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BoxLang 1.17.0 Released: Module Inception, CLI Checker, Jar Loading and much more!

Thu, 17 Sep 2026 20:20:32 +0000

BoxLang has always been an extensible language. As of 1.17.0, it is a hierarchically extensible one.

Modules can now contain other modules; aka Module Inception . Recursively. To any depth. Each nested module gets its own class loader, chained to its parent’s, and BoxLang’s own ModuleService discovers, registers, activates, and unloads that entire tree itself. No package manager. No build tool. No install ordering. Drop in one artifact and everything it depends on comes with it, already wired up and already isolated. Not only that, you can now package modules into a single jar.

Debugging Is Invariant Discovery: What One Kafka Session Taught Us About AI Agents

Tue, 08 Sep 2026 14:14:36 +0000

Every Java team has a sentence like this one somewhere:

Any operation on a TID must go through the single asynchronous path and must not let TMS and EMV drift apart.

It is the kind of rule that is obvious once said and invisible until then. A terminal ID (TID) in a payments system, an asynchronous pipeline that updates two downstream systems, and the requirement that the two never disagree. Nothing in the type system enforces a rule like that, and no single class owns it. Rules of this kind live between consumers, services and stores.

From Spec-Driven Development to Living Specifications in Java Projects

Fri, 04 Sep 2026 05:51:45 +0000

From Spec-Driven Development to Living Specifications in Java Projects

Coding agents have changed the speed at which we can produce software. But producing code faster does not automatically mean producing better software.

As I started using agents more frequently in real-world software development, one question became increasingly important: how do we give an agent enough freedom to be useful without losing engineering intent?

Trying to control every line of code generated by an agent removes much of the value it can provide. At that point, it might simply be faster to write the code ourselves.

Did Your AI Agent Ever Run a Debugger? One JVM Bug, Two Agent Runs

Thu, 03 Sep 2026 13:48:32 +0000

Java developers reach for the debugger without thinking about it. Set a breakpoint, run the failing test, look at the variables, then decide what to change.

Most AI coding agents I have watched skip that step. They read the stack trace, read the source, and propose a patch. The reasoning can look excellent and still be a guess about a program nobody ran.

I wanted to know what that guess costs. So I took one real JVM bug, one model, one prompt, and ran it twice with two debugging workflows. Here is what the two transcripts show.

Introducing BoxLang AI Explorer: A Local Catalog for Every AI Pattern

Tue, 18 Aug 2026 14:15:48 +0000

Learning a new AI API usually means jumping between scattered documentation pages, guessing at imports, and copy-pasting code that may or may not still work. We wanted something better for BoxLang AI, so we built the BoxLang AI Explorer : a local, browser-based catalog of runnable BoxLang AI examples, organized by category and difficulty, each with guidance, source code, and sample output. You can also try it online at https://ai.boxlang.io/explorer/

Build Secure AI Chat Applications with BoxLang, RAG, Ollama, and Amazon Bedrock with Dan Card

Thu, 13 Aug 2026 14:52:13 +0000

Build Secure AI Chat Applications with BoxLang, RAG, Ollama, and Amazon Bedrock

AI demos are everywhere. Building an AI-powered feature that can securely work with your documents, databases, and real application data is a very different challenge.

How do you give an AI model access to the right information without exposing sensitive data? Should you use a locally hosted model or a cloud service? How do you move beyond a basic chatbot and build something genuinely useful for your users or organization?

Why Spring Teams Don't Need a Second Runtime for AI Agents

Wed, 10 Jun 2026 19:02:58 +0000

A JVM-native runtime for building, governing and operating AI agents on existing Spring infrastructure.

Every time a Spring team decides to add AI agents to a production system, the story tends to follow the same arc. A few prototypes are built, several frameworks are evaluated, and then a new reality emerges: the project is no longer just about adding agents. It is about operating a new platform.

With that new runtime come new deployment pipelines, new monitoring mechanisms, additional security reviews, and new operational responsibilities.

Context Is Code: A Tour of APM and AgentRC

Fri, 05 Jun 2026 08:35:05 +0000

Context Is Code: A Tour of APM and AgentRC

If you’ve shipped an AI agent into a real codebase in the last twelve months, you’ve felt this: every agent, every developer, every machine — different setup. A README that says “install these extensions.” A copilot-instructions.md somebody copy-pasted from another repo. MCP server configs in three different files. The same skills duplicated for Copilot, Claude, Cursor, and Codex.

