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Neuroanatomy Explorer

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Memory Deck

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The Agent Loop in 3D

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Planning, Reasoning, and Reflection

Manual: General · Subject: Agentic AI

Understand how agents think through steps, check their work, and improve decisions.

Thinking before, during, and after action

Planning

Planning means deciding a sequence of steps before acting. In agentic systems, planning can be explicit, where the system writes a checklist, or implicit, where the system chooses one action at a time while still following a strategy.

Reasoning and reflection

Reasoning is the process of using information to make a decision. Reflection is checking whether the result makes sense after acting. Reflection helps an agent notice mistakes, gaps, or better alternatives.

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Reflection improves reliability

An agent that checks its own output is often more useful than one that acts quickly but blindly.

Planning vs reflection

Planning

  • Happens before action
  • Chooses a route toward the goal
  • Helps organize complex work

Reflection

  • Happens after or between actions
  • Checks whether progress is correct
  • Helps catch mistakes and improve

What is reflection in an agentic system?

Why can planning be helpful for multi-step tasks?

A practical pattern

A simple and effective pattern is: plan briefly, act, evaluate the result, then revise. This loop is often better than trying to solve everything in one pass.

PLAN · REASON · REFLECT

The cognitive engine at the heart of every agent

Chain-of-Thought (CoT)

Agent reasons step-by-step before answering. Increases accuracy on complex multi-step tasks.

ReAct

Reasoning + Acting interleaved: think → act → observe → think again.

Plan-then-Execute

Full plan first, then parallel or sequential execution. Better for predictable workflows.

Reflection

Agent critiques its own output. "Is this correct? Did I miss anything?" Catches errors early.

The ReAct Loop in Detail

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Thought

Internal reasoning

→
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Action

Call a tool

→
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Observation

Read the result

→
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Loop / Done

Next thought or finish

Strategy Effectiveness on Complex Tasks