Wenn the Vibes Begin to Fade - Systemic Limits of AI Code Assistants in Complex Projects

In this article, we analyze insights gained from three real-world development projects to illustrate the practical use of modern AI coding assistants. The first is a Python project for linear optimization comprising a few hundred lines of code; the second, a C# heuristic based on beam search with around two thousand lines of code; and the third, a SvelteKit application managing wholesalers, products, and offers, spanning roughly 50,000 lines of code.
The AI systems employed include ChatGPT 5, Claude Sonnet 4.5, Claude Opus 4.1, and Gemini 2.5 Pro.
The vibe coders — JetBrains Pro AI Agent (Rider, Webstorm), GitHub Copilot (VSC), and Claude Code — promise deeper contextual understanding of larger codebases.

1 Large Language Models and the Nature of Probabilistic Generation

Modern AI coding assistants are built on large language models (LLMs), trained to predict the next token based on statistical likelihood. Rather than computing explicit logical proofs, they perform probabilistic continuation: each generated token is selected according to conditional probability distributions learned from training data.

This mechanism enables remarkable fluency in both natural language and source code, but it also imposes structural limits. The model’s reasoning is local, governed by token proximity and statistical association rather than by deterministic logic, type systems, or formal proofs.

In software engineering, this means that correctness, consistency, and architectural compliance cannot be proven within the model’s internal process. LLMs reproduce syntax and stylistic patterns but lack the capacity to verify that generated code will compile, execute, or integrate coherently into a larger system. These probabilistic foundations define the systemic constraints examined throughout this article.

2 Operational Model of AI Coding Assistants

An assistant operates within a bounded conversational session, exchanging messages and code fragments with the user. Each system maintains a context window — a token-based memory limit defining how much information can be processed simultaneously. When new material exceeds that limit, earlier content is either removed or summarized, reducing effective memory.

Two operational modes can be distinguished. In a complete-bundle configuration, the entire project is provided upfront, enabling a consistent internal model while it fits within the context limit. In contrast, incremental or vibe-coding workflows build awareness gradually as the developer exposes parts of the codebase. The assistant never holds the entire system simultaneously, forming a partial and evolving representation. Retrieval mechanisms complement this process by reloading relevant material on demand through semantic or symbolic search.

The following section details how such sessions manage and reconstruct context across multiple files.

3 Context, Retrieval, and Embeddings

3.1 Multilayered Context in IDE-Integrated Assistants

In practical implementations, IDE-integrated assistants assemble several layers of context for each generation step:

• Local editing state: the currently edited files (open "tabs" in the IDE), including the code immediately surrounding the insertion point and the most recent developer changes.
• Indexed project context: information derived from the IDE’s internal code index — such as symbol definitions, imports, and type relations — from other files within the same project.
• Embedding-based retrieval: semantically related fragments from the broader codebase, dynamically fetched when relevant.

These layers are merged through a prompt-composition process, in which the integration heuristically determines which excerpts — function signatures, class definitions, or short code blocks — fit into the available context window for the next model call.

3.2 Function of Embeddings

Embeddings are vector representations of text or code that capture semantic proximity. They enable similarity-based retrieval: given an embedding of the current query or prompt, the system identifies nearby vectors in a pre-computed index of the project and retrieves the corresponding fragments. This process — known as retrieval-augmented generation (RAG) — extends the assistant’s effective reach beyond its immediate conversational memory (Ref. 1).

However, embeddings represent approximate meaning, not precise structure. They do not preserve control flow, dependency hierarchies, or type safety. The retrieved fragments are therefore thematically relevant but not guaranteed to be logically consistent with the overall system.

3.3 Observable Runtime Behavior

Empirical traces show a recurring read–search–edit cycle. The assistant inspects selected files, generates modifications, searches for related elements, loads additional fragments, and repeats the process.
A representative sequence of operations may look as follows:

read(file A) → read(file B) → search("symbol") → update(changes) → read(file C) → update …

This pattern indicates that only the local editing state persists continuously, while indexed context and retrieval layers are reconstructed for each iteration. The resulting context turnover means that relevant portions of the project are repeatedly re-extracted and re-inserted into the limited context window, replacing previous details.

