RAID-AI: A Multi-Language Stress Test for Autonomous Agents

We’ve all seen the demos: an LLM generates a clean React component or a Python script in seconds. But in the real world, engineering isn't just about generation—it's about maintenance. It’s about diving into a 10-year-old Java repo, understanding the legacy context, and fixing a bug without breaking the entire build.
As part of my Mastery Tier submission for my current AI MOOC, I decided to tackle this problem head-on by building RAID-AI. It is a multi-language bug-fixing benchmark designed to evaluate "Green Agents" across Java, Python, and JavaScript.
The Problem: The Benchmarking Gap
Most AI benchmarks are "toy" problems. They exist in a vacuum. To truly test if an agent is ready for a production environment, it needs to face:
Multilinguality: Can it context-switch between the rigid types of Java and the dynamic nature of JS?
Environment Constraints: Can it handle real-world dependencies?
Efficiency: Is the agent solving the problem with minimal tokens, or is it "brute-forcing" the solution?
The Architecture: Under the Hood of RAID-AI
RAID-AI operates as an orchestration layer. It manages three distinct "Project Managers" (Java, Python, and JS) that interface with local bug repositories.
For the Java component, I integrated Defects4J, a database of thousands of real-world bugs. This wasn't a simple "plug-and-play" situation. To get the environment stable on WSL/Ubuntu, I had to navigate a "dependency minefield."
The Technical "War Story": Perl and Environment Parity
The biggest hurdle was achieving environment parity. Defects4J is built on a Perl-based backend, which led to the infamous String::Interpolate.pm error. I spent a significant portion of the development phase playing "dependency whack-a-mole," manually installing system-level libraries like libstring-interpolate-perl and liblist-moreutils-perl to ensure the benchmark could actually communicate with the Java projects.
This experience highlighted a critical truth in AI Engineering: Infrastructure is the ultimate bottleneck. If your testing environment isn't reproducible, your AI’s "success" is just a hallucination.
The Scoring Rubric: Why "Green" Matters
In RAID-AI, we don't just care about a "Pass" or "Fail." We use a weighted rubric to calculate the Green Agent Score:
Correctness (50%): Does it pass the original test suite?
Code Quality (20%): Is the fix maintainable or is it "spaghetti"?
Efficiency (15%): We track the time and token consumption. A fix that takes 10 minutes and 50k tokens is scored lower than a surgical 2-minute fix.
Minimal Change (15%): We penalize agents that rewrite entire files to fix a single-line logic error.
By enforcing a 600-second timeout per bug, RAID-AI forces agents to be decisive and computationally efficient.
Lessons from the Mastery Tier
Moving through this MOOC to the Mastery Tier has shifted my focus from "Prompt Engineering" to "System Design." My three biggest takeaways for fellow developers are:
Polyglot Agents are the Future: The next generation of engineers won't be "Python Developers"; they will be "System Orchestrators."
Adversarial Testing: You have to try and break your benchmark before you let an agent near it.
The Importance of Reproducibility: Automated bug-fixing only works if the "Check-out -> Fix -> Test" loop is atomic and indestructible.
Join the Project
RAID-AI is currently initialized with 64 high-priority bugs (17 Java, 17 Python, 30 JS), and this is only the beginning. If you're interested in building autonomous systems that actually work in the real world, I highly recommend checking out the curriculum that guided this build.
👉 Check out the MOOC here: https://agenticai-learning.org/f25
What are you building to test the limits of your agents? Let's discuss in the comments below.