AI systems that accomplish goals with limited supervision: autonomy, tool use, and orchestration. The shift from models that respond once to agents that loop until the goal is done.
System design rounds for AI Engineer and ML platform roles. "Design an autonomous coding assistant" or "Build an agentic customer support system" — you need to explain the loop, tool design, memory, and guardrails.
Common questions:
Strong answer: Mentions the ReAct pattern by name. Classifies tools by reversibility. Proposes audit trails and explicit error contracts for multi-agent systems. Knows that tool description quality drives agent decision quality.
Red flags: Calls any LLM with a function call "agentic". Cannot explain what happens when a tool fails. Has no answer for how to prevent the agent from taking unintended actions. Thinks bigger context windows solve memory problems.
Key takeaways
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