Large language models used to just write code. Now they run it. That small change-letting AI agents execute the code they generate-is turning them from helpful assistants into active problem-solvers. If you’ve ever watched an AI suggest a Python script to sort data or fix a bug, only to have you copy-paste it into your terminal, you’ve seen the old way. Today, the best LLM agents don’t just suggest-they test, debug, and run code on their own. This isn’t science fiction. It’s happening right now in tools like GitHub Copilot, Amazon CodeWhisperer, and Google Codey.
Why Code Execution Changes Everything
Before code execution, LLMs were guessers. They’d write a function to calculate compound interest, but you had to check if it worked. Did it handle leap years? Did it round correctly? You had to run it yourself. Now, agents can generate that same function, run it in a secure environment, and return the result-no human needed. This cuts out the middleman: no more manual testing, no more guessing if the code is broken. The difference shows up in real results. GitHub reports a 41% drop in code errors when their Copilot Workspaces use execution validation instead of just code suggestions. That’s not a small win-it’s the difference between shipping buggy software and shipping reliable updates. In finance, healthcare, or logistics, where a single decimal error can cost thousands, that kind of accuracy matters.How It Actually Works: The Three-Layer System
You might think an AI just types code and hits Enter. It’s not that simple. Every code-executing agent runs on a strict three-layer system:- The LLM Core: This is the brain-GPT-4 Turbo, Claude 3 Opus, or Gemini 1.5 Pro. It understands the problem and writes the code.
- The Code Validation Layer: Before the code even runs, this layer scans it. It blocks dangerous patterns like
os.system(),subprocess.Popen(), or network calls to unknown domains. It’s like a bouncer checking IDs before letting someone into a club. - The Secure Execution Environment: This is where the code actually runs. It’s not your laptop. It’s a locked-down, temporary container with only 2GB of RAM and one virtual CPU. Execution time is capped at 30 seconds. No access to your files. No internet unless explicitly allowed. This is the safety net.
Security: The Biggest Risk and How Platforms Handle It
The biggest fear isn’t that AI will write bad code. It’s that it will write code that breaks out of its cage. Every major platform tackles this differently:- GitHub Copilot uses Firecracker microVMs-tiny, isolated virtual machines that lock down the OS. Their proprietary system, called CodeSpaces Secure Execution, blocks 92% of known injection attacks after their December 2024 "Code Execution Shield" update.
- Amazon CodeWhisperer runs code inside AWS Lambda with extreme limits: only 128MB memory and 15 seconds of runtime. It’s designed to die quickly if something goes wrong.
- Google Codey uses gVisor containers with seccomp filters that shut down 317 out of 339 Linux system calls. That means even if code tries to access hardware or network interfaces, it’s blocked at the kernel level.
subprocess.Popen(). That’s not a bug-it’s a flaw in the assumption that LLMs can’t be tricked into writing code that exploits edge cases.
Real-World Problems: What Code Execution Can and Can’t Do
Code execution lets agents do things they couldn’t before:- Automatically test if a sorting algorithm works on edge cases
- Run simulations to predict server load under traffic spikes
- Debug a failing API endpoint by stepping through the code line by line
- Generate SQL queries and immediately verify they return the right data
- No persistent storage: Every execution starts fresh. You can’t save files or cache results between runs.
- No external APIs: Unless you explicitly grant permission, the agent can’t call your Stripe, Slack, or database APIs.
- No heavy lifting: Training a model, rendering video, or processing large datasets? Too slow. Too expensive. The sandbox won’t allow it.
- Environment mismatch: The sandbox runs Python 3.11. Your production server runs 3.12. A library works in one, fails in the other. This trips up 37% of users.
Who’s Using This and Why
Fortune 500 companies are adopting code-executing agents fast. According to Forrester, 57% of them now use some form of it-up from 22% just a year ago. Why? - Dev teams: They’re cutting debugging time by 30-35%. One JPMorgan developer said Copilot’s execution feature saved him 15 hours a week. - Startups: Small teams can’t afford QA engineers. Code execution acts as a junior tester, catching bugs before they reach users. - Researchers: Running simulations or analyzing datasets? Agents generate the code, run it, and return graphs or stats-no manual scripting needed. But adoption isn’t smooth. Many teams hit roadblocks:- Security teams block it entirely because of "untrusted code" fears.
- Developers get frustrated when legitimate code is blocked by false positives. GitHub has 87 open issues on this-32% of all code execution complaints.
- Training engineers to use it properly takes time. You can’t just turn it on and expect magic.
The Hidden Cost: Security Engineering
Implementing code execution isn’t just about buying a tool. It’s about building a wall. AWS’s whitepaper says companies need 8-12 weeks of dedicated security engineering to set it up right. Why so long?- Sandbox configuration: 32% of effort. Getting the right memory limits, timeouts, and blocked system calls takes trial and error.
