Systems | Development | Analytics | API | Testing

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 everything changed. In 2025, the security reality for AI-integrated microservices is stark.

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, and self-hosted models. Each pattern looks API-like on the surface. Yet many organizations handle them with separate tools. The result?

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 traditional APIs your teams have been running for years; they introduce a whole new set of hurdles around operations, security, and governance that most organizations are still struggling to understand.

For enterprise teams evaluating API testing tools like Insomnia and Postman, understanding the real cost is critical. Postman’s recent changes and Insomnia’s alternative approach tell different stories. Let’s dive in.

Bringing APIs and events together has always been a challenge. REST APIs give developers a familiar interface, while event brokers like Solace Broker excel at fan-out, filtering, and scalable, reliable event delivery. The tricky part? Bridging these two worlds without building a lot of custom glue. That’s exactly what the new Kong plugin for Solace upstream mediation does.

Enterprise product teams are squarely in the throes of a massive shift. New Gartner research crystalized that companies using AI tools for product management are reaping a significant 35% productivity gain and achieving a two to three-year time-to-market advantage over those who wait.

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.

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 share the GA of Managed Redis cache for Kong Dedicated Cloud Gateways (DCGW).

A persistent challenge arises as businesses adopt multicloud architectures and agentic AI: the need for state synchronization. API and AI gateways require a robust persistence layer to synchronize data, whether it's for governing AI token usage, facilitating agent-to-agent communication, or boosting performance through caching.

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 rapidly evolving. MCP servers expose tools and capabilities that AI agents can use. However, once organizations begin deploying multiple MCP servers across environments, the question becomes clear.