Building and Optimizing a Competitive AI Coding Agent

Leonardo Weber — Fri, 16 Oct 2026 16:56:04 +0000

I developed an autonomous coding agent designed to solve challenging programming tasks in a competitive environment. Rather than relying on an LLM to generate code in a single pass, I built a complete workflow combining repository analysis, planning, implementation, execution, testing, and iterative refinement.

The agent’s workflow:

Task → Analyze → Plan → Implement → Execute → Test → Diagnose → Refine → Retest → Submit

The feedback loop is where the real improvement happens. When a test fails, the agent analyzes the error, identifies the likely cause, updates the code, and reruns the relevant tests. This helps catch logic errors and edge cases before submission instead of accepting the first plausible-looking solution.

Performance comparison

I evaluate the agent with and without iterative feedback using the same benchmark tasks and execution limits:

Task success rate: [baseline]% → [optimized]%

Tests passed per task: [baseline] → [optimized]

Average completion time: [baseline] → [optimized]

Compute cost per successful task: [baseline] → [optimized]

These measurements help show whether each optimization improves correctness and reliability without introducing excessive latency or compute overhead.

Beyond the feedback loop, I optimize model selection, prompt design, repository context, tool usage, inference settings, and failure recovery. The goal is to build an agent that doesn't just generate code, but uses execution results as evidence to improve its solutions systematically.

DEV Community: Leonardo Weber

Building and Optimizing a Competitive AI Coding Agent