In the chaotic world of AI agents, even the self-proclaimed "worst coder" can take the plunge. This is the story of a coding newbie who decided to build an agentic system for work—specifically, an AI that cracks leaderboards. Along the way, they faced steep learning curves, surprising rewards, and a few hard-won lessons in programming. Dive into this Q&A to explore their journey, from fumbling with basic concepts to deploying a functional agent.
What does 'agentic' mean in coding, and why did you choose this approach?
Agentic refers to building autonomous software agents that can take actions, make decisions, and adapt to environments without constant human input. I chose this because my task—cracking a leaderboard—demanded a system that could learn patterns, execute repetitive tasks, and self-correct. As a newbie, it felt like jumping into the deep end, but the promise of a hands-free solution motivated me. I wanted to see if agentic design could compensate for my limited coding skills by offloading complex logic to the AI.
What was the biggest coding challenge you faced?
The most daunting hurdle was handling error handling and state management. My agent had to navigate a live leaderboard API that changed frequently. Without robust error recovery, it would crash on the first unexpected response. I spent days debugging try-except blocks and implementing fallback strategies. Another challenge was context retention—ensuring the agent remembered previous actions without burning through memory. I learned to leverage small databases and caching, which felt like real engineering.
How did you design the leaderboard-cracking logic?
I broke the problem into steps:
- Data ingestion: Scrape leaderboard scores periodically.
- Pattern analysis: Use simple statistical methods (moving averages) to detect scoring trends.
- Action planning: The agent decides the optimal time to submit higher scores based on trends.
- Execution: Automate submissions via API calls.
This modular approach let me test each piece independently. I even added a sleep timer to avoid rate limits—a rookie mistake I learned early.
What did you learn about AI agents from building this?
The biggest lesson: agents are only as smart as their feedback loops. My initial version acted blindly, repeating failures. After adding performance metrics and a simple reward system (like tracking win rate), the agent improved autonomously. I also discovered the importance of logging—without it, debugging agent behavior was like guessing in the dark. Finally, I realized that even a "worst coder" can build functional agents by starting small and iterating fast.
Did the AI actually crack the leaderboard? What was the outcome?
Surprisingly, yes—kind of. The agent moved me from bottom 10% to top 25% on the leaderboard over two weeks. It couldn't beat top players (they had better strategies), but it consistently improved my rank. The real win wasn't the ranking, though. It was proving that agentic approaches lower the barrier for non-experts. I automated what would have taken me hours manually, and I learned real coding concepts like exception handling and state persistence along the way.
What advice would you give to other newbies wanting to build agents?
Three things: start with a clear goal, embrace failure, and use pre-built tools. Pick a tiny problem—like automating a simple task on a website. Expect your first agent to break, and learn from each crash. Also, leverage libraries like OpenAIʼs API or LangChain to skip low-level coding. Most importantly, donʼt let the "worst coder" label hold you back. Agentic development rewards creativity over pure programming skill. Just build and iterate—the rest follows.