Kong AI Gateway handles the traffic patterns production agent systems depend on. The Straiker integration can be applied to configured traffic so the relevant request context is evaluated before the action continues.

For LLM traffic, Straiker evaluates risks such as prompt injection, jailbreak attempts, sensitive-data exfiltration, unsafe output, and policy violations in the context of the broader agent workflow.

At runtime, the flow is intentionally simple: Kong receives the AI traffic, Straiker evaluates the configured context and returns a security verdict, and Kong enforces the resulting action in the request path. That lets customers preserve Kong as the enforcement point while adding security analysis designed specifically for agent behavior.

1. - An agent retrieves a document or message containing hidden instructions to move sensitive information.
2. - The agent follows the instruction and attempts to call an MCP tool that can send data to an external destination.
3. - Kong mediates the MCP request as part of the normal traffic path.
4. - Straiker evaluates the semantics of the request and tool arguments and returns a block decision when the action violates policy or matches malicious behavior.
5. - Kong blocks the transaction. If the behavior indicates that the agent itself is compromised or unsafe, the response can escalate beyond that single request.

Per-request enforcement solves the immediate transaction. It does not necessarily solve the actor generating the transaction. A compromised agent can try a different tool, start another session, or pass malicious context downstream.

Straiker's [_Agentic Kill Switch_](https://www.straiker.ai/blog/agentic-kill-switch-for-coding-agents)_Agentic Kill Switch_ is the escalation path for that case. Security teams can move from blocking an unsafe action to containing the agent itself, including revoking tools, suspending a session, freezing memory, or taking the agent or a fleet of agents offline. Kong continues to enforce traffic policy; Straiker provides the agent-level response when the security problem extends beyond one transaction.

That distinction matters operationally. A security team can stop the malicious action in the traffic path first, then remove the agent's ability to continue acting when the evidence points to broader compromise.

The requirements behind this integration are familiar from customer deployments and active proofs of value across healthcare, life sciences, health insurance, and financial services. In those environments, AI traffic can touch sensitive data and high-impact systems while multiple application teams share the same platform infrastructure.

Security teams in those organizations typically want more than another block list. They want the evidence around the decision: which agent initiated the behavior, what it was connected to, what happened earlier in the session, which attack path succeeded during testing, and whether the event should be contained at the request level or escalated to the agent itself.

Kong provides a consistent place to govern and observe the AI traffic. Straiker adds the agent-specific security context and response around that traffic. This gives platform and security teams a shared operating model instead of separate architectures and separate views of the same workflow.

The lowest-friction motion is to start with production workflows already routed through Kong, prove the runtime value against real traffic, and expand the security program only where the customer needs it.

1. Select one or more production agent workflows already routed through Kong AI Gateway.

2. Start in detect or observe mode and review the security decisions against normal production behavior.

3. Move high-confidence controls into inline enforcement through Kong.

4. Add inventory, posture, and adversarial testing where the security team needs deeper context around the agent and its connected tools.

5. Define Agentic Kill Switch criteria for incidents where the agent itself must be contained, not just the latest request.

This gives customers a practical adoption path: protect what is already running, validate the signal in their own environment, and add broader agent security capabilities without rebuilding the gateway architecture.

As agents gain access to more tools and data, platform and security teams need to stay aligned on the same execution path. Kong gives platform teams the controls to govern AI traffic consistently. Straiker gives security teams the agent-specific insight to understand how that traffic can be abused and what to do when the agent itself becomes unsafe.

Kong customers should not have to choose between standardizing AI traffic through the gateway they already operate and giving security teams deeper visibility into agent behavior. The partnership is designed to bring those two requirements together.

Kong remains the place where AI traffic is routed, governed, observed, and enforced. Straiker adds a complementary agent security layer that can provide deeper security insight, adversarial validation, runtime decisions, and agent-level containment as customer needs grow. That lets teams start with a focused integration and expand without creating a second AI traffic architecture. For a deeper technical look at how Straiker extends Kong AI Gateway across adversarial testing, runtime defense, and agent-level containment, read [_Straiker’s guide to the Kong + Straiker integration_](https://www.straiker.ai/blog/kong-straiker-agentic-ai-security?utm_source=kong&utm_medium=partner_blog&utm_campaign=kong_straiker_integration&utm_content=straiker_deep_dive)_Straiker’s guide to the Kong + Straiker integration_.

To learn more, visit [_Straiker_](https://www.straiker.ai/?utm_source=kong&utm_medium=partner_blog&utm_campaign=kong_straiker_integration)_Straiker_, [_request a demo_](https://www.straiker.ai/demo-request?utm_source=kong&utm_medium=partner_blog&utm_campaign=kong_straiker_integration&utm_content=demo_cta)_request a demo_ to evaluate the integration against your own agent workflows, or explore the [_Kong Technology Partner Program_](https://konghq.com/partners)_Kong Technology Partner Program_.

**How does Straiker complement Kong AI Gateway's existing security capabilities?**

Kong provides the gateway controls, observability, authentication, AI governance, and native protections used to manage LLM, MCP, and A2A traffic. Straiker adds agent-specific security analysis and response, including deeper context around agent behavior, connected tools, attack paths, and cases where the agent itself may need to be contained.

**What types of AI traffic can the integration evaluate?**

The integration is designed around the same traffic patterns Kong AI Gateway manages: LLM requests and responses, MCP and tool calls, and agent-to-agent communication. The exact context and enforcement behavior can be configured for the customer's deployment. This can span coding, homegrown, and other types of agents.

**Can customers start by observing security decisions before they enable blocking?**

Yes. A detect-first rollout gives platform and security teams a way to review decisions against normal traffic, validate the signal in their own environment, and move high-confidence policies into enforcement through Kong when they are ready.

**How does the integration help platform and security teams work together?**

Both teams can work from the same AI traffic path rather than introducing a separate proxy or security architecture. Kong remains the gateway and enforcement point, while Straiker adds the agent-specific security context security teams need to investigate behavior, validate risk, and determine the appropriate response.

**What happens when the security issue extends beyond a single malicious request?**

Kong can enforce the immediate request-level action. If the evidence indicates that the agent itself is compromised or unsafe, Straiker can escalate to agent-level containment through the Agentic Kill Switch. That separates immediate traffic enforcement from the broader incident response needed to stop repeated malicious action.