3.4 Systemic Outcome

Even with multiple layers, assistants remain confined by the finite context window. Prompt composition relies on heuristic selection, embeddings provide approximate rather than exact information, and no complete project state is retained persistently. As a result:

• Partial knowledge: cross-file relationships and global invariants rarely coexist in memory.
• Repeated re-loading: relevant code must be fetched anew for each operation.
• Incomplete architectural model: similarity search cannot reconstruct full structural dependencies.
• Probabilistic reasoning: generation remains statistical, allowing plausible but incorrect code to appear.

This layered design increases reach and responsiveness but does not remove the underlying constraints of bounded memory and probabilistic reasoning.

3.5 Advanced Context Solutions: The Model Context Protocol (MCP)

Recognizing the limitations of generic, on-the-fly context retrieval, platform providers are introducing more sophisticated, protocol-based solutions. A prominent example is the Model Context Protocol (MCP), utilized by enterprise versions of GitHub Copilot (Ref. 2).

The MCP is designed specifically to solve the context problem described earlier. Instead of relying solely on embedding-based similarity search, an MCP-compliant server acts as a dedicated intermediary, providing the LLM with a centralized, curated knowledge source. It allows an organization to index its entire private codebase, internal documentation, and coding conventions, delivering a highly relevant and authoritative context payload for each generation request.

This protocol-driven approach offers several advantages over standard retrieval:

• Higher Precision: The protocol enables the delivery of precise API definitions and internal library usage patterns instead of merely "semantically similar" but potentially incorrect snippets.
• Reduced "Hallucination": By grounding the model in a trusted knowledge base via a structured protocol, it significantly reduces the risk of generating references to non-existent code.
• Improved Consistency: It helps enforce team-specific conventions and architectural patterns by making them a primary part of the provided context.

The existence of a dedicated protocol like MCP directly validates the central thesis of this article: that effective context management is a critical bottleneck for AI assistants. While MCP represents a significant step toward solving the context problem, it does not alter the probabilistic nature of the LLM itself. The generation step remains a statistical prediction.

Therefore, even with perfect context delivered via MCP, the generated logic can still contain subtle bugs or security flaws, reinforcing the need for the deterministic validation cycles described in Section 6.

4 Practical Risks and Workflow Impact

The combination of probabilistic inference and restricted context produces tangible inefficiencies and risks in daily development.

4.1 Probabilistic Generation and Its Consequences

The probabilistic mechanism becomes most visible when assistants operate on partial project data. Each completion or suggestion is a continuation of observed tokens, guided by statistical similarity rather than structural verification. Even when a model reproduces naming conventions and syntax accurately, correctness depends on relationships that may lie outside the visible window.

Typical manifestations include:

• code that appears coherent but does not compile;
• references to undefined variables, parameters, or functions;
• cross-layer violations, such as backend logic generated inside UI components.

These outcomes reflect not software bugs but the inherent behavior of probabilistic generation itself. The system produces plausible text without a mechanism to test its validity against the actual program. This gap between probabilistic fluency and deterministic correctness defines the operational risk underlying all subsequent workflow patterns.

4.2 Dev-Workflow: Capacity and Planning Uncertainty

Most assistants do not expose their remaining context capacity, leaving developers unaware of how close a session is to its memory ceiling. Extended multi-file edits can fail abruptly once this limit is reached. The absence of transparency introduces a subtle dependency: workflow continuity becomes constrained by parameters outside the user’s control.

When the context or memory ceiling is hit, AI agents need to compactify the conversation — that is, to create an internal summary representation of the existing context and then discard most of the detailed knowledge previously held in memory. Some vibe coders perform this process silently in the background. The system appears to operate continuously, but, put provocatively, it resembles the Memento effect — losing its short-term memory while still believing it remembers.

This effect is less noticeable with vibe coders because they dynamically re-fetch what seems to be the relevant context through the retrieval mechanisms described earlier. However, this reconstruction is approximate, not identical: each cycle subtly reshapes the assistant’s internal model of the project.

