DEV Community: ModernCpp

Why New Language Features Need to Target AI Agents, Not Developers

ModernCpp — Thu, 11 Jun 2026 14:12:20 +0000

For the last 50 years, programming language evolution has had one single north star: Human Ergonomics. We design syntax sugar, optional typing, and complex implicit behaviors to make code faster to write and easier for human brains to read.

But we are ignoring the elephant in the repository.

With autonomous platforms like OpenHands, GitHub Copilot workspace, and custom multi-agent reasoning loops writing, refactoring, and deploying code, the primary consumer of source code is radically changing. We aren’t just writing code for our peers anymore; we are writing code that AI agents must ingest, parse, and modify.

It’s time to face a contrarian truth: New language features must be optimized for AI processing, not human typing convenience.

1. Syntax Sugar is Token Poison

Humans love concise, clever shortcuts. We love implicit returns, ternary operators, and magical macros that turn 10 lines of code into 1.

For an AI, however, syntax sugar often adds unnecessary ambiguity. When an LLM generates or parses code, it breaks it down into tokens. Highly implicit, dense syntax requires more reasoning steps and token overhead for the model to successfully predict structural intent.

Instead of adding more magical, compressed syntax features, modern language updates should focus on explicitness. Explicit type declarations, mandatory block boundaries, and structured configurations make it vastly easier for an AI agent to read an abstract syntax tree (AST) and execute a flawless refactor without hallucinating edge cases.

2. The Nightmare of Code Sprawl and Context Windows

AI agents can write code faster than any human engineering team in history. But that speed introduces a terrifying side effect: Accelerated Code Sprawl. When bots generate thousands of lines of code across massive dependency structures, they easily shoot past their own context window limitations. If a language allows tightly coupled architectures, messy implicit imports, or sprawling global states, the AI will quickly lose track of the codebase logic, resulting in catastrophic dependency loops and structural decay.

Languages need built-in architectural guardrails. We don't need features that let us write code faster; we need language features that enforce strict boundaries, isolate modules natively, and flag structural corruption right at the compiler level.

3. Designing for Compiler-Driven AI Feedback

The most effective way an AI agent improves its code is through a tight feedback loop with the compiler or a static analyzer.

If an LLM writes bad code, a cryptic compiler error message helps no one. Language features should evolve to provide semantic, structured error payloads (like JSON-formatted diagnostics) rather than raw text outputs. Imagine a compiler or static analysis engine that doesn't just say Error: NullPointerException, but explicitly outputs a deterministic matrix map of the structural loop causing the failure.

When the language infrastructure speaks the same structured data language as the AI, agentic self-healing code transitions from a sci-fi concept into standard CI/CD reality.

Conclusion: The New Dev/AI Symbiosis

This doesn't mean human developers are obsolete. It means our roles are shifting from "code typists" to software architects and system reviewers.

If we continue to update languages like C++, Java, or Rust based purely on how many keystrokes it saves a human finger, we are optimizing for the past. The languages that dominate the next decade will be the ones that turn code into a clear, predictable, and highly structured data asset that AI agents can refactor with 100% mathematical precision.

What do you think? Should language designers start prioritizing LLM readability over human convenience, or are we risking making code completely unreadable for the humans left in the loop? Let’s discuss in the comments below.

5 Powerful Tips to Generate Better Code with AI

ModernCpp — Wed, 11 Feb 2026 22:50:09 +0000

This isn’t another “how to prompt AI” post.

This is about lesser-known habits that dramatically improve the quality of code you get from LLMs.

Tip 1 — Say you’re not satisfied (immediately)

If the generated code isn’t what you expected, say it clearly and early.

Why this works

LLMs adapt within the conversation.

When you signal dissatisfaction, you push the model to:

Reconsider assumptions
Apply stricter reasoning
Explore alternative approaches

Very often, the second answer is far more thoughtful than the first.

Don’t accept “almost right” code. Push back.

Tip 2 — Ask twice (even when it’s good)

Even if the solution works, try this:

“Improve this code.”

You’ll be surprised how often AI will:

Simplify logic
Improve naming
Enhance performance
Reduce edge-case risk

Iterating with AI is like having a senior reviewer on demand.

Tip 3 — Give the big picture (real context matters)

AI performs dramatically better when it understands your architecture.

Instead of:

“Write a method that does X”

Try:

“This belongs to the service layer. We use a layered architecture:

Controller → Service → Repository. Where should this logic live?”

Now the AI can:

Place responsibilities correctly
Avoid leaking business logic into technical layers
Produce code that fits your design

The real power move

Instead of describing the system manually, use an AI Prompt Builder that injects real project context.

