agents — quality & anomalies
Cortega Agents: verifier, scorer, sentiment, trajectory, and workload agents
Cortega insights across agents and model calls
MCP servers registered and governed in Cortega
Cortega model recommendations based on measured traffic

From the Cortega console.

Pricing

Plans

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MAKE YOUR NEXT MOVE

Put governance
into practice.

Explore how Cortega fits your AI environment.

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