Agent Architectures and the Agentic Loop
ReAct, plan-act-observe-reflect, termination and budget controls, and the interview's favorite question: when should you not build an agent at all
A precise, vendor-neutral treatment of the agentic loop for AI engineering interviews: the observe-decide-act loop itself, the ReAct pattern and why explicit reasoning traces help tool selection, the plan-act-observe-reflect variant and when its extra LLM calls are worth it, single-agent vs multi-agent at a glance, termination and budget controls, failure modes with mitigations (loops, tool hallucination, runaway cost, context poisoning, analysis paralysis), a worked compounding-error-rate calculation, and a worked workflow-vs-agent decision.
Practice questions (5)
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Workflow or Agent? Deciding for an Expense-Report Reviewer
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ReAct vs. Plan-Act-Observe-Reflect for a Research Agent
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Compounding Error Rates in a Long-Running Data-Cleaning Agent
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Diagnosing a Runaway Agent from an Incident Report
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Resisting Multi-Agent for a Document-Summarization Feature
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