A working agent loop does not tell you whether failures come from the harness, the evaluation, or the model. The useful lens here is to study those three layers together: implementation choices shape the trajectories you measure and ultimately train on.
What Sets It Apart
- Three individual assignments cover harness construction, evaluation design, and training, giving each layer a concrete implementation target.
- Tool use, context management, memory, and planning connect to coding, GUI, and deep-research agents, helping separate general design principles from domain-specific choices.
- Supervised fine-tuning, RL algorithms, and training systems sit alongside sandboxing and monitoring, encouraging readers to assess capability gains together with operational risks.
- A half-semester project in teams of 2–4 turns the implementation foundation into an open research question.
Who Should Take It
Great fit if you have trained neural language models and want to understand where to intervene when an agent fails. Public slides, readings, recordings, and assignment repositories support self-study. Look elsewhere if you need an introductory programming course or a quick framework walkthrough; the stated prerequisites include neural language-model training, and the RL assignment requires a training backend.
