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Agentic AI

Planning loops, tool use, memory architecture, multi-agent coordination, MCP, and evaluation — the questions AI platform and applied-AI teams actually ask.

14 questions

Agentic AIJunior1 min

What is the ReAct loop, and why do agents use it instead of generating a final answer in one shot?

planningtool-use

✦What a strong answer covers

1
How the ReAct loop worksInterleaves Reasoning, Action, and Observation steps so the model thinks before each tool call and updates its plan based on real output, not hallucinated continuations.
2
Why it beats one-shot generationHandles uncertainty and tool failures mid-task. Each observation can correct the model's plan before committing to the next step.
3
Real-world examplesSearch then code execution then data analysis pipelines where each step depends on the prior result. Coding agents that write, run, and fix based on test output.
4
When NOT to use itDeterministic, well-defined tasks don't need a loop. Adding one introduces latency, token cost, and non-determinism with no benefit.

▣ Model answer

ReAct stands for Reasoning and Acting. Instead of asking the model to produce a final answer immediately, you interleave a Thought step — where the model narrates what it believes and what it needs next — with an Action step that calls a tool, followed by an Observation that feeds the tool result back in. This cycle repeats until the model decides it has enough information to g…

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Interview Q&A

Agentic AI

What is the ReAct loop, and why do agents use it instead of generating a final answer in one shot?

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When should you use an agent instead of a deterministic workflow, and when is an agent the wrong tool?

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What is the ReAct loop, and why do agents use it instead of generating a final answer in one shot?

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