For example, tools like CppDepend’s AI Prompt Builder — a feature that generates reliable, context-aware prompts using:

Dependencies between components
Layer violations
Code metrics (complexity, size, coupling…)
Type relationships & call graphs

That context can be added to the prompt so the AI doesn’t guess — it understands your system’s structure.

Now AI isn’t just generating code.

It becomes an architecture-aware assistant working with real project data.

Tip 4 — Step away and refactor yourself

After generating multiple AI-driven methods, pause.

AI optimizes locally, not globally. You still must:

Reorganize classes
Merge duplicated logic
Extract shared abstractions
Clean design inconsistencies

AI accelerates coding.

You are still the architect.

Tip 5 — Ask AI to criticize its own code

This is one of the most underrated tricks.

After the code is generated, ask:

“What are the weaknesses of this code?”
“Where could this fail in production?”
“What edge cases are missing?”

Now the AI switches roles:

Creator → Reviewer

This often reveals:

Hidden bugs
Performance risks
Missing validations
Concurrency issues
Security gaps

You essentially get a free design review.

Final Thought

AI doesn’t replace engineering judgment — it amplifies it.

The best workflow looks like this:

Generator → Improver → Critic → Assistant

…while you remain the architect.

Focus on the developer skills and not only on the code quality.

ModernCpp — Wed, 13 Sep 2023 20:01:25 +0000

When we want to improve the code quality of a project we try to evaluate the code to detect if it’s well designed, well implemented and very well tested. After the analysis using the existing tools or even by a review from an expert we can get a report of what needs to be improved in the code to assure a delivery of a release with a minimum of issues.

But wait, who is the origin of the code? It’s a development team, each one has its responsibility. We can have an architect, the designers, the developers and the testers. And what matters more than the code is the team skills involved in the project, don’t forget that a bad architect could ruin your project even if you have excellent developers.

During the development process of a project try to elevate the development team skills by detecting regularly their weaknesses and correct them as soon as possible to avoid making the same mistakes for their next development tasks.

There are many tools that can help the developers to improve their code quality, many of them focus on the code and not on the developer skills. Scanyp is a highly customisable solution to adapt it to your needs to focuses more on the team skills. And It’s free for the projects less than 500K lines of code.

Scanyp offers a comprehensive evaluation of the team’s development proficiency across various categories, encompassing code implementation, design, unit testing, and code documentation. What effectively contributes to code quality improvement.

After the analysis using Scanyp the dashboard give us the whole picture about the skills scores and what more interesting is their evolutions, to check if the developers increase their skills or not:

However from the team skills page we have the details per category:

Code implementation score:

This score is calculated from the following scores:

The maintainability score which gives an approximation about the maintainability of the project.
The naming score is calculated from the rules detecting the violation of the names against the most known coding standard naming conventions of the programming language concerned. Of course you can from the admin area change these rules to match your chosen naming conventions.
The clones score which is calculated from the number of the clones detected in the source code
The code smells score which is calculated from the code smells detected in the code, and as many parameters in Scanyp we can also change the code smells parameters from the admin area to match our expectations. For example by default scanyp considers that a type is big if LOC>500. However, you can change this threshold from the script function of this query. These rules are grouped in the code smells group.
Compliance with the implementation rules which is calculated from the code implementation violated rules, And also you can modify as you want the rules provided by default concerning the implementation.

Note that all the numbers are clickable to have exactly where the problems are in the code.

The design score:

This score gives us a whole picture about the design of your project:

This score is calculated from the following scores:

Coupling score which is calculated from the most coupled types methods in the code base. If many types and methods are highly coupled this score become low,
Cohesion score which is calculated from the not cohesive types found in the project. If many types have a low cohesion, the score becomes low.
Design smells score which is calculated from the violated design rules, And like the code implementation smells we can easily change the design smells rules.
Rules compliance which is calculated from the violated design rules, And also you can modify as you want the rules provided by default concerning the design.

The bug free score:

This score gives us a whole picture about the issues detected in your project:

This score is calculated from the number of the issues detected. However the weight of an issue is proportional to its severity. Which means that you can have a low score if only 20 critical issues are detected and a good score even if you have 100 low issues detected.

The unit testing score:

Which represents the coverage percent of the project.

The documentation score:

This score gives us a whole picture about the documentation of the public types and methods of your project:

This score is calculated from the following scores:

Comments on public big methods and types : The documentation is needed for big types and methods. Indeed for small methods no need to affect the score if a documentation is not found.
Comments on public complex methods : For complex methods it is recommended to have documentation, and no need to affect the score if a not complex method with no documentation is detected.
Comments on interfaces/ abstract types: These kind of types are destined to be used as an API and it’s mandatory to have a documentation for each method exposed, so the user knows exactly what this method does.

To resume, as a team member try to focus to elevate your skills to avoid repeating the same mistakes, what effectivly contribute to have a high quality code.