While tools like Claude Code and Cursor can read massive codebases, they lack the architectural context senior engineers have. Without a semantic layer (a structured, machine-readable representation of system structure, relationships, constraints, and domain concepts), AI agents hallucinate APIs, violate architectural boundaries, and make incorrect assumptions about data flow.
As you can imagine, we've been doing a lot of thinking and experimenting around the AI-assisted Software Development Lifecycle (or as we call it, AI SDLC) here at CloudGeometry. I mean, let's face it, the days of grinding it out on your own instead of letting a coding assistant take care of at least some part of the process are over; that genie is well and truly out of the bottle.
And why not? Ask any engineer who has tried coding with a modern AI assistant and they'll tell you that whether they like them or not, these tools are impressive. Claude Code, Cursor, Copilot Workspace, and others can read large contexts, open multiple files, jump across a codebase, propose refactors, and even scaffold new features. You can point them at thousands of lines of code and the model will read and summarize them instantly.
So it's easy to see why developers often make one important assumption:
“If the AI can see all my code, then it understands the system.”
Wrong.
It's that gap between “reading” and “understanding” that is exactly where AI-generated code starts to break down.
So how do we get over that gap? The difference is having a semantic layer.
Developers Overestimate What AI Understands
When engineers adopt a coding assistant, they usually try it out on something straightforward, like boilerplate, or refactoring. Or they may vibe code something new. And in these situations, the result is usually pretty good, which leads developers to think of the coding agent like a fast, tireless senior engineer, and a senior engineer usually knows:
- the architecture of the system
- the module boundaries
- the data models and how they interact
- the naming conventions
- the intended layering
- the real API contracts
- the organizational workflows
- the historical decisions
- the parts of the system that are deprecated or fragile
But AI models aren't senior engineers, and they don’t come with this system-level knowledge. Especially when it comes to existing codebases they didn't build themselves, they don’t actually reason about your architecture. They just consume tokens and generate the most likely next token sequence.
In a real software system, meaning isn’t contained in one place. It’s scattered across:
- code files
- configuration files
- CI/CD pipelines
- IaC
- docs and wikis
- Product Requirement Documents
- Architecture Decision Records
- database schemas
- event logs
- tribal knowledge inside Slack messages or PR threads
Even humans struggle with this fragmentation. Expecting an LLM to reconstruct a correct mental model from raw text is unrealistic.
And that’s why a semantic layer matters.
So what is a semantic layer?
The term “semantic layer” sounds like a buzzword, but the underlying idea is straightforward:
A semantic layer is a structured, machine-readable representation of your system that captures what exists, how it connects, what is allowed, and why things work the way they do.
Concretely, it includes information such as:
1. System Structure
- modules
- directories
- services
- interfaces
- contracts
- configuration
- schemas
It gives the model an actual map instead of making it deduce structure by interpreting the code.
2. Relationships
- dependencies
- call graphs
- data flows
- publish/subscribe pathways
- ownership boundaries
Software is not flat. The semantic layer captures its topology.
3. Constraints
- coding conventions
- architectural boundaries
- security rules
- compliance requirements
- policies enforced by CI/CD
This is the difference between “possible” code and “acceptable” code.
4. Domain Concepts
- glossary
- business entities
- canonical names
- forbidden synonyms
- historical rationale
This prevents the AI from treating “account,” “tenant,” “customer,” and “organization” as interchangeable--unless that's what you intend to happen.
In other words, a semantic layer gives an LLM the same system context your senior engineers have — the stuff they internalize over years of working with the code.
Failure Modes Without a Semantic Layer
Without a semantic layer, you're likely to run into the same types of problems over and over again, and they all stem from how coding agents actually work under the hood. Again, they are not symbolic reasoners. They are not architectural analyzers. They are pattern-matchers operating on token sequences with no built-in concept of boundaries, constraints, or intent.
Here are the most common failure modes and what’s happening inside the model when they occur.
1. Hallucinated APIs
This is when the coding agent confidently generates function calls, parameters, or entire methods that don’t exist anywhere in your system. You end up with code that looks reasonable but fails immediately because the API it’s calling is imaginary.
Why it happens:
An LLM doesn’t “look up” your real API contracts. It infers them. When the agent sees references to similar method names or similar parameter shapes, it will interpolate the “most likely” version of that API based on its training data and the patterns it recognizes. If it can’t find a clear definition in the files it loaded, it fills in the blanks with what seems plausible, even if that function or field never existed in your system.
This isn’t a bug, it’s just how probabilistic language models work.
2. Incorrect Assumptions About Data Flow
The agent wires components together based on what “seems normal” rather than how your system actually moves data. This leads to code that calls the wrong services, bypasses queues, or misunderstands where certain data is supposed to come from.
Why it happens:
Most data flow in modern systems isn’t obvious by reading the code. You might have asynchronous queues, event buses, side effects triggered by config, cross-service calls buried in SDK wrappers, or operations happening in infrastructure code outside the repo entirely.
LLMs, however, assume direct call patterns because they were trained on millions of examples of simple, linear code. That bias shows up as wrong assumptions about:
- who calls whom
- what triggers what
- what part of the system owns a piece of data
When the agent can’t see an explicit mapping, it assumes the “default” patterns from its training set, not the architecture you actually have.
3. Violating Architectural Boundaries
In this case, the model places code in the wrong layer or makes components depend on things they shouldn’t. These mistakes might compile, and they might even work, but they erode the architecture and create long-term technical debt.
Why it happens:
LLMs have no concept of architectural rules like:
- “business logic can’t depend on presentation code”
- “data access must go through this layer”
- “service X must never import service Y”
To an LLM, all imports are just tokens, and all code is just one big sequence. If two files are in the prompt, the model assumes they are allowed to interact. That’s why you see generated code that looks fine in isolation but breaks your carefully maintained boundaries.
