Systems | Development | Analytics | API | Testing

Every technology cycle comes with hype, backlash, and eventually… utility. AI is shaping up to be no different. As we head into 2026, the conversation is already shifting from “AI will replace everything” to “why isn’t this paying off yet?” This shift is heavily influenced by evolving market trends, as businesses and technologists respond to changes in customer behavior, operational patterns, and broader market conditions that shape expectations around AI.

API protocols evolve every few years. We have moved from SOAP to REST, then to GraphQL, gRPC, and AsyncAPI for event-driven systems. Now with the rise of large language models (LLMs) and AI agents, organizations need a new class of interfaces that allow agents to take action across real systems, not just generate text. LLMs are powerful reasoning engines, but they lack context. They cannot perform actions by themselves, see real-time data, private information, or internal systems.

You may be surprised to learn that more than 70% of software failures due to unaddressed functional issues that could have been caught during testing. Think about it: you release a new app or system, a user clicks their way through a common user flow…and it fails. In our competitive digital economy, we assume performance and intuitiveness—one hiccup, and users will stop using you as a provider, and possibly undermine your credibility.

Automate the boilerplate so you can focus on what actually matters. Developers spend somewhere between 30-50% of their time on repetitive tasks that add little value to the final product. In API development, this overhead is particularly painful: writing nearly identical CRUD endpoints over and over, manually updating documentation that immediately drifts out of sync, copying data between environments, and handling routine maintenance that should happen automatically.

Engineering teams want to understand the real health of their applications – not just what was planned or what was tested, but what is actually happening in production. The challenge is that these signals live in different systems, each optimized for a specific part of the delivery lifecycle. Test execution data, issue tracking, and production monitoring each describe a different aspect of system behavior. On their own, they answer narrow questions about validation, delivery, or stability.

In an age where software developers are speeding up their code development to meet the demand of rapid application deployment, there are new tools being developed based on Artificial Intelligence (AI) technology. Replit and Cursor have received a lot of excitement for both of these platforms due to their use of artificial intelligence in assisting developers with coding.

Kong AI Gateway 3.13 moves enterprises from AI experimentation to shipping production-grade agents by unlocking new capabilities focused on agentic security, developer productivity, and resilience, including MCP tool-level access control, expanded provider support, and smarter load balancing.

Happy holidays from the Kong team! Consider this our gift to everyone who's been asked "what's the ROI on AI?" one too many times. The AI boom has a dirty secret: for most enterprises, it's bleeding money. Every LLM call, every agent invocation, every API request that powers your AI products — they all cost something. And right now, most organizations have no idea what they're spending, who's spending it, or how to control that spend. Finance teams are flying blind.

Discover expanded OpenTelemetry support, full cloud-native authentication, powerful Datakit orchestrations, and PCI DSS 4.0 attestation for Kong Konnect and Cloud Gateways. As API ecosystems grow more complex, maintaining visibility and security shouldn't be a hurdle. Kong Gateway 3.13 simplifies these challenges with expanded OpenTelemetry support and more flexible orchestration. These new capabilities not only make your APIs more observable but also make it easier to implement orchestration.