This dependency is reinforced by usage-based pricing. Unlike traditional IDE licensing, AI assistants meter interaction volume via token consumption. Hitting a subscription limit may disable essential features, making team productivity subject to vendor-defined thresholds rather than technical competence.

4.3 Efficiency and Memory Overhead

While eliminating routine work, many assistants introduce coordination costs. Developers restate rules, and verify code that looks correct but fails in testing. The human can become the assistant’s external memory controller. The cumulative time spent clarifying and correcting can offset the nominal efficiency gain from automation.

4.4 Integration Limitations

Software development is inherently iterative and stateful, involving continuous builds, testing, and deployments. AI assistants, however, operate on static context snapshots with limited persistence, limited awareness of file-system changes, and weak coupling to build or test pipelines. This disconnect makes sustained refactoring and architectural evolution fragile and error-prone.

4.5 Security Considerations

Probabilistic generation can introduce subtle security flaws. Typical examples include unparameterized database queries, missing validation, or incomplete authentication flows, which are common vulnerabilities highlighted by security standards like the OWASP Top 10 for LLM Applications (Ref. 4). A 2024 study by Veracode revealed that 45% of AI-generated code contains security flaws (Ref. 6). Furthermore, a study from Stanford University found that participants using an AI assistant wrote significantly less secure code than those without one (Ref. 5).

Because the generated text is presented with steady confidence, such issues can escape notice. AI-produced code should therefore be treated as untrusted input and subjected to deterministic static-analysis tools such as CodeQL, SonarQube, or Veracode before integration.

4.6 Cognitive Offloading

A recurring behavioral pattern observed among developers integrating AI agents into their workflow is cognitive offloading — the tendency to delegate mental effort to an external system. Humans naturally strive for efficiency: to achieve the greatest possible outcome with the least possible effort. This evolutionary bias manifests in software development as well.

Traditionally, an effective developer is driven by the desire to understand the system they are working on — its architecture, design, and internal logic. Learning a new codebase typically follows the OODA cycle: observe, orient, decide, act. With each iteration, understanding deepens until the developer internalizes the relevant structures and conventions.

However, the introduction of AI agents changes this process. When a developer requests a new functionality, the assistant quickly produces plausible code fragments. If the result is not yet correct, the user simply instructs the agent to fix it — after all, it was the agent’s output that caused the issue. This seemingly harmless feedback loop creates a shift in responsibility: instead of analyzing and understanding the error, the developer delegates problem-solving to the system.

Over time, this dynamic replaces the virtuous learning loop — in which active engagement strengthens understanding — with a vicious circle of dependency. The cycle becomes: generate → review → detect problem → request fix → review …
In this mode, the developer acts primarily as an observer rather than as an active problem-solver. Empirical research in educational psychology consistently shows that active engagement produces deeper learning outcomes than passive observation. Even haptic involvement — such as handwriting or manual debugging — significantly improves retention and conceptual understanding (Ref. 7, 8). When the assistant performs most of the cognitive and mechanical work, the developer’s procedural memory and internal system model remain shallow. Syntax patterns, design principles, and error-resolution strategies are less likely to be retained.

This leads to repeated reliance on the agent. Since key aspects of the system were never fully internalized, the developer resorts to asking the assistant again — and again. A few exceptional individuals may memorize structural relationships merely by reading or observing, but for the majority, effective learning requires active doing.

Another related phenomenon is self-efficacy (Selbstwirksamkeit): the subjective sense of competence and control that arises when one can effectively navigate a system. As developers become familiar with the architecture, they gain confidence — a feeling of “I have this under control.” In AI-mediated workflows, this confidence can erode. The constant presence of an external “expert” subtly undermines autonomy: an inner voice begins to question, “Am I sure this is correct? Wouldn’t it be safer to let the assistant handle it?”
Lower perceived self-efficacy correlates strongly with increased reliance on external guidance and reduced problem-solving persistence (Ref. 9).

In summary, while AI agents accelerate certain tasks, they also foster cognitive outsourcing. The immediate convenience of delegation can prolong the learning phase, weaken system understanding, and diminish the developer’s sense of mastery — turning an empowering tool into a source of quiet dependency.

