‹ Learn agentic development

7. Exercises

In short

  • Six exercises, from writing a spec to running your own small team of agents.
  • Work on a fork of OpenLucid; every exercise has a clear ‘done when’.
On this page

Fork github.com/emolionl/openlucid, check out checkpoint-02, and get the backend tests running locally first (see backend/README.md).

1. Read before you build

Read backend/SPEC.md and answer: which principle decides what happens when user B asks for user A’s journal entry? Where in the code is it enforced? Which test proves it?

Done when: you can point to the principle, the line of code and the test.

2. Write a spec section

Feature: users can add tags to a night (for example stressed, new-moon, cue-night). Write the spec section: principles (if any), data model changes, endpoints, status codes, isolation and test rules. Don’t write code yet.

Done when: a classmate can implement it without asking you a question.

3. One agent, one feature

Give an agent your spec from exercise 2 and let it implement the tags feature, with tests. Then check its work as in lesson 5: rerun the tests yourself, read the queries that filter by user, check git status.

Done when: the full suite passes on SQLite and on PostgreSQL (scripts/pg_test.py), and you found at least one thing to improve in the agent’s work, or can explain why it’s correct.

4. Be the reviewer

Pick one lens from lesson 4 (security, isolation, contract, operations) and review the tags feature through that lens only. Write each finding with a concrete scenario as evidence.

Done when: you have at least one finding, or a written argument why the lens finds nothing.

5. Prove it with a failing test

For each finding from exercise 4: write a test that fails because of it. Then fix the code until the test passes. If you can’t make a test fail, the finding isn’t real; write down why.

Done when: every finding is either fixed with a test or rejected with a reason.

6. A small team

Write a workflow with three agents, modelled on examples/backend-workflow.js: one builds a feature, one reviews it through two lenses, one fixes the findings test-first. Use file ownership and a structured report for each agent.

Done when: the workflow runs end to end, and you can explain in two sentences why each agent needed the information it was given, and nothing more.

Reflection

Write half a page: where did the agent save you the most time, where did it mislead you, and which check caught it? That half page is worth more than the code.