Systems | Development | Analytics | API | Testing

In the United States, the evolution of shopping malls is no longer just about retail, it has also become about experience, engagement, and intelligence. With more than 900 active shopping malls nationwide attracting millions of visitors annually, traditional brick-and-mortar destinations are battling shifting consumer preferences and rising digital expectations. Today’s consumers are blending browsing with dining, entertainment, socializing, and convenience-driven digital interactions.

As we close out 2025, it's time to reflect on the hard lessons learned from deploying AI agents in production environments. The promise of AI agents is compelling: autonomous systems that can handle complex tasks, make intelligent decisions, and execute actions on our behalf. But as several high-profile incidents this year have starkly demonstrated, this autonomy comes with unprecedented risks when proper identity and access management controls are absent.

Organizations across industries are under immense pressure to modernize operations, manage evolving regulations, and deliver seamless customer experiences. To meet these demands, leaders often turn to process orchestration. Yet, despite heavy investment, many are left with expensive automation projects that never scale and "orchestration" programs that turn into technical debt. Why does this happen? The problem isn't usually the technology itself but how it is applied.

Across the globe, 1.5 billion people remain unbanked, without access to even the most basic financial services. For the rest, fewer than 50% of the banked population qualify for formal credit, limiting both financial inclusion and lending growth. In an era where traditional credit models struggle to assess evolving financial behaviors, AI credit scoring is emerging as a strategic differentiator for banks and fintechs alike.

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.

Let me introduce you to Jake. He’s four years old, loves puzzles, and sees the world in his own unique, beautiful way. Like one in 31 children in the US, Jake has autism. His parents are told that early intervention is crucial for him to develop the skills and behaviors he’ll need to thrive. He just needs a little help with things many of us take for granted: pointing to make a request, speaking up, asking for things, making friends.

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.

As businesses continue to amass vast amounts of data, the need for robust data governance procedures has become more critical than ever. Examining data governance procedures has long been a crucial practice for businesses that collect data because it ensures that collected data is managed, stored, and utilized in a secure, compliant, and efficient manner. It also enhances data quality, risk mitigation, and better decision-making.

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.