BCAIHUB — Business Central & AI Consulting

Every Business Central development team eventually faces the same challenge. As solutions grow, repositories become larger, customizations multiply, and release cycles become shorter. Developers are under constant pressure to deliver new functionality faster, while architects are expected to ensure code quality, maintain standards, review pull requests, and keep technical debt under control.

In theory, every pull request should receive a thorough architectural review. In reality, that’s rarely possible.There are simply not enough senior architects to review every change. As a result, they often become the bottleneck in the development process. Every critical pull request lands on their desk, requiring them to assess performance implications, validate development standards, evaluate architectural decisions, verify maintainability, and ensure the solution remains scalable and upgrade-ready. As development velocity increases, maintaining this level of oversight becomes increasingly difficult.

That led me to ask a simple but powerful question:

What if every Business Central pull request had access to an AI Solution Architect?

Not an AI coding assistant that generates code. Not a chatbot that answers questions, but an AI agent capable of reviewing AL code, analyzing repository structure, identifying risks, highlighting best-practice violations, and providing recommendations similar to those of an experienced Business Central Solution Architect. An AI reviewer that never gets busy, never skips a review, and can provide consistent architectural guidance across every pull request, every repository, and every project. That idea became the foundation for an experiment that could fundamentally change how Business Central development teams approach code reviews and architectural governance.

The Hidden Cost of Growing Repositories

Most Business Central projects do not struggle because developers write bad code. In fact, the majority of technical debt is created through small decisions that seem perfectly reasonable at the time.

A public procedure whose purpose is obvious today but may be unclear to the next developer six months from now. None of these issues are likely to break the application. Most will pass testing, deploy successfully, and function exactly as intended that is precisely why they are dangerous.

Individually, these decisions appear insignificant. But as a solution evolves and hundreds of similar decisions accumulate across multiple releases, they gradually transform into technical debt. Over time, this debt begins to impact performance, maintainability, upgrade readiness, developer productivity, and ultimately the long-term cost of supporting the solution. This is why code reviews remain such a critical part of the Business Central development lifecycle.

A good reviewer does far more than verify whether the code works. They assess whether it follows best practices, aligns with architectural standards, remains maintainable, and will continue to support the business as the solution grows. The challenge, however, is that effective code reviews depend on experienced reviewers and experienced reviewers are often the busiest people in the organization.

Senior developers, technical architects, and solution architects are expected to balance project delivery, customer meetings, design decisions, mentoring, troubleshooting, and governance responsibilities. As repositories grow and development velocity increases, expecting them to thoroughly review every pull request becomes increasingly unrealistic. As a result, many quality issues slip through—not because teams lack standards, but because there simply isn’t enough time to apply those standards consistently across every change. This is exactly where AI has the potential to make a meaningful difference.

Building an AI Solution Architect

As I explored this challenge further, I began asking a fundamental question:

Could AI perform the first level of architectural review for a Business Central repository?

Not just identify compiler errors or syntax issues—modern development tools already handle those effectively. What interested me was something much deeper.

Could an AI agent review a repository the way an experienced Business Central Solution Architect would? Could it evaluate not only whether the code works, but whether it follows best practices, remains maintainable, scales effectively, and aligns with long-term architectural standards? To answer that question, I developed an AI-powered AL Code Review Agent specifically designed for Microsoft Dynamics 365 Business Central development.

The goal was to create an intelligent reviewer capable of assessing multiple dimensions of solution quality, including:

• Coding standards and development best practices
• Performance optimization opportunities
• Error handling and exception management
• Documentation completeness and quality
• Localization and translation readiness
• Event design and extensibility patterns
• Repository governance and project structure
• Maintainability and long-term supportability
• Upgrade readiness and future compatibility
• Indicators of technical debt

However, simply finding issues was never the primary objective. Most code analysis tools can generate a list of warnings. The real challenge is helping developers understand why those issues matter. For every observation, the agent was designed to provide context, explain the potential impact, assess the level of risk, and recommend practical remediation steps. The objective was to transform a traditional code review into an architectural assessment that helps teams make better development decisions.

