Learning roadmap
Choose a goal, then follow each stage as an ordered, explained outline.
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Build a durable mental model, then transfer it into a working application.
Stage purpose: Establish the vocabulary and first working mental model.
Required background: Complete the linked prerequisites below or bring equivalent experience.
Expected outcome: Explain the core idea and use it in a small example.
Estimated time: 40 minutes
Prerequisites: No prerequisite topics
Evidence: Practise the outcome in code and record what you learned.
Current status: Not started
Recommended next: The Supervisor Orchestrator — Add a supervisor that routes ambiguous work, audits delegation, and owns the system-wide finish condition.
Stage purpose: Connect the concepts into a repeatable problem-solving model.
Required background: Complete the linked prerequisites below or bring equivalent experience.
Expected outcome: Apply the model to a representative exercise without copying a recipe.
Estimated time: 35 minutes
Prerequisites: Multi-Agent Topologies
Evidence: Practise the outcome in code and record what you learned.
Current status: Not started
Recommended next: Complete this path by applying these outcomes in your own project.
Prioritise high-frequency material, retrieve it quickly, and practise explaining it clearly.
Stage purpose: Secure the concepts that recur in most interviews.
Required background: Complete the linked prerequisites below or bring equivalent experience.
Expected outcome: Give a concise, accurate explanation with one concrete example.
Estimated time: 15 minutes
Prerequisites: No prerequisite topics
Evidence: Answer aloud, give a concrete example, and identify the relevant trade-off.
Current status: Weak
Recommended next: The Supervisor Orchestrator — Compare static routing with a semantic supervisor and defend the loop-safety trade-offs.
Stage purpose: Rehearse implementation choices that invite common follow-up questions.
Required background: Complete the linked prerequisites below or bring equivalent experience.
Expected outcome: Walk through a realistic API decision and defend the trade-offs.
Estimated time: 12 minutes
Prerequisites: Multi-Agent Topologies
Evidence: Answer aloud, give a concrete example, and identify the relevant trade-off.
Current status: Weak
Recommended next: Complete this path by applying these outcomes in your own project.