Understand the shift from AI as a feature to AI as an actor: what makes a system genuinely agentic, and why it changes the product paradigm

Build practical literacy in agent anatomy: how LLMs, memory, tools, retrieval, and APIs fit together

Apply decision frameworks for scoping agent capabilities, designing constraints, and determining where human-in-the-loop or human-on-the-loop oversight is essential

Design for reliability and failure: guardrails, fallback mechanisms, hallucination management, and what it means to trust an agent with real actions

Establish an evaluation and observability mindset from day one: how to know whether your agent is working, and how to debug it when it isn't

Recognise the moment when a single agent is no longer enough and what questions to ask before adding more