Because finding problems is useful. Understanding their impact—and knowing how to fix them—is where the real value begins. In many ways, the vision was not to build another code scanner. The vision was to create a virtual Business Central Solution Architect capable of providing consistent, scalable, and objective guidance across every repository and every pull request.

Putting the Agent to the Test

Building an AI Solution Architect was only the first step. The real question was whether it could provide meaningful insights when applied to an actual Business Central project. To validate the concept, I deployed the AI agent against a real-world Business Central extension repository containing multiple AL objects, extensions, and supporting project files. The repository represented the type of codebase commonly found in partner implementations and customer customization projects. My expectation was that the AI would identify a number of coding issues and best-practice violations.

What I did not expect was the depth and structure of the analysis, rather than generating hundreds of isolated warnings or technical observations, the AI produced a comprehensive architectural review report. The findings were categorized by severity, risks were clearly highlighted, positive development practices were acknowledged, and a prioritized remediation roadmap was generated to help the development team focus on the most impactful improvements first. The experience felt less like running a code analysis tool and more like receiving feedback from an experienced Business Central Solution Architect.

What impressed me most was not the number of findings. It was the quality of the observations.

The AI consistently focused on areas that architects typically prioritize during repository reviews, including performance optimization opportunities, localization and translation standards, documentation quality, repository governance, maintainability, and long-term upgrade readiness. In several cases, the agent identified issues that would likely pass functional testing without any immediate problems but could create challenges months or years later as the solution evolved. These are precisely the types of observations that experienced architects look for—not simply whether the code works today, but whether it will remain scalable, maintainable, and supportable in the future. That was the moment I realized this was more than just an AI-powered code review tool. It was demonstrating the potential to bring architectural thinking into every repository review, regardless of project size or team capacity.

Why This Matters for Business Central Teams

As Business Central solutions continue to grow in complexity, maintaining consistent code quality becomes increasingly challenging. Development teams are under constant pressure to deliver new features faster, while architects are expected to review an ever-growing volume of code and ensure long-term solution quality.

The challenge is that experienced Business Central architects are a limited resource. This is where AI has the potential to transform the development lifecycle.

Imagine every pull request automatically receiving an architectural review, performance assessment, documentation validation, maintainability analysis, localization checks, and upgrade readiness recommendations before a human reviewer even looks at the code. Instead of spending valuable time identifying common issues and enforcing basic standards, architects can focus on higher-value activities such as solution design, strategic decision-making, technical leadership, and mentoring development teams. In essence, AI doesn’t replace architectural expertise—it helps scale it across every repository, every project, and every pull request.

The Future of Business Central Development

Much of today’s conversation around AI focuses on code generation. While the ability to generate AL code faster is undoubtedly valuable, I believe the far greater opportunity lies in intelligent governance.

Writing code faster improves productivity. Writing better code consistently improves the entire lifecycle of a solution. The future of Business Central development may not be defined by how quickly AI can generate code, but by how effectively it can help development teams maintain quality, enforce standards, reduce technical debt, improve upgrade readiness, and ensure architectural consistency across every repository. As Business Central solutions continue to evolve, these challenges become increasingly important. Every customization, extension, and enhancement adds complexity. Without strong governance, even successful projects can accumulate technical debt that impacts maintainability, performance, and long-term scalability.

This is where AI can create significant value. Not by replacing architects, but by extending their reach.

Business Central Solution Architects bring something AI cannot replicate—experience, business understanding, judgment, customer context, and the ability to make strategic decisions. Their role remains critical in designing solutions and guiding development teams. However, AI can help scale that expertise across more projects, more repositories, and more development teams than would otherwise be possible.

What began as an experiment to review AL code ultimately revealed a much bigger opportunity.

The future may not be a world where every development team hires more architects. Instead, it may be a world where every pull request has access to architectural guidance before a human reviewer ever opens the code. A world where quality checks, performance assessments, documentation reviews, upgrade readiness analysis, and governance validations happen automatically and consistently. If that vision becomes reality, it could fundamentally change how Business Central solutions are built, reviewed, supported, and maintained and that is where the true long-term value of AI in Business Central development may ultimately be found.