Cequence AI Gateway

An AI Gateway is a secure infrastructure layer that bridges the gap between AI agents and enterprise applications. It acts as a universal translator, converting requests from AI models into native API calls that your business systems—like CRMs, development tools, and internal databases—can understand and execute. By managing the interaction between agents and APIs, an AI Gateway allows organizations to move AI initiatives from experimental prototypes to secure, production-ready assets.

An AI Gateway works as a secure bridge that translates an AI agent’s requests into the technical API commands your business systems understand. When an agent wants to perform a task, the gateway first verifies its identity through your existing security providers to ensure it has permission to access that data. It then applies real- time guardrails to block malicious activity or unauthorized actions before safely executing the command within your applications. Throughout this process, the gateway creates a clear audit trail, giving you full visibility and control over exactly how AI agents are interacting with your internal and external tools.

The features of AI Gateway are designed to provide the security, connectivity, and visibility necessary to move AI agents into production. Key capabilities include:

• Native MCP Support: Model Context Protocol (MCP) acts as a universal translator, allowing AI agents to communicate seamlessly with diverse enterprise applications without custom coding.
• Automated Tool Discovery: By simply uploading OpenAPI specifications, AI Gateway automatically transforms traditional APIs into “agent-ready” tools that AI models can instantly understand and use.
• Identity and Access Governance: AI Gateway integrates with standard Identity Providers (IdP) using OAuth 2.0 to map agent identities to specific permissions, ensuring AI agents only access authorized systems and data.
• Agent Personas: Agent Personas enable users to describe, in plain English, a job description for an agent. The AI Gateway will then ensure that the agent only has access to the tools it needs to do its job and doesn’t stray from its job description. This not only makes agents much safer to use, but also makes them perform better.
• Advanced Security Guardrails: AI Gateway applies real-time protections and context-aware policies to block prompt injections and business logic abuse.
• Indelible Audit Trails: AI Gateway provides a centralized, real-time view of all AI-to-API traffic, offering a complete record of which agents are accessing which systems and exactly what actions they are taking.
• Enterprise-Grade Scalability: Built for high-performance environments, AI Gateway supports horizontal scaling and flexible deployment options, including managed SaaS or private cloud environments to meet data residency requirements.

‍

AI Gateway is designed for broad compatibility, providing native support for Model Context Protocol (MCP) to connect a wide variety of AI agents with enterprise applications. It’s built to be flexible, allowing for manual integration with virtually any LLM or agentic tool your organization utilizes. By acting as a universal translator, it enables these agents to securely interact with a catalog of over 140 enterprise applications, such as Salesforce, Jira, Slack, and Confluence, as well as any internal or custom-built APIs.

The AI Gateway secures agentic AI by serving as a centralized management hub that enforces consistent security policies and governance across all AI-to-application interactions. This approach prevents common risks like rogue MCP servers and unauthorized data access by providing a trusted registry of vetted endpoints. To ensure robust compliance and identity management, the gateway integrates directly with OAuth 2.0-compliant identity providers. This enables granular authentication and authorization, ensuring only authorized agents and users can access specific systems and data. Features like session binding protection further enhance security by locking authenticated sessions to originating IP addresses, effectively preventing token theft and unauthorized reuse. Continuous visibility is maintained through built-in monitoring and audit logging, which tracks every agent-API interaction. This detailed visibility allows security teams to identify suspicious patterns, prevent business logic abuse, and ensure that AI agents operate within established organizational bounds.

AI Gateway is designed for rapid deployment, allowing organizations to make enterprise applications agent-ready in just a few minutes. Its no-code approach simplifies the process of converting existing internal, external, or SaaS APIs into tools compatible with the Model Context Protocol (MCP). By removing the need for extensive coding or developer up-skilling, teams can transition from initial setup to operational status with just a few clicks.

AI Gateway is built on a SaaS-based, cloud-hosted architecture specifically engineered to support the scalability and performance requirements of the world’s largest organizations. This infrastructure allows the gateway to handle high-throughput applications through horizontal scaling, ensuring that as your volume of AI-to-application interactions grows, the system maintains consistent reliability and low-latency performance. AI Gateway can also be deployed on-premises if desired.

AI Gateway utilizes the Model Context Protocol (MCP) as a universal translation layer that bridges the gap between AI agents and enterprise applications. By adopting this open standard, AI Gateway acts as a standardized way for agents to discover and interact with external data sources and tools at runtime. This eliminates the need for manual coding or custom integrations for every new AI-to-application connection.

The Act distributes obligations across six articles, but for deployers, two are particularly important. Article 14 requires that high-risk AI systems support effective human supervision — you must be able to detect anomalies, override outputs, and intervene when something goes wrong. Article 26 makes you accountable for monitoring the system in operation, retaining logs for at least six months, and reporting serious incidents.

Articles 9, 12, and 13 primarily fall to your AI vendor, but they affect you directly: if a provider cannot produce its risk management documentation, logging capability confirmation, or transparency disclosure, the gap lands on your deployment regardless.

More than most security teams expect. Article 26 makes you accountable for the AI you deploy regardless of who built it — monitor it, retain logs for at least six months, assign qualified human oversight, and report serious incidents. Building it yourself is not a prerequisite for owning the compliance obligation.

What you cannot own — and must demand from vendors — are the provider-side obligations: Article 9 risk management documentation, Article 12 confirmation that logging is technically enabled, and Article 13 transparency documentation covering capabilities, limitations, and known risks. The Act puts those on the provider, but if your vendor cannot produce them, the exposure touches your deployment.

Send those requests in writing now. Vendor responses — or the absence of them — become part of your compliance record.

For Cequence AI Gateway specifically: Cequence holds Articles 9 and 10 obligations for the intelligence layer and can provide Annex IV documentation under NDA. For foundation models routed through the Gateway, the GPAI provider holds those obligations.

Cequence AI Gateway addresses the deployer side operationally. Agent Personas enforce least-privilege access, with tool-call activity logs capturing agent identity, data accessed, user context, and full request/response flows — all exportable to your SIEM. Anomaly detection, policy enforcement, and kill-switch controls satisfy the intervention capability Article 14 demands. Automatic agent discovery gives your legal counsel the inventory they need to make the Article 49 registration determination.

‍

Cequence AI Gateway provides granular controls to limit token and monetary spend limits, including tokens per minute, day, and month, as well as monetary spend per day and month. Admins can also specify allowed LLM models and tie all of these options back to a particular team.

The Prompt Guard feature in Cequence AI Gateway screens LLM prompts for injection, jailbreak, and system-prompt-extraction patterns before they reach the model. Think of it as semantic threat detection for LLM traffic. Several categories are included, and organizations can also create custom rules, or “Guards.”