Kong Adds AI Cost, Credential and Observability Controls to Konnect

Kong introduced AI Management in Kong Konnect on November 4, 2026, adding controls for AI spending, credentials, governed resource discovery and observability as enterprises move agents into production.

The cost-management, credential, context and basic observability components come with Kong AI Gateway on Konnect rather than as separate add-ons, so teams already running that gateway can use them without another purchase. The broader release also introduces governed discovery for models, Model Context Protocol (MCP) servers and agents.

Kong is addressing four operational gaps: spending that cannot be assigned to a team or application, credentials embedded in agent code or prompts, APIs repeatedly wrapped as agent tools, and the absence of a consolidated account of an agent’s activity. Those gaps become harder to manage when one task crosses multiple models, MCP servers, APIs, tools and other agents. Putting controls in the connectivity path allows policy and usage data to follow those interactions instead of being reconstructed by each application team.

Cost Attribution and Runtime Optimization

AI Cost Management combines gateway consumption data with request identity and context to attribute spending to business units, engineering teams, applications, agents, individual users, customers or tenants. Its price catalog centralizes provider token and spending rates and permits overrides for negotiated arrangements. Integrations with third-party AI tools extend attribution to coding harnesses, copilots and software-development workflows.

Teams can set budgets against those attribution groups and receive alerts as consumption approaches thresholds. The Konnect overview adds hard and soft budget options and a dashboard for comparing spending with budget ceilings by team, harness or repository.

Optimization is applied at the infrastructure layer through intelligent model routing, semantic caching, prompt caching, prompt compression and reasoning controls. Kong says the potential benefit varies by workload: semantic caching is particularly suited to repetitive or similar requests, while routing can matter more when expensive models handle tasks that lower-cost alternatives could perform. Opportunities, a forthcoming feature for finding additional efficiencies and measuring optimization savings, is marked as coming soon rather than included in the initial release.

Per-User Credentials for Agent Traffic

Token Vault launched with per-user three-legged OAuth in Kong AI Gateway 2.3 and is included with all Kong Gateway subscriptions. A user and agent authenticate to an MCP server proxy using corporate identity-provider credentials. Access, logging, rate limiting and other gateway policies are evaluated before an upstream credential is loaded.

On first use, the gateway can return a short-lived enrollment link through MCP URL elicitation or a synthetic tool. The user completes the upstream service’s normal OAuth consent flow, after which the gateway encrypts the resulting refresh tokens with the customer’s key and sends the encrypted envelope to Kong Identity for storage.

On later authorized calls, the gateway decrypts the credential in memory with a private key read from the customer’s secrets manager, inserts it into the upstream request, and refreshes and re-encrypts expired tokens. Kong says the agent receives the response without seeing the token. Konnect stores the encrypted tokens but lacks the key needed to open them; removing a user’s policy access takes effect immediately even if the upstream credential has not yet been revoked.

Governed Discovery and Operational Visibility

Kong AI Registry covers commercial, self-hosted and custom models, MCP servers, and internal and third-party agents. Developers and agents can discover sanctioned resources instead of depending only on connections hard-coded into each agent. Registration alone does not grant access: AI Gateway supplies authentication, authorization, policy enforcement, traffic management and observability at runtime, with rate limits and security guardrails among the controls.

Context Mesh generates governed MCP servers from existing APIs and MCP servers, combines multiple sources into composites and can deploy the resulting servers to AI Gateway. With Code Mode, an agent searches the available API surface, retrieves the relevant schema and executes the operation rather than loading every tool definition. It can also compose operations with loops, conditionals and error handling. Kong had already made Context Management generally available as part of AI Management on September 30, 2026; that earlier availability is background to the new packaging.

AI Observability currently provides an estate overview, prebuilt and custom dashboards, an Explorer for ad hoc queries and connectivity logs. Dashboards break down traffic and spending by model, agent, MCP tool, team or environment. Shared views are subject to role-based access controls, while OpenTelemetry and metrics exporters allow the telemetry to feed existing observability systems.

Advanced session-and-turn tracing across agent steps, tool calls and model calls is scheduled for late November or early December 2026. The planned workflow lets teams inspect an individual turn’s inputs, outputs, tokens, cost and latency alongside the policy checks and tool calls that produced the result.

The release extends the gateway’s role beyond passing or blocking individual AI requests: it becomes a shared layer for attaching identity, budgets and tool-access policy to agent activity. Existing Konnect gateway customers receive the core cost, credential and discovery controls within that package, while the full session audit trail and evidence of optimization savings belong to the next stage of the rollout.