For a while now, I’ve been experimenting with AI coding tools and there’s something fascinating happening when you combine Rust with agents such as Claude Code or OpenAI’s Codex: The experience is fundamentally different from working with Python or JavaScript - and I think it comes down to one simple fact: Rust’s compiler acts as an automatic expert reviewer for each edit the AI makes.

If it compiles, it probably works; that’s not just a Rust motto: it’s becoming the foundation for reliable AI-assisted development.

The problem with AI coding in dynamic languages

When you let Claude Code or Codex loose on a Python codebase, you essentially trust the AI to get things right on its own: Sure, you have linters and type hints (if you are lucky), but there is no strict enforcement: the AI can generate code that looks reasonable, passes your quick review, and then blows up in production because of some edge case nobody thought about.

With Rust, the compiler catch these issues before anything runs. Memory safety incidents? Caught. Data runs? Caught. Lifetime issues? You guessed it—caught in compiler time. This creates a remarkably tight feedback loop that AI coding tools can actually learn from in real time.

Rust’s compiler is basically a senior engineer

Here is what makes Rust special for AI coding: the compiler doesn’t just say “Error” and leave you guessing: It tells you exactly what went wrong, where it went wrong and often suggests how to fix it; this is absolute gold for AI tools like Codex or Claude Code.

Let me show you what I mean: say the AI writes this code:

fn get_first_word(s: String) -> &str {
let bytes = s.as_bytes();
for (i, &item) in bytes.iter().enumerate() {
if item == b' ' {
return &s[0..i];
}
}
&s[..]
}

The Rust compiler doesn’t fail just with a cryptic message, but it gives you:

error[E0106]: missing lifetime specifier
--> src/main.rs:1:36
|
1 | fn get_first_word(s: String) -> &str {
|                   -             ^ expected named lifetime parameter
|
= help: this function's return type contains a borrowed value,
but there is no value for it to be borrowed from
help: consider using the `'static` lifetime
|
1 | fn get_first_word(s: String) -> &'static str {
|                                 ~~~~~~~~

Look at this. The compiler is literally explaining the ownership model to AI - it is saying to - “Hey, you’re trying to return a reference but the thing you’re referencing will be dropped when this function ends - that’s not going to work.”

For an AI coding tool, this is structured, deterministic feedback. The error code E0106 is consistent; the location is found to the exact character; the explanation is clear; and there’s even a suggested fix (though in this case the real fix is to change the function signature to borrow instead of taking ownership).

Here’s another example that constantly happens when AI tools write concurrent code:

use std::thread;

fn main() {
let data = vec![1, 2, 3];

let handle = thread::spawn(|| {
println!("{:?}", data);
});

handle.join().unwrap();
}

The compiler response:

error[E0373]: closure may outlive the current function, but it borrows `data`
--> src/main.rs:6:32
|
6 |     let handle = thread::spawn(|| {
|                                ^^ may outlive borrowed value `data`
7 |         println!("{:?}", data);
|                          ---- `data` is borrowed here
|
note: function requires argument type to outlive `'static`
--> src/main.rs:6:18
|
6 |     let handle = thread::spawn(|| {
|                  ^^^^^^^^^^^^^
help: to force the closure to take ownership of `data`, use the `move` keyword
|
6 |     let handle = thread::spawn(move || {
|                                ++++

The compiler literally tells the AI: “Add move here, Claude Code or Codex can parse it, apply the fix and move on - no guesswork, no hoping for the best, no Runtime - Data Races that crash your production system at 3 AM.

This is fundamentally different from what occurs in Python or JavaScript: When an AI produces buggy concurrent code in those languages, you might not even know there is a problem until you hit a race condition under specific load conditions; with Rust, the bug never makes it past the compiler.

Why Rust is perfect for unsupervised AI coding

I came across an interesting observation from Julian Schrittwieser at Anthropic, who put it perfectly:

Rust is great for Claude Code to work unsupervised on larger tasks. The combination of a powerful type system with strong security checks acts like an expert code reviewer, automatically rejecting incorrect edits and preventing bugs.

This matches our experience at Sayna where we built our entire voice processing infrastructure in Rust. When Claude Code or any AI tool changes, the compiler immediately tells it what went wrong, there are no waiting for runtime errors, no debugging sessions to figure out why the audio stream randomly crashes, the errors are clear and actionable

Here’s what a typical workflow looks like:

# AI generates code
cargo check

# Compiler output:
error[E0502]: cannot borrow `x` as mutable because it is also borrowed as immutable
--> src/main.rs:4:5
|
3 |     let r1 = &x;
|              -- immutable borrow occurs here
4 |     let r2 = &mut x;
|              ^^^^^^ mutable borrow occurs here
5 |     println!("{}, {}", r1, r2);
|                        -- immutable borrow later used here

# AI sees this, understands the borrowing conflict, restructures the code
# AI makes changes

cargo check
# No errors, we're good

The beauty here is that every single error has a unique code (E0502 in this case) If you run rustc –explain E0502, you get a full explanation with examples. AI tools can use this to understand not only what went wrong but also why Rust’s ownership model prevents this pattern, because the compiler essentially teaches the AI as it codes.

