Systems | Development | Analytics | API | Testing

At their best, code generation LLMs reduce cognitive load, accelerate iteration, and serve as a great pair programmer for well-scoped tasks. That said, they also introduce a level of risk. Whether it’s using a variable that was never declared, making up functions that aren’t part of a class, using code from outdated packages, or misdiagnosing an issue, code generation models can create problems.

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.

This is Part 2 of our WEX series. In this blog, we explore how the company scaled self-service analytics by embedding AI—read Part 1 on their people-first approach. You’ve got AI pressure from every angle: execs, customers, and competitors. But legacy analytics doesn’t just slow down development—it frustrates users and undermines the value your product is supposed to deliver.

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.

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.

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.

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.

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.

Happy holidays! 2025 has been a busy and productive time here at 'Badger HQ. While we shipped a lot of cool things, four features really stand out as we look back on the year. Our top features in 2025 include: We also attended some conferences this year! MicroConf, RailsConf, Laracon, ElixirConf, Rocky Mountain Ruby, and SF Ruby: It was great connecting with so many folks in person, discussing application monitoring, and sharing some delicious meals.

Kong was born to connect. The world is shifting from connecting cloud services with apps to connecting LLMs through agents. API calls and tokens are moving in tandem; a new unit of intelligence is forming. As AI traffic explodes into hypervolumes, speed is all that matters. The same principles of performance, security, and reliability behind Kong are essential in an agentic world. A new connectivity layer for AI is born.