What is reasoning in AI and what is dynamic reasoning?

Reasoning in AI and Dynamic Reasoning

Reasoning in AI refers to the model's ability to work through multi-step problems logically. Dynamic reasoning is the ability to adapt the reasoning strategy mid-problem based on intermediate results.

What Is AI Reasoning?

Traditional LLMs complete text token-by-token. Reasoning models add a deliberate "thinking" phase before responding:

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Standard LLM:
  Prompt → [single forward pass] → Answer

Reasoning Model:
  Prompt → [extended thinking / chain of thought] → Answer
  (may internally explore multiple paths before committing)

Types of Reasoning

Chain-of-Thought Prompting

The simplest way to elicit reasoning:

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from anthropic import Anthropic

client = Anthropic()

response = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=1024,
    messages=[{
        "role": "user",
        "content": '''Solve step by step:
A store sells apples for $0.50 each and oranges for $0.75 each.
If I buy 8 apples and 5 oranges, how much do I spend?
Think through this carefully.'''
    }]
)
# Model will work through: 8×0.50 = $4.00, 5×0.75 = $3.75, total = $7.75

Extended Thinking (Claude)

Claude supports explicit extended thinking — a dedicated reasoning block:

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response = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=16000,
    thinking={
        "type": "enabled",
        "budget_tokens": 10000  # How much thinking to allow
    },
    messages=[{
        "role": "user",
        "content": "What is the most efficient algorithm for this problem: [complex problem]"
    }]
)

# Response includes thinking block + final answer
for block in response.content:
    if block.type == "thinking":
        print(f"Reasoning: {block.thinking}")
    else:
        print(f"Answer: {block.text}")

Dynamic Reasoning

Dynamic reasoning means the model adapts its approach based on intermediate results:

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# Dynamic reasoning example: agent adapts strategy mid-task
def dynamic_solve(problem: str) -> str:
    # First attempt
    initial_approach = llm.invoke(f"How should I approach: {problem}")

    # Try the approach
    result = execute(initial_approach)

    if not result.success:
        # Dynamically change strategy
        new_approach = llm.invoke(
            f"That approach failed: {result.error}. "
            f"What's a different way to solve: {problem}"
        )
        result = execute(new_approach)

    return result

Reasoning Models (2025)

When to Use Reasoning Models