The margin for error becomes extremely small when the compiler provides structured, deterministic feedback that the AI can parse and act on.

Compare this to what you get from a C++ compiler if something goes wrong with templates:

error: no matching function for call to 'std::vector<std::basic_string<char>>::push_back(int)'
vector<string> v; v.push_back(42);
^

Sure, it tells you that there’s a type mismatch, BUT imagine if this error was buried in a 500-line template backtrace and you can find an AI to parse that accurately.

Rust’s error messages are designed to be human-readable, which accidentally makes them perfect for AI consumption: each error contains the exact source location with line and column numbers, an explanation of which rule was violated, suggestions for how to fix it (when possible) and links to detailed documentation.

When Claude Code or Codex runs Cargo Check it receives a structured error on which it can directly act. The feedback loop is measured in seconds, not debugging sessions.

Setting up your rust project for AI-coding

One thing that made our development workflow significantly better at Sayna was investing in a correct CLAUDE. md file, which is essentially a guideline document that lives in your repository and gives AI coding tools context about your project structure, conventions and best practices.

Specifically for Rust projects you want to include:

1. Cargo Workspace Structure - How your crates are organized
2. Error handling patterns - Do you use anyhow, thiserror or custom error types?
3. Async Runtime - Are you on tokio, async-std or something else?
4. Testing conventions - Integration tests location, mocking patterns
5. Memory management guidelines - When to use Arc, Rc or plain references.

The combination of Rust’s strict compiler with well-documented project guidelines creates an environment where AI tools can operate with high confidence; they know the rules and the compiler enforces them.

Real examples from production

At Sayna—WebSocket - handling, audio processing pipelines, real-time STT/TTS - provider abstraction we use Rust for all the heavy lifting These are exactly the kind of systems where memory safety and concurrency guarantee matters

When Claude code refactors our WebSocket message handlers, it can’t eat it in an accidental way, when it changes our audio buffer management, it can’t create a use-after-free bug because the language simply does not allow it.

// The compiler ensures this audio buffer handling is safe
pub async fn process_audio_chunk(&self, chunk: Bytes) -> Result<()> {
let processor = self.processor.lock().await;
processor.feed(chunk)?;

while let Some(result) = processor.next_result().await {
self.tx.send(result).await?;
}

Ok(())
}

An AI tool might need several iterations to get the borrowing and lifetimes right, BUT each iteration is guided by specific compiler errors: no guessing, no hoping for the best.

Codex Going Rust is Not a Coincidence

OpenAI recently rewrote their codex CLI entirely in Rust, it wasn’t just about performance - though that was definitely a factor - they explicitly mentioned that Rust eliminates entire classes of bugs at compile time - if OpenAI is betting on Rust for their own AI - coding infrastructure - it tells you something about where this is headed.

The security implications are also massive, codex now runs in sandboxed environments using Rust safety guarantees combined with OS isolation (Landlock on Linux, Sandbox-exec on macOS), when you have AI-generated code running on your machine, having compile-time security guarantees is not optional.

The learning curve trade-off

I won’t pretend that Rust is easy to learn because the ownership model takes time to internalize and lifetimes can be frustrating when you are starting out—AI - Coding tools are actually quite good at dealing with Rust’s sharp edges.

My favorite trick is to tell Claude Code to “fix the lifetimes” and let it figure out which combination of and, ref, as_ref() and explicit lifetime annotations make my code compile while I concentrate on the actual logic and architecture.

// Before: Claude fix this
fn process(&self, data: Vec<String>) -> &str {
&data[0]  // Won't compile - returning reference to local data
}

// After: Claude's solution
fn process(&self, data: &[String]) -> &str {
&data[0]  // Works - borrowing from input parameter
}

This is actually a better way to learn Rust than struggling alone through compiler errors: you see patterns, you understand why certain approaches work and the AI explains its reasoning when you ask.

Making AI-coding work for your team

If you’re considering using Claude Code or Codex for Rust development, here’s what I’d recommend:

Invest in your CLAUDE. md - Document your patterns, conventions and architectural decisions The AI will follow them. 1.

Use cargo clippy aggressively - enable all lints. More feedback means better AI output. 1.

CI with strict checks - Make sure that Cargo test, Cargo clippy and Cargo fmt are running on every change; AI tools can verify their work before you even look it up. 1.

Start with well defined tasks - Rust’s type system shines when the boundaries are clear: define your traits and types first then let AI implement the logic. 1.

Verify but trust - The compiler catches a lot, BUT not everything: Logic errors still slip through: code review is still essential.

The Future of AI-Assisted Systems Programming

We’re at an interesting inflection point: rust is growing quickly in systems programming and AI coding tools are actually becoming useful for production work: the combination creates something more than the sum of its parts.

At Sayna, our voice processing infrastructure handles real-time audio streams, multiple provider integrations and complex state management: all built in Rust, with significant AI assistance: means we can move faster without constantly worry over memory bugs or race conditions.

If you’ve already tried Rust and found the learning curve too steep, give it another try with Claude Code or Codex as your pair programmer. The experience is different when you have an AI that can navigate ownership and borrowing patterns while you focus on building things.

The tools finally catching up to the promise of the language