No version pinning.

Tiberius: A Security Testing Framework for LLM Applications in Java

Thu, 04 Jun 2026 20:09:09 +0000

Tiberius: A Security Testing Framework for LLM Applications in Java

How do you write a regression test for a system that is non-deterministic by design?

1. The Problem

Large Language Models have moved from research artifacts to production infrastructure. Java applications are embedding them into customer-facing services via Spring Boot, and e.g. LangChain4J — for document summarization, customer support, healthcare assistance, and financial guidance, to name just a few. The deployment surface is growing faster than the security tooling.

BoxLang AI 3.2.0 — Image Generation, Web Search, Fluent Audio, Agent Registry & MCP Observability

Tue, 02 Jun 2026 12:27:07 +0000

BoxLang AI 3.2.0 is here, and it’s a landmark release. We’re shipping five major features: image generation, web search, a fluent audio builder API, a centralized agent registry, and deep MCP observability along with a suite of analytics improvements and a critical bug fix. Let’s dig in. 🎉

🖼️ Image Generation — aiImage()

You can now generate images directly from BoxLang using any provider that supports text-to-image generation. The aiImage() BIF follows the same fluent, chainable philosophy as the rest of bx-ai then act on the result with expressive method calls.

Free Webinar: Making AI useful for Java developers in Real Applications with BoxLang!

Fri, 29 May 2026 15:43:47 +0000

AI is everywhere right now, but for many development teams, the biggest question is no longer “What is AI?” it’s “How do we actually use it in real applications in a secure, practical, and maintainable way?”

That’s exactly what we’ll explore in our upcoming free June webinar:

Making AI Useful in Real Applications

A Practical Guide to Secure and Effective AI Development

Join Bill Reese, Senior Developer at Ortus Solutions , for a practical session focused on bringing AI into real-world applications using BoxLang and modern JVM development patterns.

Introducing skills.boxlang.io — The Open Agent Skills Ecosystem for BoxLang & the Ortus World

Thu, 21 May 2026 11:42:26 +0000

Today we’re launching something we’ve been quietly building for months: skills.boxlang.io — a public, agent-agnostic directory for AI skills covering BoxLang, ColdBox, TestBox, CommandBox, and the entire Ortus ecosystem.

If you’ve ever pasted a 400-line system prompt into yet another AI agent, watched two of your bots drift onto subtly different versions of the same coding standard, or spent half a Friday afternoon trying to convince an LLM that BoxLang is not Java and is not CFML, or how to code for Modern CFML; this launch is for you. 🎯

BoxLang AI Series: Complete Guide to Building AI Agents

Thu, 14 May 2026 09:26:48 +0000

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 to understand what’s possible before diving into the technical details, start here:

How to Develop AI Agents Using BoxLang AI: A Practical Guide

Tue, 12 May 2026 12:52:39 +0000

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.

In this tutorial, I’ll show you how to build a real-world AI agent using BoxLang AI — the official AI framework for the BoxLang JVM language. We’ll build SupportBot, an e-commerce customer support agent that can look up orders, check inventory, issue refunds, and answer questions grounded in your knowledge base.

BoxLang AI Deep Dive — Part 7 of 7: MCP — The Protocol That Connects Everything

Thu, 07 May 2026 21:51:08 +0000

BoxLang AI 3.0 Series · Part 7 of 7

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.

BoxLang AI Deep Dive — Part 6 of 7: Memory Systems & RAG — Building AI That Remembers

Tue, 05 May 2026 15:10:15 +0000

BoxLang AI 3.0 Series · Part 6 of 7

A chatbot with no memory isn’t a conversation — it’s a series of isolated queries. Every message starts from scratch. The user has to re-explain who they are, what they’re working on, and what was just said. It’s exhausting, and it signals that the AI isn’t really listening.

Memory is what separates a useful AI application from a toy. BoxLang AI ships with one of the most comprehensive memory systems in any AI framework — 20+ memory types across two major categories, vector embedding support for semantic retrieval, 30+ document loaders for RAG pipelines, and a per-call identity routing system that makes multi-tenant applications safe by default.

