A response to the challenges outlined in this LinkedIn post about AI coding assistants and accumulating technical debt
In a recent LinkedIn post, a development leader shared a sobering story: their team shipped a feature in three days using AI coding assistants – a task that previously took three weeks. Six months later, they couldn’t fix a critical bug in it. The team had been “accumulating technical debt at 3x speed.”
The post describes a familiar pattern emerging across the industry: impressive velocity metrics followed by maintenance nightmares. AI-generated code that works but that nobody truly understands. Functions calling functions in patterns no one recognizes. Comments describing what the code does, not why. No documented architectural decisions or design rationale.
The proposed solution – the BMAD Method – involves specialized AI agents, structured documentation, control manifests, dual reviews, and continuous ledgers. It’s a thoughtful approach that “kept 80% of the speed” while regaining “100% of the traceability.”
But here’s the thing: there’s an alternative approach that inherently solves these problems rather than managing them.
The Low-Code Alternative
Low-code development platforms sidestep the entire category of problems described in the original post. Here’s why:
Visual Architecture as Living Documentation
Unlike AI-generated code where “nobody understood the code,” low-code platforms provide visual representations of your application architecture. The schema, relationships, and business logic are modeled graphically, making the system structure immediately comprehensible to anyone on the team. There’s no mystery about opaque function chains or unrecognizable patterns – the architecture is the documentation.
Declarative Design Over Generated Code
With low-code tools, you’re not accumulating thousands of lines of AI-generated implementation code. Instead, you define your application through high-level abstractions:
- Data models and their relationships
- Business logic through visual workflows or constrained scripting environments
- UI components through structured configuration
When you need to modify functionality six months later, you’re working with these high-level abstractions, not trying to decipher implementation details that no one on your team wrote or fully understands.
Inherent Traceability Without Overhead
The BMAD Method seeks to add traceability through extensive documentation, versioning, and process discipline. Low-code platforms provide this by default. Schema changes are tracked and versioned, relationships between entities are explicit, and business rules are defined in structured, auditable formats.
Graph-Based Low-Code: Taking It Even Further
Now, here’s where it gets truly interesting: not all low-code platforms are created equal.
Traditional low-code tools use visual representations of high-level abstractions, which is already a significant improvement over managing raw code. But platforms that store the configuration of low-code runtime elements as a connected graph – for example, in a graph database – go a step further entirely.
Running Directly Off Abstract Definitions
In a graph-based low-code platform, your application doesn’t just have a visual representation that gets compiled into traditional code. Instead, the application runs directly off the abstract definitions stored in the graph:
- The schema definitions
- The business logic rules
- The frontend elements
- The relationships between all these components
There’s no hidden implementation layer. What you model is what runs. The gap between design and runtime disappears entirely.
Everything Is Connected
In a system where all components of an application are stored as nodes and relationships in a connected graph, you can simply follow the relationships to understand how everything fits together:
- Want to see what business logic uses a particular data type? Follow the edges.
- Need to find all UI components that display certain data? Trace the connections.
- Wondering what happens when a user triggers an action? Walk the graph.
This isn’t documentation that might be out of sync with reality. This is the reality of your application, queryable and traversable at any time.
AI Agents and Graph-Based Configuration
Here’s the crucial insight for teams using AI assistance: it’s orders of magnitude easier for AI agents to operate on a configuration graph than to create and maintain a text codebase for a project of the same complexity.
Think about it:
- Structured data vs. unstructured code: A graph database stores your application as structured, typed data with explicit relationships. AI doesn’t need to parse syntax, infer intent, or guess at architecture – it’s all explicit.
- Atomic, verifiable changes: Modifying a graph node or relationship is a discrete, auditable operation. Compare this to AI modifying code where a change might have ripple effects that are hard to predict or verify.
- Constraint enforcement: Graph schemas enforce data integrity. AI can’t accidentally create invalid configurations or break relationships – the database prevents it.
- Queryable context: AI can query the graph to understand the full context of any change. “Show me all business logic that uses this entity type” is a simple graph query, not a complex code analysis task.
- Reversible operations: Graph-based changes are transactional and can be rolled back cleanly, unlike code modifications that might require careful merging and conflict resolution.
In essence, when your application is a graph, AI becomes a tool for structured data manipulation rather than code generation. This is a fundamentally more tractable problem.
The Real Comparison
Let’s compare the two approaches six months after initial development:
Traditional AI-Generated Code (even with BMAD):
- Extensive documentation to maintain
- Code reviews by humans AND AI agents
- Control manifests defining boundaries
- Continuous ledgers tracking changes
- Still requires deciphering implementation details
- 80% of the speed, significant process overhead
Graph-Based Low-Code:
- Visual schema remains immediately comprehensible
- Relationships are explicit and queryable
- Changes made to high-level abstractions
- New team members understand the system quickly
- AI assistance works with structured data, not code generation
- Comparable or better speed, minimal process overhead
The Fundamental Question
The original post asks teams to implement extensive process discipline to manage AI-generated code. But perhaps we’re asking the wrong question.
Instead of “How do we add discipline to AI code generation?” maybe we should ask: “Why are we generating traditional code at all when higher-level approaches solve these problems inherently?”
And taking it further: “Why aren’t we using graph-based representations where AI can work with structured configuration data rather than generating and maintaining complex codebases?”
The Path Forward
Don’t get me wrong – the BMAD Method represents thoughtful engineering discipline. Teams stuck in traditional development with AI assistants would benefit from it.
But there’s a better path: choosing development platforms that:
- Work at the right level of abstraction
- Store application structure as connected, queryable data
- Run directly off those abstract definitions
- Make AI assistance a tool for data manipulation rather than code generation
This isn’t about choosing between speed and maintainability. It’s about choosing an approach where they’re not in conflict to begin with.
Have you experienced the AI technical debt cycle described in the original post? Have you considered how graph-based low-code platforms could fundamentally change the equation?
For teams building complex applications who want velocity without technical debt, exploring graph-based low-code platforms might be the most strategic decision you make this year.
This post was written in response to Benny Cheung’s excellent analysis of AI coding challenges. If you’re dealing with AI-generated technical debt, his BMAD Method is worth studying – and considering whether a different paradigm might serve you even better.
