Description
How to design the full anatomy and lifecycle of an agent so you will learn to build systems where model, memory, tools, and prompts behave as one coherent unit instead of a pile of ad hoc scripts
How to choose, integrate, and optimize foundation models so you will learn to balance quality, latency, and cost in agents that must operate reliably at scale
How to engineer system prompts and dynamic prompts for agents so you will learn to create stable cognitive contracts that survive changing tasks, users, and contexts
How to wire memory, tools, and frameworks such as LangChain, n8n, Autogen, and CrewAI so you will learn to connect agents to real APIs and workflows without losing control
How to apply reasoning and planning patterns such as ReAct, plan and execute, and sub agents so you will learn to handle complex, multi step tasks with transparency instead of guesswork
How to define autonomy levels, guardrails, and human in the loop supervision so you will learn to increase agent autonomy safely while keeping security, compliance, and stakeholders on your side
How to measure, test, and improve agents using metrics, AI evaluators, simulations, and the companion GitHub repo so you will learn to prove ROI and iterate without breaking production
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