AI agents are only as capable as the tools they can reach. When an agent needs to query a database, file a support ticket, or pull data from a CRM, it has to find the right tool, authenticate, and invoke it — all at runtime. The Model Context Protocol (MCP) standardizes how agents communicate…
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GitHub just sent waves of budget panic across its developer base. Seat-based Copilot pricing is out. Consumption-based credits are in. And if you're building an AI-driven product today on flat-rate pricing? You're building a problem into your roadmap. GitHub just announced Copilot plans will shift…
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Key takeaways - An MCP server is a lightweight process that exposes tools, resources, and prompts to AI applications over a standardized protocol. - It acts as the bridge between an LLM-powered client and an external system — a database, API, file system, or SaaS product. - The protocol…
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Three-quarters of enterprises are now piloting or deploying AI agents. But here’s the problem: actually integrating those agents with enterprise applications is proving to be one of the hardest parts of the whole endeavor. That’s the core finding of a recent Gartner research note, How to Integrate…
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An enterprise AI gateway should provide a centralized point of policy enforcement for routing, governing, securing, and observing artificial intelligence traffic at scale. LiteLLM is one of many AI gateways that can cover the foundational AI connectivity needs teams often start with. For…
[](/blog/enterprise/kong-ai-gateway-vs-litellm)
At TDX earlier this month, Salesforce announced Headless 360: every capability in the platform now exposed as an API, MCP tool, or CLI command. Co-founder Parker Harris put the thesis bluntly last month: "Why should you ever log into Salesforce again?" That isn't a marketing question. It's an…
[](/blog/enterprise/salesforce-went-headless-the-world-must-follow)
The shift from single-model AI features to multi-agent pipelines is no longer a future concern — it's running in production today. MCP has become the de facto protocol for tool-calling, agent-to-agent (A2A) communication patterns are proliferating, and enterprise teams are wiring together complex…
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When enterprises started experimenting with LLMs, the governance challenge — while still difficult — was relatively contained once you had an AI Gateway for LLM traffic. You had a request going to a model, a response coming back, and a gateway in between to enforce policy. With the right solutions,…
[](/blog/product-releases/kong-agent-gateway)
Every few weeks, the AI-adjacent internet crowns a new corpse. SaaS is dead. OpenAI is dead. The IDE is dead. Humans writing code is dead. The structure is always the same: take a real observation, strip it of all nuance, and toll the death knell to feed the hype train. Rinse and repeat. The…
[](/blog/enterprise/cli-vs-mcp-enterprise-ai-governance)
The microservices revolution promised agility and scalability. Teams could deploy faster, scale independently, and innovate without monolithic constraints. You gain speed and flexibility, but you also multiply trust boundaries, identities, network paths, and policy decisions. Then came AI, and…
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Modern enterprise architecture faces a three-body problem. Three distinct traffic patterns pull your teams in different directions. External APIs serve mobile apps and partner integrations. Internal microservices communicate within Kubernetes clusters. AI and LLM calls flow to OpenAI, AWS Bedrock,…
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AI APIs don't get enough credit for how much weight they're actually carrying. These AI APIs aren't merely technical connectors. They're, in fact, cost drivers and potential revenue engines. And when something goes sideways, they're ground zero. In production, they behave nothing like the…
[](/blog/engineering/monetize-ai-apis)
AI connectivity is enterprise infrastructure that governs, secures, routes, observes, and optimizes all AI interactions —providing the control layer organizations need to transform experimental AI into production-ready systems. Executive Summary AI adoption has moved past the "honeymoon phase" and…
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As enterprises race to deploy multicloud architectures and Agentic AI, they face a common bottleneck: "state." To govern AI token usage, manage agent-to-agent communication, or optimize performance via caching, API and AI gateways require a persistence layer to synchronize data. We’re excited to…
[](/blog/product-releases/multicloud-cloud-gateways-managed-redis-cache)
As enterprises start deploying AI agents into real systems, a new architectural challenge is emerging. Agents need a reliable way to discover tools, services, and capabilities dynamically, instead of relying on hardcoded integrations. This is where the Model Context Protocol (MCP) ecosystem is…
[](/blog/engineering/mcp-registry-dynamic-tool-discovery)
In the burgeoning intelligence economy, AI tokens are a metered utility, but enterprise profitability now hinges on a critical distinction: output tokens can cost up to 10x more than inputs, creating a new, invisible risk for cost overruns, particularly with Agentic AI. Learn how Kong AI Gateway…
[](/blog/enterprise/ai-input-vs-output-cost-management)