5 Strategic Use and Safe Boundaries

Effective deployment of AI assistants requires clearly defined operational limits. Reliable work on complex systems would demand persistent memory across sessions, retrieval mechanisms that preserve structural relations, visible indicators of context usage, and tighter integration with compilation and testing workflows.

Current assistants perform best when used for:

• scaffolding or boilerplate generation,
• explanation and translation of unfamiliar code,
• exploration of new libraries, and
• development of small, pattern-consistent modules.

However, their utility diminishes in more demanding scenarios. Without significant integration effort—such as implementing custom MCP servers and maintaining a comprehensive, up-to-date knowledge base—they are less suited for:

• mature codebases with specialized conventions,
• cross-component refactoring,
• architecture-level or performance-critical tasks, and
• security-sensitive domains.

Recognizing when to disengage is essential: repeated undefined references, non-compiling output, or escalating correction cycles signal diminishing returns.

6 Toward Deterministic and Hybrid Systems

The main limitation of current assistants lies in their probabilistic foundation. Achieving reliability requires combining generative flexibility with deterministic validation.

6.1 Reducing the “Limited Context Window” Problem

The limited context window is a major driver of the challenges described earlier and the reason behind many common workarounds — such as selective re-reading of files, extensive use of embeddings, or “project search engine” integrations that reload relevant fragments on demand.

Significantly expanding the context window would substantially mitigate the memory-related constraints of AI agents, though it would not resolve the underlying issue of probabilistic generation. The fundamental problem is that the computational cost of large language models increases approximately quadratically with the size of the context window. As the window grows, both memory consumption and inference time rise sharply, creating practical limits on scalability.

Emerging architectures such as xLSTM (developed by Prof. Sepp Hochreiter and colleagues, Ref. 10) and other long-context recurrent models (Refs 12 and 13) offer promising alternatives. These models aim to maintain or even improve performance while reducing the resource requirements associated with long-range dependencies. By enabling efficient handling of extended sequences, such architectures could significantly alleviate current context constraints — and thereby reduce the need for complex retrieval or embedding-based workarounds.

6.2 Structural and Logical Validation

A more dependable architecture can build on two deterministic layers:

• Structural validation: provided by the language-server architecture of modern IDEs, which maintains an abstract syntax tree (AST) encoding functions, variables, and type relations (Ref. 3). An assistant referencing this model cannot hallucinate non-existent entities, ensuring syntactic integrity.
• Logical validation: achieved through automated reasoning (AR) applied to the AST. Structural facts act as axioms (e.g., “UserService resides in the Service layer”), while architectural rules define admissible dependencies (e.g., “Service must not invoke Database directly”). The AR engine can then verify whether proposed changes comply with these constraints.

Together, these layers transform generation from statistical prediction into rule-constrained synthesis.

6.3 Hybrid Generation–Validation Loop

A hybrid system integrates probabilistic generation with deterministic validation through a generate–validate cycle:

• The LLM produces a candidate modification in response to the user request.
• The structural validator checks syntactic consistency against the AST.
• The logical validator tests compliance with architectural rules.
• Only code passing both checks is presented to the developer.

6.4 Barriers to Adoption

Three challenges currently impede this approach:

• Latency: deterministic validation increases computational cost, conflicting with expectations of instant response.
• Incomplete states: work-in-progress code is often syntactically invalid, complicating continuous validation.
• Formalization effort: few projects maintain machine-readable specifications of architectural rules required for automated reasoning.

Consequently, most current assistants prioritize responsiveness over verifiable correctness.

7 Conclusion

AI coding assistants — whether operating on a complete bundle or through incremental workflows — remain limited by bounded context and probabilistic generation. These properties restrict their accuracy, scalability, and predictability. Even with advanced solutions like the Model Context Protocol, which dramatically improve context precision, the final code generation step remains inherently probabilistic.

When used deliberately — for exploration, explanation, and routine generation — such assistants provide tangible utility. For sustained, multi-component, or safety-critical development, however, their probabilistic nature introduces irreducible uncertainty.

Future progress depends on hybrid architectures that combine probabilistic synthesis with deterministic validation. Until such systems mature, disciplined review and formal analysis remain the foundation of dependable software engineering.

References

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