The Self-Improving AI Agents: From Scaling Laws to Agentic Systems (Video Course) certification digs into a fascinating idea: ask a hard problem once and a model might fail, but give it ten thousand tries and it can succeed. This course teaches you how inference-time scaling and self-improvement loops turn latent model knowledge into working systems, giving you a serious edge in productivity, decision-making, and future-proofing your career. If you want to build AI agents that actually learn and improve, this is the certification to grab.

This certification covers the following topics:

  • Inference-time scaling and test-time compute
  • Scaling laws and their implications for agent design
  • Self-improvement loops and iterative refinement
  • Search and sampling strategies for hard problems
  • Reward modeling and feedback signals
  • Designing agentic systems with LLMs
  • Evaluation and benchmarking for self-improving agents
  • Common failure modes and how to avoid them
  • Building autonomous workflows and pipelines
  • Real-world case studies from Stanford CS329A