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Core
Context Engineering for Agents
Context engineering is the discipline of designing the full information payload that goes into an agent's context window each turn: system instructions, memory, retrieved data, tool definitions and results, and conversation history. Most agent failures are context failures, where the right information is absent, buried, stale, or crowding out the rest of the budget. Applied AI interviews probe it because it is the highest-leverage lever on agent reliability and cost, and it separates people who tune prompts from people who manage state.
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TAUGHT IN THESE COURSES
This concept is a reference. These lessons put it in sequence, with what comes before and after it.
Applied AI Engineering·What this job actually isFree12mA map of the stack, and where the hard parts actually areEvery system in this course is the same seven pieces in different arrangements. This lesson lays them out, shows which ones consume your time in practice, and gives you a mental frame to hang the next six modules on.Applied AI Engineering·Prompting as an engineering disciplineFree13mWhat a prompt is actually made ofA prompt is not a sentence you write once. It is an assembled document with parts that do different jobs, and knowing which part carries which job is the difference between fixing a problem and rewording it.Agent Engineering·The single-agent loopFree14mWhat the loop actually carries between stepsEvery agent pattern is a variation on one loop, and the interesting part is not the control flow but what accumulates in the message list. Understanding that accumulation explains the cost curve, the drift, and most of what goes wrong later.
RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
RAG & Agent System DesignWhat is context engineering, and why is it considered more important than prompt engineering for agents?→RAG & Agent System DesignYour agent's token bill is exploding. How do you control the cost of a multi-step agent?→RAG & Agent System DesignYour agent has forty tools available and picks the wrong one. How do you fix it?→RAG & Agent System DesignWhen do you build an agent instead of a single LLM call, and how do you keep a multi-step agent reliable?→RAG & Agent System DesignWhen do you use a multi-agent system, and what orchestration patterns and pitfalls matter?→System Design for AI in ProductionDesign a deep research agent that answers complex questions by searching and synthesizing many sources.→
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