BoxLang AI Deep Dive — Part 5 of 7: One API, 17 Providers — The Provider Architecture Deep Dive

Wed, 29 Apr 2026 16:38:08 +0000

BoxLang AI 3.0 Series · Part 5 of 7

Vendor lock-in is the silent killer of AI projects. You pick OpenAI, build everything against the OpenAI API, and then GPT-5 launches at three times the price. Or a competitor launches a model that’s faster for your use case. Or you need to self-host for compliance. Or your client is on AWS and wants Bedrock.

Every time the answer to “can we switch providers?” is “it would take months,” something went wrong architecturally.

Explore Spring AI SDK – Amazon Bedrock AgentCore – Part 2

Mon, 27 Apr 2026 09:09:00 +0000

If you’re joining us from Part 1 or need a quick refresher on the architecture, listen to this brief overview of how Spring AI and Amazon Bedrock work together. Generated using Notebook LLM for my previous article

In this article, we explore one of the AgentCore capabilities i.e., memory Source: Amazon

To begin, enable AgentCore memory for the agent you built earlier.

Step 1: Add the Ai model and AgentCore memory dependencies

<dependency>
 <groupId>org.springframework.ai</groupId>
 <artifactId>spring-ai-model</artifactId>
</dependency>
<dependency>
 <groupId>org.springaicommunity</groupId>
 <artifactId>spring-ai-agentcore-memory</artifactId>
</dependency>

Step 2: Create Short/Long Term in AWS Management Console

Navigate to Amazon Bedrock AgentCore > Memory to create short/long-term memories. AgentCore Memory

BoxLang AI Deep Dive — Part 4 of 7: Middleware — The Missing Layer in Every AI Framework

Thu, 23 Apr 2026 14:25:53 +0000

BoxLang AI 3.0 Series · Part 4 of 7

Here’s the question every team eventually asks about their AI agents: how do we test these things?

Agents make live LLM calls. They invoke real tools. They have non-deterministic outputs. Standard unit testing approaches fall apart. You can’t mock every provider. You can’t replay a conversation from three weeks ago. You can’t confidently tell stakeholders that the agent you deployed today behaves the same way it did when you signed off on it.

BoxLang AI Deep Dive — Part 3 of 7: Multi-Agent Orchestration — Building AI Teams That Work

Tue, 21 Apr 2026 08:32:06 +0000

BoxLang AI 3.0 Series · Part 3 of 7

A single agent is useful. An orchestra of agents is powerful.

The problem with most multi-agent frameworks is that the orchestration layer is bolted on — you’re managing agent references manually, passing outputs between them by hand, and hoping you haven’t introduced a cycle. There’s no concept of hierarchy. No cycle detection. No way to ask “who’s in charge here?” or “how deep in the tree am I?”

Explore Spring AI SDK - Amazon Bedrock AgentCore - Part 1

Mon, 20 Apr 2026 13:44:54 +0000

Introduction

Artificial intelligence has rapidly expanded across every industry since the inception of ChatGPT. It represents a breakthrough in how we build and use software. Moreover, this breakthrough technology has driven major transformation. At the same time, it has created significant noise and hype.

Today, AI is no longer experimental. Instead, it has become essential, much like electricity and the internet. As a result, it now plays a key role in our daily lives. Overall, this shift reflects strong technological acceleration across industries.

BoxLang AI Deep Dive — Part 2 of 7: Building a Production-Grade AI Tool Ecosystem

Thu, 16 Apr 2026 09:27:15 +0000

BoxLang AI 3.0 Series · Part 2 of 7

Function calling is where most AI frameworks look deceptively simple on the surface and turn into a mess underneath. You define a tool, pass it to the LLM, and when the LLM calls it — who handles the lifecycle? Who fires observability events? Who serializes the result? Who resolves the tool by name when the only thing you have is a string?

BoxLang AI Deep Dive — Part 1 of 7: The Skills Revolution

Tue, 14 Apr 2026 11:50:53 +0000

This article is part of our 7-part deep dive on building production-ready AI systems with BoxLang.

BoxLang AI 3.0 Series · Part 1 of 7

Every AI framework eventually hits the same wall: your system prompts start drifting. Agent A has a slightly different version of the SQL rules than Agent B. The tone policy on your support bot is three weeks behind the tone policy on your documentation bot. Someone copy-pasted the wrong version. Nobody noticed.

