**Translation is as follows:**

**Bumi, from building with natural language to deployment... Can the “results” of enterprise AI investments become a watershed?**

Although the enterprise AI investment boom has lasted for three years, many on-site still respond with “no visible results.” Against this backdrop, Boomi has launched “Boomi Companion,” a tool that can build, deploy, and test enterprise-level software solely through natural language commands, with some opinions suggesting this may signal a comprehensive realization of AI’s practical value.

At “Boomi World 2026” held in the United States, Boomi announced the release of the intelligent agent skill set Boomi Companion, based on open-source technology. This tool runs on Anthropic’s open standards and integrates with AI coding agents such as Claude Code, OpenAI Codex, and Microsoft ($MSFT) Copilot, aiming to enable Boomi’s full suite of solutions through natural language alone. Its core is not just generating code drafts but executing actual deployment, testing, troubleshooting, and iterative optimization.

Boomi’s Chief Technology Officer Matt McLarty views this trend as a watershed from “AI overheating” to the “practical application stage.” He pointed out that many enterprises, after the birth of ChatGPT, acknowledged the inevitability of AI innovation but failed to see corresponding value in actual business operations. Meanwhile, he emphasized that now is the time to demonstrate answers to the ongoing questions from clients and partners about “when will we see results.”

Intelligent Agent Modeling and Platform Transformation

This release has attracted much attention because “intelligent agent modeling” aims to bridge the gap between existing low-code tools and traditional platform setup methods. Previously, enterprise developers often had to choose between rapid development speed and fine-grained control. In contrast, Boomi Companion’s differentiation lies in automating complex configuration work via natural language interfaces and elevating the results to a level ready for direct deployment in operational environments.

McLarty distinguished this from the industry’s recent buzzword “Vibe Coding.” He explained that if Vibe Coding relies on a feeling that quickly generates results but produces fragile and poorly maintainable structures, then Boomi Companion incorporates Boomi platform expertise in the form of “digital twins,” embedding best practices within the intelligent agent’s skills. In other words, its output is not just customized code snippets but more structured, scalable “production-ready” solutions.

The role of graphical interfaces has not disappeared. Even if AI automatically generates most content, developers can still verify “what was created and how” through visual screens and make adjustments directly when necessary. This “two-way” approach, combining natural language-driven productivity with platform control, demonstrates a potential upgrade in enterprise software development methods.

Transformation of Software Architecture

McLarty pointed out that a greater change lies in the transformation of software architecture itself. He explained that the future’s core challenge is not replacing traditional “deterministic processing” with AI’s “probabilistic reasoning,” but designing when and how to combine both approaches. To deeply embed AI into real-time business environments, new design principles that balance predictability and flexibility are required.

This could represent a leap beyond the evolution from object-oriented, service-oriented, API-first, to microservices architectures. Especially as enterprise AI moves beyond simple chatbots or dashboard assistive functions into driving actual business processes, such structural changes are considered essential.

Ultimately, the emergence of Boomi Companion indicates that the focus of AI competition is shifting from “what can be demonstrated” to “what can be operated in practice.” The long-anticipated ROI of AI investments seems more likely to be realized first in tools that can create operational software via natural language, rather than through smarter interactive interfaces.

TP AI Notice: The article has been summarized using a language model based on TokenPost.ai. The main content may be incomplete or inconsistent with the facts.