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Core
Agent Design Patterns: ReAct, Plan-and-Execute, Reflection
These are the named control-flow architectures for LLM agents: ReAct interleaves reasoning and actions in a tight loop, plan-and-execute decomposes the task up front and then runs the steps, and reflection adds a self-critique pass that revises output. Each trades latency, token cost, and robustness differently. Applied AI interviews probe this to see whether you pick a pattern from task structure rather than defaulting to one loop for everything.
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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.
Agent Engineering·What an agent is, and when not to build oneFree14mWhat an agent is, and the question to ask before you build oneAgents are the most over-applied pattern in applied AI. This lesson gives you the spectrum from fixed workflow to autonomous loop, the honest cost of each step along it, and the decision rule the rest of this course is built on: use the least autonomy that solves the task.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.Agent Engineering·The single-agent loopFree13mThinking between actions, and what it costsInterleaving a short reasoning step before each action is the most widely used agent pattern. This lesson covers what it actually buys, the three places it helps most, and the specific way it degrades over a long run.
RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
RAG & Agent System DesignWhat is the Plan-and-Execute agent pattern, and how does it compare to ReAct?→LLM & GenAI FundamentalsWhat is ReAct (Reasoning + Acting) prompting, and why does interleaving thought and action help?→RAG & Agent System DesignWhat are Tree-of-Thoughts and LATS, and when is search-based planning worth the cost?→System Design for AI in ProductionDesign a deep research agent that answers complex questions by searching and synthesizing many sources.→LLM & GenAI FundamentalsWhat is in-context learning, and why does chain-of-thought prompting improve results?→LLM & GenAI FundamentalsExplain self-consistency and tree-of-thought prompting. When are they worth the extra cost?→
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