Teaching Agents by Example Not Code

The best way to teach a human is through demonstration and practice. So why do we teach AI agents through code and configuration files? It's time to rethink how we train AI agents. The Code-First Problem Traditional agent development looks like this: Write detailed instructions Define API endpoints Specify selectors and element IDs Handle error cases in code Debug when things break This approach treats agents like traditional software—something you build once and deploy. But agents need to learn, adapt, and evolve. They need training, not just programming. Learning by Example What if we taught agents the same way we teach people? Show them what to do Let them observe the patterns Give them opportunities to practice Provide feedback on their performance This is learning by example , and it's how SkillForge approaches agent training. The SKILL.md Approach Instead of writing code, you record yourself performing a task. The AI watches, learns, and extracts the essential patterns: # Process