Clawdemy Lessons

Overview of lesson 2: the task, instructions, context anatomy, why the same question gets different answers twice, two upgrades, and personas.

Overview of lesson 3: system prompts that persist, retrieval from your own documents, who tailors an assistant, and machines that see and speak.

Overview of lesson 4: the two filters for handing a task to AI, four signs of task fit, four consequence questions, and a verdict for your own task.

Overview of lesson 1: the prediction machine idea, the 2022 turning point, three breaks from older AI, and the two questions this track answers.

Overview of lesson 7: headless Claude Code in CI, machine readable output, inherited project config, and the independent reviewer pattern.

Overview of lesson 4: tool descriptions that drive selection, actionable error design, least-privilege distribution, and building an MCP server.

Overview of lesson 1: the four parts of every agentic system, the workflow versus agent decision, and the three trade-offs architects weigh.

Overview of lesson 6: escalation criteria that hold up, human review budgeted by segment, independent review, and multi-pass checking.

Overview of lesson 8: the capstone assignment, its three phases, the ten-point rubric, and why the written defense is the real deliverable.

Overview of lesson 2: the CLAUDE.md hierarchy, path-scoped rules files, shared slash commands, plan mode as a risk call, and diagnosing config drift.

Overview of lesson 5: coordinator and subagent design, decomposition judgment, parallel delegation, structured failure, and provenance.

Overview of lesson 3: guaranteed output shape with tool schemas, nullable fields for honest absence, and validation loops with retry judgment.

Zoom out from the agents to the wiring: the orchestration that decides who runs next, and the shared state that carries each agent's work to the one after it.

The last loop: the system records each decision at once, then on a later run, once the outcome is known, reflects on the call and feeds the lesson back.

The final lesson: watch the full multi-agent pipeline run end to end, map each report to its agent, and run the open-source system in a safe simulation.