AI Management in Kong Konnect: Govern Agents at Scale | Kong Inc.

Agents are moving from demos into production, and the platform teams running them keep hitting the same four problems: spend nobody can attribute, credentials that security cannot manage, enterprise APIs rewrapped as agent tools team by team, and no single view of what an agent actually did. Today we are introducing AI Management in Kong Konnect, a set of capabilities that answer those four problems in one control plane.
Agent spend grows quietly. Usage shows up as an item line on a shared cloud or model invoice, long after the agents that caused it have run, and most teams cannot tell which team, harness, or app drove it.
AI Cost Management solves this with brand new cost governance solutions:
If you want to learn more, check out [the deep-dive blog on AI Cost Management](https://konghq.com/blog/product-releases/kong-ai-cost-management)the deep-dive blog on AI Cost Management.
Agents need enterprise credentials to do useful work, and today they often hold them directly, in code, configuration or prompts. Every agent that holds a credential is another place it can leak. The problem grows with MCP: each server provider supports its own auth flow, some use OAuth with user consent, others use API keys, some leverage enterprise IdP on-behalf-of (OBO) flows, service accounts, etc. So when an agent accesses multiple MCP servers—internal and external—access control becomes a nightmare for developers and a major liability for security teams. When, not if, an agent behaves unexpectedly, controlling the blast radius of credential exposure is critical. Kong Token Vault enforces the principle of least privilege, preserves end-to-end auditability, and enables immediate, targeted credential revocation.
Token Vault solves these issues by ensuring:
[Learn more >](https://konghq.com/blog/product-releases/kong-token-vault)Learn more >
Enterprises already have APIs and MCP servers, but making them truly agent-ready — which means they must be token- and context-efficient — usually means either wrapping each one by hand (in the case of APIs) or rewriting existing MCP servers to support methods such as code mode.
Context Mesh solves this and generates governed MCP servers from the APIs and MCP servers you already run:
The generated servers use code mode. Instead of loading a definition for every tool into the agent's context, the agent writes short code that calls only the tools it needs. That keeps token use and context size down as the estate grows, so agents can work across many APIs and MCP servers without filling their context with tool definitions.
And of course, all of these MCP servers can be immediately deployed to a Kong AI Gateway for full governance.
[Learn more >](https://konghq.com/blog/product-releases/context-mesh)Learn more >
When an agent misbehaves or an audit asks what it did, request metrics are not enough. AI traffic has to be read as sessions, steps and model calls, and most tooling was built for API requests.
AI Observability reads AI traffic as sessions, steps and model calls, through three views:
[Learn more >](https://konghq.com/blog/product-releases/kong-advanced-ai-observability)Learn more >
Context Mesh, Token Vault, AI Cost Management, and Basic Observability are included with Kong AI Gateway on Konnect, with no separate SKU. Teams already running the gateway in Konnect can use these capabilities without buying anything extra. If you want to learn more, reach out to your Kong point of contact or [book a demo](https://konghq.com/contact-sales)book a demo.
Accelerate deployments, reduce vulnerabilities, and gain real-time visibility.
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