We don't talk enough about the best part of AI agents (michalkotowski.pl)

This piece argues that the most underrated feature of AI agents is their role as endlessly patient, non‑judgmental tutors. Through a personal story of struggling with formal schooling but thriving when allowed to learn by doing, the author shows how LLM-based agents replicate the ideal study partner: they answer imperfect questions, break big problems into manageable pieces, and let curiosity—not grades or ego—drive learning. Rather than being another flashy capability, agents restore a supportive feedback loop that many learners never had. Technically, agents enable top‑down, iterative decomposition: start with a high‑level goal, have the agent outline subsystems, then drill into components (the author likens this to reversing “atomic design”). That scaffolding—context-aware guidance, incremental problem solving, and just‑in‑time explanations—lowers the barrier to complex domains and accelerates skill acquisition. For the AI/ML community this matters: agents aren’t just productivity toys or automation engines, they’re a scalable education and mentorship platform that can democratize expertise, shape developer onboarding, and change how people acquire and apply technical knowledge.