← All drillsBuild a Disciplined Coding Agent
What you'll be able to do- ✓Build a plan-gate and a critique-gate for a coding agent, and wire them into one toggleable loop
- ✓Write a plan rubric that rejects confident-sounding filler and only passes a plan that names real edge cases
- ✓Build a critique step that reviews a draft without ever peeking at the hidden test oracle
- ✓Measure what discipline actually costs and buys — pass rate, first-pass rate, iterations, wasted edits — on a fixed task suite, gates on vs. off
- ✓Run the loop against a real local model and diagnose a failure mode a scripted model can never show you
- ✓Package the working loop as a reusable Claude Code skill
Build the habit behind every reliable coding agent — plan before you write, critique before you ship — and prove with real numbers what it costs and buys, not just claim it helps.
⌁ The discipline behind Claude Code's plan mode, Cognition's Devin, Cursor, and GitHub Copilot Workspace — all publicly wrestling with the same reliability problem this course builds from scratch.
Start this internshipCreate an account to unlock the 10 sections, the workbench, and AskThili.
BeginSections
1. Build a Disciplined Coding Agent
🔒 locked2. Lesson 1 - The Efficient Agentic-Coding Playbook
🔒 locked3. Lesson 2 - The Undisciplined Baseline
🔒 locked4. Lesson 3 - The Planning Gate
🔒 locked5. Lesson 4 - Grading a Plan
🔒 locked6. Lesson 5 - The Critique Gate
🔒 locked7. Lesson 6 - Closing the Loop
🔒 locked8. Lesson 7 - Measuring the Discipline
🔒 locked9. Lesson 8 - A Real Model, Not a Script
🔒 locked10. Lesson 9 - Packaging It as a Skill
🔒 lockedDig deeper
🔗thilidiscipline — the reference implementation you build in this course
code📄Reflexion: Language Agents with Verbal Reinforcement Learning (Shinn et al., 2023)
paper📄Self-Refine: Iterative Refinement with Self-Feedback (Madaan et al., 2023)
paper🔗Ollama — run open LLMs locally
docs