Blog

We are thrilled to announce ColdBox 8.1.0, a targeted minor release packed with powerful new features, important improvements, and critical bug fixes across ColdBox, WireBox, and CacheBox. While minor in version number, this release delivers some truly exciting capabilities — especially for BoxLang developers building AI-powered applications.

Across the United Kingdom, many universities still rely on legacy ColdFusion and CFML systems to power student portals, enrollment platforms, research databases, payment gateways, and internal academic workflows.

These systems are often:

• 15 to 25 years old
• Mission-critical
• Deeply integrated with student information systems
• Running on older Adobe ColdFusion or Lucee versions
• Tightly coupled monolithi...

The world of AI development is moving fast, but building real, production-ready AI agents doesn’t have to be complex.

This series walks you step by step through how to design, build, and deploy AI agents using BoxLang AI. Whether you’re exploring AI for the first time or looking to modernize your current applications, these guides will help you move from concept to implementation with clarity.

Start Here: A Practical Overview

If you’re new to BoxLang AI or want t...

We just shipped the BoxLang Google Cloud Functions Runtime — and it brings the same write-once-run-anywhere serverless experience you already know from our AWS Lambda runtime, now running natively on Google Cloud Functions Gen2.

We believe the best way to learn a programming language is by writing code — real code, with real feedback, and real tests. That's exactly why we built BoxLings.

BoxLang 1.12.0 marks a meaningful turning point. After establishing a rock-solid foundation across runtime, compiler, CFML compatibility, and the module ecosystem, BoxLang has entered its innovation cycle. The language is mature, battle-tested, and production-deployed across the industry.

Being a CTO can be surprisingly isolated.

Not because of the title, but because of the decisions.

Every day, you’re expected to make calls that impact system stability, performance, security, team productivity, and long term costs. And often, you’re making those decisions with incomplete information, limited resources, and no real sounding board.

That’s where the real challenge begins.

It’s Not a Talent Problem, It’s a Context Problem

Most CTOs don’...

AI agents are transforming how we build software. Unlike traditional chatbots that just answer questions, agents can reason about what tools they need, decide when to use them, chain multiple actions together, and remember what happened earlier in a conversation.

The AI ecosystem has a tool problem. Every framework has its own way of defining tools, every agent has its own way of calling them, and every integration requires custom code on both sides. An agent built in Python can't easily use tools built in Java. An MCP server written for Claude Desktop can't easily be consumed by a BoxLang agent without a custom adapter.