It’s not trying to break the rules. It just doesn’t know the rules exist.
4. Terminology Drift (Confusing Domain Concepts)
In this case, the agent uses the wrong names for core domain entities for example, using transaction, payment, and settlement interchangeably. This produces code that compiles but undermines the clarity and consistency of your domain model.
Why it happens:
LLMs treat synonyms as interchangeable. In natural language, "transaction", "payment", and "settlement" are often similar. In your system, they are not the same thing. But the model doesn’t know that unless you tell it.
Because its default behavior is to smooth over differences in terminology, the agent will:
- introduce inconsistent naming
- use the wrong entity for the wrong subsystem
- mix domain concepts that must remain distinct
You end up with code that compiles but no longer matches the mental model of your domain.
5. Code That Compiles but Doesn’t Integrate
The generated code passes syntax checks but breaks as soon as it interacts with the rest of the system. These are the hardest errors to debug because nothing looks “wrong” until you try running it.
Why it happens:
This is the most dangerous failure mode, and it’s also the hardest to detect. LLMs optimize for:
- syntactic correctness
- local coherence
- patterns that match training data
They do not optimize for:
- integration with the rest of your system
- runtime behavior
- lifecycle edge cases
- migration impact
- CI/CD pipeline effects
- compliance rules
- performance constraints
6. Overgeneralizing Patterns From Training Data
The agent applies generic patterns it learned from other codebases instead of the specific patterns your system uses. The result is code that feels stylistically off or misaligned with your established conventions.
Why it happens:
LLMs were trained on millions of codebases. Yours is not one of them.
When the model doesn't have explicit guidance, it defaults to:
- “common” architectural patterns
- “common” naming conventions
- “common” error-handling strategies
- “common” ways to structure tests
- “common” ways to do config
But “common” is not the same as “correct for your system.”
This is why you sometimes see AI-generated code that is stylistically perfect, but culturally alien to the way your team actually works.
7. Losing Track of Intent
The model “simplifies” or rewrites code that was written a certain way for a reason, often violating a design decision or hidden constraint. Over time, this causes subtle regressions that chip away at the system’s integrity.
Why it happens:
LLMs have noimplicit memory of why something was built. If a piece of code looks odd or indirect, the model will try to “simplify” it even if the indirect approach exists for a reason, such as:
- historical bugs
- performance bottlenecks
- business logic quirks
- compliance rules
- legacy interop
- developer experience considerations
Without the “why,” the model can only optimize for aesthetics or general patterns — not the real constraints you’re working under.
8. Fixing Problems With Edge-Case Hacks
In this case, the agent produces code that “works” only by adding special-case branches, bypasses, or one-off conditionals because it can’t see how the behavior should fit into the existing architecture. You end up with fixes that solve the immediate symptom but quietly violate the system’s intended design.
Why it happens:
LLMs don’t understand architectural patterns or cross-component responsibilities; they only see local context. When they can't reconcile how a change should integrate with upstream or downstream components, they default to patching over the gap with local conditional logic — basically cargo-culting behavior that looks right but breaks architectural cohesion.
How the semantic layer fixes these problems
By documenting the facts of your system (and even providing an opportunity for you to provide human review of those facts), the semantic layer removes the uncertainty that leads the coding agent to go with the most probable answer rather than the deterministically correct answer.
Why This Matters More as AI Takes Over the SDLC
Right now, most teams let coding agents handle small, isolated tasks, which hides the real risk: these tools won’t stay confined to safe refactors and boilerplate for long. As they begin touching multiple modules, evolving APIs, adjusting schemas, or modifying workflows, the consequences of misunderstanding the system grow quickly. A single wrong assumption can break an invariant, introduce subtle regressions, or cause drift between services — and those problems compound as the pace of AI-generated changes increases.
The Semantic Layer Is the Next Phase of AI-Driven Development
As coding agents become more capable, it’s tempting to think the future of AI in software development will come from bigger or better models, larger context windows, or smarter prompting techniques. But none of that addresses the fundamental gap we’ve been talking about: the gap between what an AI can read and what it can understand. The agents aren’t struggling because they lack raw capability, they’re struggling because they're not grounded. They operate on patterns when what they really need is structure.
The semantic layer is how we supply that structure. It gives the coding agent the same systemic context engineers accumulate over years: the rules, boundaries, relationships, contracts, workflows, and domain language that make a complex codebase coherent. And once that context exists in a machine-readable form, something important happens: the agent stops acting like a probability engine and starts behaving like a participant in the architecture. Instead of guessing what “should” be true, it works with what is true.
This is ultimately why the semantic layer isn’t just a helpful enhancement. Instead, it’s the next phase of AI-driven software development. As agents take on more responsibility, the cost of operating without a shared mental model grows exponentially. You can’t move fast if every AI-generated change introduces a little bit of architectural drift. You can’t scale AI across teams if each agent is working from a different, incomplete understanding of the system. And you certainly can’t rely on AI to evolve your platform if the model only sees fragments instead of the whole.
A semantic layer changes that dynamic. It makes the system legible. It keeps the architecture intact. It preserves intent. It anchors the agent’s behavior in the real structure of your software rather than in whatever it learned from the wider internet.
So as AI takes on a larger role in the SDLC, the question isn’t whether coding agents will become central to how we build software. That part is already happening. The real question is whether we’ll give these agents the context they need to operate safely and effectively. If we want AI to be a reliable collaborator instead of just a fast autocomplete, then the semantic layer is the path forward.
