Systems | Development | Analytics | API | Testing

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.

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Whether it’s using a variable that was never declared, making up functions that aren’t part of a class, using code from outdated packages, or misdiagnosing an issue, code generation models can create problems.

NVIDIA just announced NVIDIA Nemotron 3, a new family of open models, datasets, and libraries designed to support long-context reasoning and multi-step AI workflows. With the ability to work across enterprise ecosystems, this family of models empowers enterprises to build and deploy reliable multi-agent systems at scale, offering an important set of technologies at a pivotal moment in AI evolution.

RAG evaluation measures how effectively a system retrieves relevant context and uses it to generate grounded answers. These evaluations detect hallucinations, measure retrieval precision and reveal where pipelines degrade after model updates or knowledge-base changes. Engineers rely on these tools to maintain output quality, prevent regressions, validate prompt and architecture choices and ensure that production answers stay aligned with trusted sources.

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.

Talk to anyone who works with data, and you’ll hear a familiar story: Data engineers are still bogged down cleaning, prepping, and untangling semantic models. Analysts are churning out dashboard after dashboard, with little time left for real analysis. Developers are hand-coding embedded analytics, turning every new feature into a months-long project. And business users are stuck in line, waiting for answers.

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.

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.

Human Resources is rich in data but often struggles to make it accessible and actionable. As a profession we also live in the ‘grey’ – of course recognizing that People are by very definition different, unique and often require individual approach.