BoxLang AI v3 Has Landed - Multi-Agent Orchestration, Tooling, Skills and so much more

Mon, 06 Apr 2026 12:03:58 +0000

It’s been a while since we’ve shipped something this big. BoxLang AI 3.0 is a ground-up rethink of how AI agents, models, and tools work in the BoxLang ecosystem — and it lands with ten major features at once.

The headline is the AI Skills system : a first-class implementation of Anthropic’s Agent Skills open standard that lets you define reusable knowledge blocks: coding styles, domain rules, tone policies, API guidelines once in a SKILL.md file and inject them into any number of agents and models at runtime. No more copy-pasting the same system-prompt boilerplate everywhere. Skills are versioned, composable, and come in two modes: always-on (full content in every call) and lazy (only a name + description until the LLM asks for more).

From “Crypto AI” to general AI: Do AI agents dream of electric langoustines?

Mon, 23 Feb 2026 18:11:54 +0000

x402, ERC-8004, A2A, and The Next Wave of AI Commerce: Do AI Agents Dream of Electric Langoustines?

A Blade Runner riff for a world where the lobster ships paid endpoints while humans still argue about the roadmap.

The shift that matters for agent commerce - From “Crypto AI” to general AI

Today, you can search the web all day and never see an invoice.

That happens because you are not the paying client.

DevBcn, the developer's conference in Barcelona

Tue, 17 Feb 2026 09:56:33 +0000

Hola developers !!!!!

The 11th edition of DevBcn, the developer’s conference in Barcelona is coming next June. The perfect place to have great technology talks 🤖, perfect weather 🏖️, amazing food 🥘, and an unforgettable experience 💖.

It will be hosted on the 16th and 17th of June 2026, in the World Trade Center Conference Center , having 4 rooms with talks in parallel during the 2 days of the conference : Java/JVM, AI, Cloud, Frontend, Managing and Soft skills.

Windsurf AI and SonarQube The Dream Team for AI Code Quality

Mon, 16 Feb 2026 09:34:00 +0000

Hola! 👋 As a Java developer, you probably spend a good chunk of your day making sure your code doesn’t just “work,” but is also maintainable, secure, and reliable. I’ve been using a tool that is changing how I handle large projects: Windsurf.

It is an agentic IDE that understands what you are trying to do across your whole project. Let’s see why this is a game-changer for our Java workflows. 🚀

Join Our Webinar: Build Agents, RAG Pipelines and More

Tue, 10 Feb 2026 12:18:17 +0000

Join Our Webinar: Build Agents, RAG Pipelines & Multi-Model Workflows with BoxLang AI

Unlock Enterprise AI on the JVM with BoxLang AI

Build Agents, RAG Pipelines & Multi-Model Workflows — One API, Zero Lock-In

Modern enterprises want to move fast with AI—but juggling different providers, SDKs, formats, and deployment models quickly becomes complex and brittle.

That’s why we’re excited to announce our upcoming webinar:

Unlock Enterprise AI on the JVM: Build Agents, RAG Pipelines & Multi-Model Workflows with BoxLang AI One API, Zero Lock-In Led by Luis Majano

JC-AI Newsletter #13

Thu, 05 Feb 2026 21:12:12 +0000

Two weeks have passed, and it is time to present a new collection of readings that may shape developments, utilization or ideas in the field of artificial intelligence in 2026.

While significant activity characterizes the AI field, many unresolved research, design, and implementation challenges continue to impact progress. Future advancement depends heavily on understanding the nature of these challenges to approach probabilistic problems from the appropriate directions. This JC-AI newsletter features insightful interviews with key figures in the field, enabling readers to ask the right questions and compare visions of an ‘uncertain future’ against current capabilities to maintain a grounded perspective.

Claude Code SonarQube MCP: A Java Developer’s Workflow

Mon, 02 Feb 2026 15:15:17 +0000

Hola Java developers! 👋

We all know the feeling. You are “in the zone,” coding away in your terminal, feeling like a hacker from a 90s movie. But then, reality hits. You need to check a rule, review a vulnerability, or verify if your project passes the quality gate.

What do you do? You Alt+Tab. You open the browser. You log in. You search. And just like that… the flow is gone. 📉