- Output validation: 28%. You need rules to catch malicious outputs-like code that tries to exfiltrate data or call external services.
- Integration testing: 24%. Making sure the agent works with your CI/CD pipeline, code reviews, and deployment tools.
The Future: Where This Is Headed
The market is exploding. AI code assistants hit $2.8 billion in 2024 and are projected to hit $9.3 billion by 2027. GitHub Copilot leads with 38% market share, followed by CodeWhisperer at 29% and Codey at 22%. But the biggest warning comes from Gartner: by 2026, 70% of enterprises will use code-executing agents-but only 35% will have adequate security. That’s a recipe for breaches. New tools are emerging to help. NVIDIA’s CUDA-accelerated validation cuts code checking time by 63% for GPU-heavy tasks. OWASP is finalizing version 2.0 of its LLM Top 10 list, with expanded rules on code execution risks. The EU AI Act now requires formal risk assessments for any AI that generates and runs code. The long-term question isn’t whether code execution will become standard-it already is. The real question is: can we build systems that are secure enough to trust? MIT’s CSAIL lab says we need fundamental architectural changes. Others say we’re just in the early days of a new era. One thing’s clear: if you’re writing code in 2025, you’re not just working with a tool that suggests. You’re working with an agent that runs. And that changes everything.Can LLM agents execute code on my local machine?
No. All major platforms run code in isolated, cloud-based sandboxes. They don’t have access to your files, network, or system. This is intentional for security. Even if you’re using an IDE plugin like GitHub Copilot in VS Code, the code executes on remote servers-not your computer.
Is code execution safer than manual code review?
It’s not safer-it’s different. Manual review catches logic errors and design flaws. Code execution catches runtime errors: syntax bugs, infinite loops, wrong outputs. Together, they’re stronger. But execution alone won’t catch poor architecture or bad data handling. You still need human oversight.
Why does code execution add so much latency?
It’s not the code running that’s slow-it’s the safety checks. Before execution, the system must validate the code, spin up a secure container, load dependencies, run the code, capture output, and shut everything down. That entire process takes 450-600ms. In a fast-moving environment, that’s noticeable. But for debugging or testing, the trade-off is worth it.
Can I use code execution for production deployments?
Not directly. The sandboxed environment is designed for testing, not deployment. You can’t install custom packages, access internal databases, or run long-running services. Code execution helps you write better code faster-but you still need to deploy it through your normal CI/CD pipeline.
What happens if an LLM generates malicious code?
The sandbox blocks most of it. But some attacks, like indirect prompt injection, can slip through. For example, if a hacker embeds malicious instructions in a data file the LLM reads, it might generate harmful code. That’s why output validation and input filtering are critical. No system is 100% foolproof-security is layered, not absolute.
Kate Tran
December 13, 2025 AT 14:45so like... i just let copilot run my code now? no joke, i used to check every line like my grandma checks the oven. now i just stare at the screen waiting for it to magic itself into working. kinda scary but also kinda amazing.
also why does it take half a second to do a simple math thing? my toaster is faster.
amber hopman
December 14, 2025 AT 19:31i love how this is basically giving junior devs superpowers. my team’s QA engineer just quit last month-copilot’s execution feature caught 12 bugs in one sprint that we’d have missed. not perfect, but way better than nothing.
the sandbox limits are kinda annoying though. i had to rewrite a script because it needed 3GB of RAM to process a CSV. guess i’m going back to my local machine for heavy lifting. still, 90% of the time? it’s a game changer.
Jim Sonntag
December 14, 2025 AT 23:15oh wow so now ai is our new intern who can’t touch the coffee machine but can debug our entire backend? brilliant.
you know what’s wild? we pay $40 a month so our AI doesn’t delete our production database. that’s not innovation, that’s just capitalism with a firewall.
also, the 600ms delay? that’s the sound of my productivity crying in the corner. but hey, at least it’s not running rm -rf / on my laptop. progress?
Deepak Sungra
December 16, 2025 AT 20:25bro this whole thing is just a glorified autocomplete with a panic button.
my buddy tried using codey to fix his api and it blocked his own script because it "looked suspicious"-turns out he was calling a local dev server. 3 hours wasted. meanwhile, i just paste into terminal and go.
and don’t get me started on the "security engineering" bs. 12 weeks to set up a sandbox? my 12-year-old cousin could do that in a weekend. they’re just selling fear.
also why is everyone acting like this is new? we’ve had jupyter notebooks for years. chill out.
but honestly? if it saves me 10 hours a week? i’ll pay the $28.50. whatever.
also why is the internet so loud about this? it’s just code. stop hyping it up.
also why is this even a post? it’s like writing an essay about a toaster that now toasts better.
also i’m bored now. bye.