Systems | Development | Analytics | API | Testing

AI is no longer an experimental line item in the budget. For most U.S. CXOs, the real challenge in 2026 is far more practical: where should we place our bets first? With dozens of AI use cases competing for attention, capital, and executive sponsorship, prioritization has become a boardroom conversation, not a lab discussion. Are you investing in AI initiatives that can move the needle this fiscal year, or are you spreading resources thin across pilots that never scale?

Getting APIs to work with AI has become one of the major themes in the API space recently. And that’s not surprising because APIs are at the core of an AI’s ability to reach out into the world, to get access to data and information, and to invoke commands and workflows to act. This was always what APIs were for, but in this article we will dive a little deeper what that evolution looks like, and what that means for API governance and management.

When we think of a typical API, part of a production-ready setup generally includes a cache. This cache allows for similar requests to be served without having to do the entire roundtrip. But when it comes to AI applications powered by large language models, traditional caching falls short. This is because queries to an AI endpoint may look different in terms of how things are worded or phrased but actually mean the same thing semantically.

If you're part of a GitHub shop evaluating Bitrise for your mobile app teams, you might be hearing a familiar objection: "Why add another tool? GitHub Actions is our org standard, and it will work for mobile." It's a reasonable point. Nobody wants to maintain a snowflake system that sits outside the approved tool list. But here's the thing — it doesn't have to be GitHub Actions *or* Bitrise. The reality is that mobile CI/CD has unique demands.

This guide explains how enterprises can replace cloud-hosted AI developer tools with secure, on-prem alternatives. It covers architectures, governance, and selection criteria that meet compliance and performance goals. You will learn how teams stand up private code assistants, model gateways, vector search, and policy controls behind the firewall.

Errors and exceptions are inevitable in any software, and FastAPI applications are no exception. Errors can disrupt the normal flow of execution, expose sensitive information, and lead to a poor user experience. Hence, it is important to implement robust error-handling mechanisms in FastAPI applications. In this article, we will discuss the different types of FastAPI errors to help you understand their causes and effects.

For most businesses, the break-even point for self-hosting only makes sense if processing 100–200 million tokens daily. Otherwise, managed API solutions are more cost-effective, faster to deploy, and easier to maintain. Alternatives like DreamFactory offer pre-built, secure API layers, saving time and money while simplifying enterprise AI integration. Bottom line: Building your own LLM data layer is a major investment with hidden challenges.

Kong Operator 2.1 is a significant update for our customers, opening up integration into the Kong ecosystem for our Kubernetes customers. With Kong Ingress Controller, when your Control Plane was hosted in Kong Konnect, and you were using Kubernetes Gateway API, your dataplane, routes, and services were in read-only mode.

The promise of agentic AI is clear: autonomous systems that can reason, plan, and act on your behalf. But there's a fundamental problem standing between that vision and enterprise reality: agents need context to make decisions, and that context lives scattered across your organization. Context is any data — or any abstraction that enables access to data — that an agent needs to do its job. Customer records in your CRM. Inventory levels behind your fulfillment APIs.