Discover the future of intelligent API traffic management. Learn how AI-powered gateways enable smarter routing, adaptive security, and real-time decision-making to optimize and secure API ecosystems.
Agent-to-agent communication is the next frontier of AI infrastructure. As teams decompose monolithic AI workflows into specialized agents — a research agent, a booking agent, a summarization agent — the calls between those agents become as important to govern as the calls from end users. With…
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Kong Agent Gateway is a new capability within Kong AI Gateway that extends our platform to more robustly cover agent-to-agent (A2A) communication. With this release, Kong AI Gateway now natively handles all three patterns of AI traffic in a product that is already being used by some of the largest…
[](/blog/product-releases/kong-agent-gateway)
Showback and chargeback are not the same thing. Most organizations conflate these two concepts, and that conflation delays action. Understanding the LLM showback vs chargeback difference is the first step toward effective AI cost governance. Both are valuable, and each has its place given certain…
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The Anatomy of Architectural Complexity Modern architectures now juggle three distinct traffic patterns. Each brings unique demands. Traditional approaches treat them separately. This separation creates unnecessary complexity. North-South API Traffic External APIs are your digital front door.…
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Executive Summary AI adoption has moved past the "honeymoon phase" and into the "operational chaos" phase. As enterprises juggle multiple LLM providers, skyrocketing token costs, and "Shadow AI" usage, the need for a centralized control plane has become critical. This guide explores how the AI…
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Managed Redis cache is a turnkey "Shared State" add-on for Kong Dedicated Cloud Gateways. It is designed to combine the performance of an in-memory data store with the simplicity of a SaaS product. When you spin up a Dedicated Cloud Gateway in Kong Konnect, you can now toggle on Managed Redis with…
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The Shifting Economic Landscape: The AI token economy in 2026 is evolving, and enterprise leaders must distinguish between low-cost input tokens and high-premium output tokens to maintain profitability. Agentic AI Financial Risks: The transition to agentic AI and multi-step reasoning models creates…
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Claude Code is Anthropic's agentic coding and agent harness tool. Unlike traditional code-completion assistants that suggest the next line in an editor, Claude Code operates as an autonomous agent that reads entire codebases, edits files across multiple directories, runs terminal commands, executes…
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Your dashboards scream 99.9% uptime. Latency stays well under 200ms. Error rates dwell at zero. These operational metrics look perfectly all right. Yet customer complaints continue to hit all-time highs. Your AI assistant prescribes incorrect medical dosages. Your cloud bill tripled overnight.…
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We have crossed a threshold in the AI economy where the competitive advantage is no longer about access to data — it’s about access to context. The "context economy" has arrived, defined by a fundamental shift from selling data products to selling context products as a service. This is the shift…
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AI agents are spreading across organizations rapidly. Each agent needs secure access to different Model Context Protocol (MCP) servers. Authentication becomes complex. Scaling creates bottlenecks. The dreaded "too many endpoints" problem emerges. You face a classic AI infrastructure headache. The…
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Agents are ultimately decision makers. They make those decisions by combining intelligence with context, ultimately meaning they are only ever as useful as the context they can access. An agent that can't check inventory levels, look up customer history, or verify pricing isn't autonomous — it's…
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In today's digital landscape, APIs are the backbone of modern applications, and AI is the engine of innovation. As organizations increasingly rely on microservices and AI-powered features, the API gateway has become the critical control point for managing traffic. But as LLM/GenAI and MCP requests…
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MCP servers expose all tools by default. There are two problems with this: security (agents get capabilities they shouldn't have) and performance (too many tools degrade LLM tool selection). The solution? Put a gateway between agents and MCP servers that filters tools based on who's asking. Default…
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The Kong MCP Registry acts as a central directory for AI agents and clients to access services that provide context or take action. For AI agents, think of it as a combination of a "Service Catalog" and a "Developer Portal." It offers the metadata, API endpoints, and capability definitions that…
[](/blog/product-releases/kong-mcp-registry-tech-preview)
The example below shows how an AI agent can be built using Volcano SDK with minimal code, while still interacting with backend services in a controlled way. The agent is created by first configuring an LLM, then defining an MCP (Model Context Protocol) endpoint that represents a promotions service.…
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