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.
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…
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As organizations transition from experimental AI to production-grade systems, they often face a fragmented landscape of unmanaged LLM providers, complex tool integrations, and escalating security risks. Without a centralized orchestration layer, developers struggle to maintain consistent…
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Scaling LLM and agentic AI adoption from pilot programs to enterprise-wide deployments is a massive logistical rollout. As AI and agentic usage grow, so does a nagging question for leadership: Are agents using the right tools to get the job done? While raw infrastructure metrics might tell you if a…
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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,…
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Every enterprise moving AI into production is about to face a familiar problem in an unfamiliar form: the cost explosion, but for LLMs. This is very similar to what happened with cloud. In the early days of cloud, teams spun up infrastructure with no visibility into who was consuming what. Finance…
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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 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…
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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…
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The AI coding and Agent Harness approach is no longer experimental. This is likely the most impactful agentic AI use case in production today, and Claude Code is one of the solutions really leading the charge. But as engineering teams race to adopt Claude Code across their organizations, a critical…
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Your traditional monitoring dashboards may show perfect infrastructure health, but that doesn't guarantee your large language model (LLM) application is running correctly. AI observability is the crucial fourth pillar that adds behavioral telemetry to detect and troubleshoot unique issues like…
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Last week, Gartner published a report titled MCP Servers Will Fuel the Next AI Revenue Surge — Context as a Service (1) that should be required reading for every enterprise technology leader. Then, Kong CEO Augusto Marietti (Aghi for short) wrote out his thoughts on the subject and why context is…
[](/blog/enterprise/the-platform-enterprises-need-to-compete)
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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