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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. This infrastructure gap leaves AI applications vulnerable to sophisticated threats like prompt injection and data exfiltration, necessitating a unified stack that secures the edge while streamlining the data plane..

Most engineering teams adopt Apache Kafka for one simple reason: it works. It scales effortlessly, it is incredibly reliable, and it powers real-time systems across almost every industry. But as your Kafka usage expands across different teams, regions, and external consumers, success creates a brand new problem. Kafka is a massive data firehose, and without the right nozzle, it quickly becomes unmanageable.

Modern Kafka deployments struggle with a familiar tension. You want fine-grained access control per client, per team, and even per request. However, traditional ACLs force you into static, cluster-level configurations that are brittle, hard to scale, and painful to maintain. Administrators are often forced to manage massive, hardcoded lists of topics and users. But what if you could dynamically craft these ACLs using identity context?

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 server is "up," they fail to tell you if your AI investment is being leveraged.

Insomnia natively understands Postman's export format and can bring over your collections, environments, folder structures, and even pre-request scripts with most of the conversion handled automatically..

Digital agreements are at the heart of many critical business processes. As companies modernize their technology stacks and adopt API-driven architectures, integrating agreement workflows directly into applications has become increasingly important. Traditional agreement processes were slow and heavily manual. Documents were often created in office tools, shared through email, printed, signed physically, and stored across multiple systems.

How Kong Gateway 3.14 closes the consistency gap in IAM-based authentication across AWS, Azure and GCP — and what it means for your production deployments Enterprise security teams have clear requirements: no static credentials, no exceptions. Every service-to-service connection, whether it's Kong talking to databases, caches, or vaults, should authenticate using the same IAM-based identity model that governs the rest of their cloud infrastructure.

Modern API architectures are no longer linear. A single request can traverse multiple layers of authentication, transformation, enrichment, and observability. As these flows grow more dynamic, the need for fine-grained control over when plugins execute becomes critical. For years, the standard approach to API Gateway configuration followed a strict hierarchical model: you applied a plugin to a Service, a Route, or a Consumer.

How OAuth 2.0 Token Exchange Reshapes Trust Between Services — and Why the API Gateway Is Exactly the Right Place to Enforce It OAuth 2.0 Token Exchange fundamentally alters how services establish trust. And the API gateway is the ideal location to enforce this new model. Modern applications don’t run as a single monolithic.

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 AI workflows that span multiple providers, services, and agents. Every hop in that data path is an opportunity for something to go wrong. The challenge is governance.