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The problem is not that your team lacks priorities.It is that maintenance, delivery, and long-term improvement are competing for the same people.

For companies running business-critical CFML, ColdFusion, or BoxLang applications, that pressure can be especially difficult to solve. Experienced developers are not always easy to hire quickly, application knowledge may be concentrated in one or two people, and a generalist may need significant context before taking ownership of the work. A permanent hire can also require a significant investment in recruiting, compensation, onboarding, and long-term capacity even when the immediate need is a specific project, urgent maintenance, or a period of transition.

Reasoning-capable models have been usable in BoxLang AI for a while. params passes straight through to the provider body, so params: { thinking: { type: "enabled", budget_tokens: 10000 } } for Claude, or params: { reasoning_effort: "high" } for OpenAI, already reached the API. What never worked was reading the reasoning back. It was parsed out on arrival and silently dropped. In 3.4.0, that's fixed, and it's fixed the same way for every provider.

TestBox 7.1.0 is here, and it is the largest single expansion of our assertion and expectation library since TestBox was born.

You call writeDump() on an ORM entity or a very rich class graph . The browser locks up. Thirty seconds later you get a page with forty thousand rows on it, you scroll for a while, you give up, and you go edit your code to dump a sub-key instead. Or worse, you crash the server.

LLM applications face a class of attack traditional input validation was never built for: prompt injection. An attacker embeds instructions in user input, a retrieved document, a web page your tool fetched, or an MCP result, trying to override your system prompt, exfiltrate data, or hijack a tool call. BoxLang AI 3.4.0 ships four layered, configurable defenses against exactly this, plus one more that's on unconditionally.

Everyone knows the datasource password should not be sitting in plain text in a config file. Everyone has also, at some point, shipped exactly that, because the alternative was a pile of environment variable plumbing that nobody wanted to build on a deadline. x

Here's a bug that's easy to miss until it bites someone in production: an agent turn asks for two tool calls at once, both need human approval, and only the first one actually suspends. The second one gets silently skipped. Not rejected, not queued, just gone. That's what happened before 3.4.0, and it's fixed now with batched tool-call approvals.

If you have an existing ColdFusion or Lucee application, one of the first questions you may have about BoxLang is probably not:

“Should I rewrite my application in BoxLang?”

It is much simpler:

“Can BoxLang run the CFML application I already have?”

That was the focus of veteran CFML troubleshooter Charlie Arehart’s session at Into the Box 2026, Getting Started with BoxLang as an Alternative CFML Engine.

HumanInTheLoopMiddleware used to do everything itself: decide which tool calls needed approval, present the request, and wait for a decision. In 3.4.0, that logic has been pulled apart into a real subsystem, and the piece developers will feel the most is that a human's "always allow this" now actually means always.