Systems | Development | Analytics | API | Testing

Kong Agent Gateway Is Here — And It Completes the AI Data Path You had a request going to a model, a response coming back, and a gateway in between to enforce policy. With the right solutions, this becomes manageable pretty quickly.. That world is over. Today's agentic architectures look nothing like that. Agents are delegating tasks to other agents via A2A. These other agents are producing and consuming event streams.

You whipped up a simple MCP server prototype over the weekend. It routed a single AI agent to a few internal tools, your demo impressed leadership, and the team asked the dreaded question: "When can we ship?" You smiled and said, "Give me two weeks." Fast-forward three months**.** You’re firefighting expired tokens at 2 AM. The compliance team is camped in your inbox. Your once-elegant codebase is now a distributed systems nightmare. Sound familiar?

CLIs, MCP, and the Real Governance Tradeoffs Shaping Enterprise AI Agents.

The global financial services industry is undergoing a massive, API-driven revolution. With the global open banking market valued at $31.61 billion in 2024 and projected to grow to $135.17 billion by 2030, this shift is accelerating worldwide. This definitive guide explores the core APIs, the evolving global regulations (including FAPI 2.0, PSD3, and Section 1033), and the massive opportunities shaping the future of finance for banks, fintechs, and enterprises.

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 got the bill. Engineering got the blame. No one had the data to make good decisions. It took years of hard-won FinOps discipline to fix that. LLM spend is on the same trajectory *and moving faster*.

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.

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.