Kong announced Kong Context Management as generally available in Kong AI Management on September 30, 2026. It is designed to let AI agents discover and use enterprise APIs, MCP servers and other data sources without loading every possible tool definition into every model request.
Why Kong is adding a context-management layer
Turning a large enterprise API into MCP tools can create hundreds of separate operations. If all of those definitions are placed in an agent’s prompt, the model has to process a large amount of information before it can choose a single action. Kong identifies three resulting problems: a context-window tax, more difficult tool selection, and higher token use and latency.
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Context Management addresses that problem by separating discovery from execution. Instead of presenting an entire API surface up front, the system gives the agent a compact way to search for the capability it needs, obtain the relevant schema, and then execute the operation. Kong positions this as infrastructure for production agents rather than simply another interoperability connector.
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Kong’s Code Mode is the runtime mechanism for this approach. It exposes a compact MCP interface rather than a long list of individual tools. The agent then works through the API surface as needed.
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- Search: The agent searches the available API capabilities through the compact interface.
- Retrieve: It requests the schema for the operation that matches the task.
- Execute: It invokes the selected operation after receiving the necessary details.
- Compose: For a multi-step job, it can combine operations in code, including loops, conditionals and error handling.
That last step is important for workflows that would otherwise require repeated model-to-tool exchanges. A single generated program can call several enterprise operations, handle a failed response and continue or stop according to the logic of the task. Kong’s stated goal is to reduce the amount of context and coordination the model must carry on each request; the announcement does not provide an independent benchmark quantifying the savings.
What Context Mesh contributes
Kong’s February 10, 2026 Context Mesh launch describes the mediation layer between agents and APIs. Kong says it can automatically discover APIs managed in Kong Konnect, assemble curated toolkits for an agent, generate MCP interfaces, and deploy them through Kong AI Gateway.
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| Capability | What Kong describes | Practical purpose |
|---|---|---|
| API discovery | Automatic discovery of APIs managed in Kong Konnect | Find relevant enterprise capabilities without manually registering every operation with each agent |
| Curated toolkits | Selection of context for a particular agent | Expose an appropriate subset of enterprise functionality |
| MCP generation | Instant generation of MCP tooling | Make selected APIs usable through an agent-compatible interface |
| Deployment | Deployment through Kong AI Gateway | Put the agent-facing interface behind the organization’s gateway layer |
| Controls | Inherited access controls and runtime policy enforcement | Apply existing permissions and policies as agents discover and call capabilities |
Kong’s documentation describes Context Mesh as a mediation layer that scales to large API surfaces without flooding the model’s context. The broader Context Management announcement also names MCP servers and enterprise data sources as part of its scope. Kong has said the product direction will extend beyond REST APIs toward event streams and other enterprise sources; that is a roadmap direction, not a claim that every such source is already available in the generally available release.
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Interoperability alone does not decide what an agent should be allowed to see or do. Context Management is intended to combine four controls in one operating path:
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- Discovery: identify APIs and other capabilities that are available.
- Selective exposure: choose the context and toolkit appropriate for an agent or task.
- Execution: run the selected operation, including composed multi-operation code in Code Mode.
- Gateway governance: enforce inherited access controls and runtime policies through Kong AI Gateway.
This arrangement matters when an organization has already invested in APIs as its integration layer. Marco Palladino, Kong’s chief technology officer and co-founder, said: “Organizations have spent years building APIs as the nervous system of the enterprise. Context Mesh allows us to reuse that investment to power agents instead of starting from scratch.”
Does it reduce token cost and latency?
The architecture is designed to reduce both. Runtime search avoids sending every tool definition in every prompt, while schema retrieval limits detailed descriptions to the operations the agent actually needs. Code Mode can also reduce the number of back-and-forth model calls in a multi-step workflow by composing several actions in one execution.
Those are mechanisms, not published performance results. Kong’s announcements do not state a universal percentage reduction in tokens or latency, and they do not include independent benchmark results or customer measurements. Actual results will depend on API size, schema complexity, model behavior, network conditions and the number of operations in a workflow. Kong has not published pricing in the materials announcing Context Management.
Availability and product scope
Kong Context Management became generally available in Kong AI Management on September 30, 2026. The Context Mesh launch on February 10, 2026 established the discovery, toolkit and mediation concepts that Kong now associates with efficient agent access. Together, the announcements describe a platform approach covering APIs, MCP servers and enterprise data sources, with additional context types planned over time.
What teams should evaluate before adopting it
Organizations assessing this approach should test the operational details that the announcements do not quantify:
- Whether the APIs and data sources an agent needs can be discovered and represented through the available interfaces.
- How much schema information is retrieved at runtime and how that affects the chosen model’s context limits.
- Whether inherited permissions and runtime policies match the organization’s separation-of-duties requirements.
- How Code Mode handles failed calls, retries, loops and partial results in the team’s highest-value workflows.
- What observability is available for discovery, schema retrieval, policy decisions and downstream API execution.
- The deployment model, operational responsibilities and total cost, including model-token consumption and gateway usage.
Why the announcement matters for enterprise AI
Kong’s argument is that MCP can make systems interoperable but does not by itself solve context selection or governance. Large enterprises need agents to find the right capability at runtime, expose only what the task permits, and execute it under existing controls. Context Management and Context Mesh target that gap by treating enterprise context as something to mediate and govern rather than something to paste wholesale into every prompt.
Kong’s February 2026 press release attributed a warning to a January 2026 Gartner report: 40% of agentic AI initiatives could be at risk of cancellation by 2027 if organizations fail to bridge the integration gap. That figure is reported by Kong; it should not be read as an independently verified Gartner publication based on the information available here.
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