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.
This is part of a three-part series. For the full story, see Your AI Agent Knows What, It Doesn't Know Why and The Case for Kafka as the Agent's Memory Layer . We made the case for why agentic AI systems need a durable commit log rather than static state snapshots. The argument is…
This is part of a three-part series. For the full story, see Your AI Agent Knows What, It Doesn't Know Why and Building the Agentic Commit Log . Once you accept that agentic AI systems need a durable commit log — an ordered, immutable record of every tool call, decision, and context shift —…
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…
Customers today face significant challenges as their Kubernetes environments scale. The proliferation of microservices, external integrations, and new AI workloads increases traffic volume and connectivity complexity, creating material risks to performance and availability. The core issue is a lack…
This is part of a three-part series. For the full story, see Building the Agentic Commit Log and The Case for Kafka as the Agent's Memory Layer . There's a reason we don't find our keys by scanning every room like a security camera. We replay the tape. We remember the groceries, the front…
Your AI infrastructure is producing financial signals your CFO has never seen. Token consumption is a direct cost line item. Cache hit rate is a margin improvement. Model routing decisions are cost arbitrage events. These things are happening right now, in the gateway layer, with no route to the…
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…
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…
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…
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…
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…
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…
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,…
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…
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,…
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…