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…
Tool discovery for AI agents In early agent implementations, tools are often statically configured inside the agent. For example: { "mcpServers": { "weatherServer": { "command": "uv", "args": "run", "weather_server.py" ] }, "githubServer": {…
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…
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…
Gartner's strategic planning assumption: by 2029, 50% of software application providers will be forced to share their context layer externally for third-party orchestrators to stay relevant. Today, that number is less than 2%. This isn't a gradual evolution. It's a structural shift in where value…
Gartner's strategic planning assumption stops you in your tracks: by 2029, 50% of software application providers will be forced to share their context layer externally for third-party orchestrators to stay relevant, versus less than 2% today. Let that number sink in. Nearly every enterprise…
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…
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…
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…
AI spending is exploding across the organization—but often not in the ways leadership approved or finance can track. Development teams spin up LLM connections to ship features faster. Data teams provision GPU clusters for experiments that get abandoned. Multiple teams solve the same problem three…
Let's be honest about what's happening inside most enterprises right now. Development teams are under intense pressure to ship AI features. The mandate from leadership is clear: move fast. And so they do. They spin up LLM connections, integrate third-party AI tools, and route data to models without…
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.…
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…
If you've used KAi before, you'll notice the difference immediately. The enhanced version has deeper access to your Konnect environment and new tools for debugging, analytics, and configuration management: The new KAi is more capable because it's backed by a structured set of tools that can query…
The report identifies a mindset trap that's holding most organizations back: "inside-out" integration thinking. Inside-out means viewing integration from the perspective of only prioritizing the reuse of legacy integrations and architecture (i.e., simply wrapping existing integrations with MCP and…
The first pillar is enablement. Developers need tools that reduce friction when building AI-powered applications and agents. This means providing: The goal isn't to replace existing development workflows but to extend them. Developers should be able to build agents using familiar paradigms while…
Secure your AI infrastructure with prompt guards, PII sanitization, and centralized governance. Control LLM costs with token-based rate limiting and semantic routing across providers.