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Timothy Smith (Zerovapor)
Timothy Smith (Zerovapor)

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The Next Evolution of Code Agents is Coming

#ai #openai #rag #machinelearning

The next evolution of LLM-generated code or code modification isn’t another fine-tuned model, nor is it a singular new tool or a tool chain. The next evolution of LLM code generation is going to be much deeper than you expect. To solve this, we need to understand that this is a multidimensional problem that LLMs cannot solve because they lack the ability to think, rationalize, and infer meaning.

Cursor, Windsurf, Codex, and cli tools like Gemini/Claude/Warp all do the same thing with a different coat of paint. They all optimize context stuffing, and not in a cost-effective manner. Here is the hard truth: context stuffing is meaningless without understanding the application as a whole, and we will continue to see inferior outputs from LLMs.

I am sure that many of you who Vibe Code (myself included) have noticed that more often than not, LLMs will duplicate code, ignore existing design patterns, and shortcut their way to an answer that is the quickest fix to the task provided. There are two primary reasons that I have identified as root causes, but I am sure there are many more.

Reason 1
The most optimal answer is the one that makes you the happiest, the quickest, or at least that's what they are trained to do.† LLMs are trained to be overly supportive and make you feel good. This is acceptable for general-purpose LLMs that handle day-to-day requests, but the collective we, as developers, should not want an LLM that makes us feel good. We need an LLM that delivers the best possible code implementation, not only solving the task we provided but also following existing design patterns or implementing design patterns that are aligned with the task at hand.

Reason 2
LLMs lack a rationale for the provided application. They fulfill the task asked of them, using the provided context or searching for task-relevant data. They lack a comprehensive understanding of the application as a whole; where are the separations of concern, does the application use DTOs, and how does the application handle the startup and teardown of testing apparatuses? These are just a few examples, but I hope you get the idea. This is a problem that you can’t solve with context stuffing, abstract syntax trees, or language servers. All of these provide code, not reasoning, not intent, no comprehensive knowledge. Let's not forget to mention the number of tokens and tool calls being made with every step of resolving the task.‡

Remember kids, the larger the context window grows, the more likely LLMs are to hallucinate or provide incorrect answers.§

So, I’m sure you're thinking by this point, this is great and all, but how do you plan on solving this, Mr. Know-it-all? I’m glad you asked. I’ve been working on a new process I’m calling Intent Querying. This represents a new approach to classifying, indexing, and retrieving code. This gives LLMs a comprehensive understanding of all existing code, patterns, and intent throughout the application, while also reducing the number of tokens used in each request.

This system consists of three major components.

  1. A high-fidelity "digital twin" of the code base.
  2. The "AI Reasoning Engine" that understands code relationships, intent, and semantics.
  3. A powerful Domain Specific Language (DSL) designed for agentic workflows. It translates natural language intent into structured, multi-perspective queries, allowing developers to ask questions not just about what the code is, but what it means from different viewpoints, like business purpose or code structure

DSL Example:
A developer needs to deprecate an old library or pattern and wants to find all conceptually related code, even if it doesn't share any text.

Before (Developer's Natural Language)
"I need to get rid of our old payment processing logic. Find all the code that's conceptually related to the deprecated 'processTransaction' function, and provide a mermaid chart of the workflow."

After (System's DSL Query - High Level):
The system translates this request into a precise, multi-faceted query, searching for code that is conceptually similar from both a business purpose and a code structure perspective simultaneously. To give a glimpse of how this structured intent is captured, the system might generate a query object that looks something like this:

FETCH [(~'func:processTransaction')-('doc:~5*Transaction')]=>[LENS: Business, Arch]
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This query would then be compiled into a multi-stage execution plan. While the actual process is in development, a conceptual model of the plan might involve parsing the DSL, utilizing AI Lenses to identify candidates, and then re-ranking the results based on their graph properties.

I’m not yet ready to release all the information about this project. There are still substantial amounts of work in progress, and I am continuing to work on the accompanying white paper.

The goal is not only to change how LLMs interact with codebases but also to transform our approach to utilizing LLMs in their entirety. We have only started to skim the surface of what we can do with LLMs, and this will be the next major unlock.

[†]Research supporting this remark:

  • Su et al. (2024).AI-LieDar: Examine the Trade-off Between Utility and Truthfulness in LLM Agents (arXiv:2409.09013)
  • Wei et al. (2024).How do Large Language Models Navigate Conflicts between Honesty and Helpfulness? (arXiv:2402.07282)

[‡]Research supporting this remark:

  • Zhang et al. (2025). How Does LLM Reasoning Work for Code? A Survey and a Call to Action (arXiv:2506.13932)
  • Wang et al. (2025). Bridging the Gap Between LLMs and Human Intentions: Progresses and Challenges in Instruction Understanding, Intention Reasoning, and Reliable Generation (arXiv:2502.09101)
  • Liu et al. (2025). Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMs(arXiv:2502.19411)
  • Chen et al. (2025). Break-The-Chain: Reasoning Failures in LLMs via Adversarial Prompting in Code Generation (ResearchGate)
  • Li et al. (2024). A Deep Dive Into Large Language Model Code Generation Mistakes: What and Why? (arXiv:2411.01414)

[§]Research supporting this remark:

  • Xu et al. (2025). Shadows in the Attention: Contextual Perturbation and Representation Drift in the Dynamics of Hallucination in LLMs (arXiv:2505.16894)
  • Li et al. (2025). Hallucinate at the Last in Long Response Generation: A Case Study on Long Document Summarization (arXiv:2505.15291)
  • Huang et al. (2024). Estimating Privacy Leakage of Augmented Contextual Knowledge in Language Models (arXiv:2410.03026)

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