Cequence AI Gateway
The missing agentic AI governance layer connecting and protecting your applications and data
The Cequence AI Gateway provides the security, governance, and control enterprises require to confidently deploy agentic AI workflows at scale.
Agentic Zero Trust Architecture
Extends zero trust to every agent’s actions with a behavioral containment boundary
Agents Personas
Binds agents to a job description, a defined tools, APIs, and skills, and guardrails
Runtime Guardrails
Monitoring, logging, rate limiting, and sensitive data protection
“The Cequence AI Gateway is a major unlock for us to safely expose internal API to MCP servers as it looks to solve many of the authentication/authorization gaps left behind in the MCP design.”
— CISO, Fortune 50 Telecommunications Company
Agents are Your Newest, Most Powerful Insider
An agent with access to your applications and data is much more than a tool, it’s a privileged insider operating at machine speed. Without proper governance, you’re left with greater insider risk than ever before. Cequence Agent Personas constrain agents to their job description, judge every behavior against that description, and prevent agent overreach.
AI Gateway – Security, Governance, and Control
Agentic Zero Trust Architecture
AI Gateway authenticates agents and then verifies every action taken. The gateway enforces policy inline in the request path, for the full session, on every tool call. Independent research from Dr. Chase Cunningham and Anthropic arrived at the same architecture Cequence had already built.
Agent Personas
Define your agent’s job description in plain English to generate a tailored “Agent Persona” with only the tools, APIs, skills, and permissions it needs to operate. Agent Personas automatically apply strict boundaries and protections based on what a specific agent is actually permitted to do. The result is minimized risk, better performance, and lower operational costs.
Security Built In, Not Bolted On
AI Gateway provides trusted registries of MCP servers, APIs, and Skills for agents to access. Official application APIs are transformed into MCP-compatible tools with just a few clicks. Built-in guardrails such as automated tool risk scoring and rate limiting keep agents from going rogue.
End-to-End Authentication and Authorization
Integration with OAuth 2.1-compliant identity infrastructure ensures adherence to organizational identity policies and permissions. Built-in token lifecycle management provides appropriate identity-based access to systems and data while preventing unauthorized AI agent access.
Monitoring and Visibility
Real-time visibility into AI-tool traffic with full audit logging enables detailed tracking of user, agent, and tool behavior, which applications are being accessed, and what API calls agents are making. MCP server and tool usage activity trends are available at-a-glance.
Sensitive Data Protection
Apply DLP scanning to AI agent requests and MCP server responses to detect and prevent unintended sensitive data exposure. Monitor, redact, and block sensitive data across more than 100 out-of-the-box detection types. Easily integrates with existing DLP infrastructure.
AI Discovery
You can’t govern what you don’t know exists. Cequence provides visibility into existing agent and AI use, discovering sanctioned and shadow AI alike. It surfaces every agent, MCP server, and LLM provider enterprise-wide from existing SIEM logs.
Enablement Made Easy
The Cequence AI Gateway is the easiest, fastest, and safest way to open applications to the benefits of agentic AI. Delivered as a SaaS-based solution, no new infrastructure is required. And the built-in security, governance, and control capabilities ensure you can safely enable agentic workflows.
Built for the Enterprise
AI Gateway supports SaaS-based and on premises deployments, offering the scalability, performance, and reliability that the world’s largest organizations demand. Continuous environment monitoring, OAuth 2.1 IdP support, RBAC, and discrete pre-prod/prod modes are but a few of the AI Gateway’s enterprise capabilities.
Complements Existing Cequence Deployments
Integration with Cequence’s API Security and Bot Management solutions provides access to enhanced API specs that boost agentic AI performance, accuracy, and cost effectiveness. Cequence also delivers protection from agent-fueled attacks, abuse, and fraud against all applications and APIs, including those connected to the AI Gateway.
Standard Gateway vs. Cequence Gateway
A standard gateway only authorizes the call. Cequence governs the agent.
| Capability | Why It Matters | Standard AI Gateway |  |
|---|---|---|---|
| What it connects and sees | |||
| AI and API discovery | You can't govern the unknown. Agents can reach undiscovered shadow models, APIs, and MCP endpoints. |  |  |
| Proxy MCP tool calls | MCP is the most common agent channel; every call should pass one control point. | | |
| Trusted MCP registry | Agents should reach only vetted MCP servers. | | |
| Proxy direct API calls | Agents call APIs directly, not only through MCP. That path needs governing. | | |
| Governed API registry | Code-writing agents build against API specs. One token, one registry keeps that in bounds. | | |
| Multi-model LLM routing | Routing and cost control across models is a platform-team need, not a security control. | | |
| Governing the agent as an insider | |||
| Agent Persona (the guardrail) | Agents need boundaries created from written job descriptions, ones that holds when the model does not. | | |
| Dynamically scoped access | Least-privilege access policies generated from job descriptions. | | |
| Behavioral Identity | Actions judged based on authentication (who) AND behavior (what). | | |
| Rogue agent containment | Agents go rogue with authentication alone, because every action is authorized. | | |
| Immediate detection | Agents have no warm-up window. Detection has to start at the first action. | | |
| Data protection | |||
| Contextual API and MCP sensitive-data detection | Sensitive data moves on every channel; pattern matching alone misses most of it. | | |
| Coverage across tool calls | Data leaks across a sequence of calls, not in one. A single-hook redaction fails the audit. | | |
| Immediate inline response action | Detection is not enough if you cannot act. | | |
| Enterprise | |||
| Secure self-service rollout | Security cannot sit in the path of every rollout without becoming the bottleneck. | | |
| Tool-level audit trail | Every tool call should be attributable. | | |
| Agent-level audit trail | Attribute behavior to the agent and its person, not just the call. | | |
| SIEM integration | Findings accessible from the tools your SOC already runs. | | |
| Latency | Inline enforcement should disappear into the latency the workflow already pays. | | |
| High availability | Enforcement in the request path must not be a single point of failure. | | |
Industry Recognition
Co-author of the CIS Controls AI companion guides (LLM, Agent, and MCP). Named reference implementation in independent agentic zero-trust research, with independent convergence on the same architecture. Co-chair of TM Forum’s AI-Native Blueprints, leading Agentic Interaction Security.
Learn more about AI Gateway
Frequently Asked Questions
What is an 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.
How does an AI Gateway work?
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.
What are the features of AI Gateway?
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.
Which AI agents does AI Gateway support?
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.
How does AI Gateway ensure security and compliance for AI agents?
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.
How quickly can AI Gateway be set up?
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.
How scalable is AI Gateway?
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.
How does AI Gateway utilize model context protocol (MCP)?
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.
What specific technical and organizational controls does the EU AI Act require for high-risk AI systems?
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.
My organization deploys AI built by third-party vendors. What do I actually own under the Act?
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.
Ready to Get Started?
Get a live, personalized demo and see how the Cequence AI Gateway can transform how your employees, partners, and customers interact with your organization.