What Is LLM Reasoning and How Does It Apply to AI Agents?

What Is LLM Reasoning?

LLM reasoning refers to a model's ability to break down complex problems into logical steps, evaluate intermediate results, and arrive at well-supported conclusions. Rather than generating an immediate response based on pattern matching, reasoning models allocate additional computation at inference time to think through problems systematically.

All reasoning techniques share a common principle: they enhance response quality by scaling test-time compute — allowing the model to generate more tokens of internal reasoning before producing a final answer. This tradeoff between speed and quality is fundamental to modern AI agent design.

Three Categories of LLM Reasoning

1. Long Thinking

Long thinking extends the model's reasoning process by generating explicit chains of intermediate steps before arriving at a conclusion. The model essentially "shows its work," making the reasoning process transparent and debuggable.

Chain of Thought (CoT) is the foundational technique. By prompting models to think step-by-step before answering, CoT dramatically improves performance on mathematical, logical, and multi-step reasoning tasks. Instead of jumping directly to an answer, the model generates intermediate reasoning steps that build toward the conclusion.

DeepSeek-R1 advanced this concept through novel reinforcement learning techniques that enable models to autonomously explore and refine their reasoning strategies. Rather than relying on hand-crafted prompts, R1 models learn to reason more effectively through training.

2. Searching for the Best Solution

Search-based reasoning generates multiple candidate solutions and evaluates them to select the best one. This is particularly valuable for problems with large solution spaces where the first answer is unlikely to be optimal.

Tree of Thought (ToT) extends chain-of-thought by exploring multiple reasoning paths simultaneously, evaluating each branch, and selecting the most promising direction. This enables the model to consider alternative approaches rather than committing to a single reasoning chain.

Self-Consistency generates multiple independent reasoning chains for the same problem and selects the answer that appears most frequently. This voting mechanism reduces the impact of individual reasoning errors.

3. Think-Critique-Improve

Iterative reasoning loops where the model generates a response, critiques its own output, and refines it based on the critique. This self-improvement cycle can run multiple times, with each iteration producing a better result.

ReAct (Reasoning + Acting) combines reasoning with action for multi-step decision-making. The model alternates between thinking about what to do next and taking actions — calling tools, querying databases, or making API requests. This interleaving of reasoning and action is the foundation of modern AI agent architectures.

Self-Reflection adds a critique step where the agent analyzes its own reasoning, identifies potential errors or weaknesses, and revises its approach. This produces more reliable outputs for complex, high-stakes tasks.

How Reasoning Applies to AI Agents

AI agents are autonomous systems that perceive their environment, make decisions, and take actions to achieve goals. Reasoning is what transforms a simple chatbot into a capable agent.

Planning and Task Decomposition

Agents use reasoning to break complex user requests into manageable sub-tasks. For example, a request to "book a flight to Tokyo next week under $800" requires the agent to: identify date constraints, search for flights, filter by price, evaluate options, and present recommendations.

Tool Selection and Usage

Agents must decide which tools to use, when to use them, and how to interpret the results. ReAct-style reasoning enables agents to think about which API to call, formulate the correct parameters, process the response, and determine whether additional tool calls are needed.

Error Recovery

When a tool call fails or returns unexpected results, reasoning agents can diagnose what went wrong, try alternative approaches, or ask the user for clarification — rather than simply failing or hallucinating a response.

Multi-Step Workflows

Complex business workflows — scheduling appointments, processing orders, handling insurance claims — require the agent to maintain state across multiple reasoning and action steps, adapting its plan as new information becomes available.

Frequently Asked Questions

What is the difference between LLM reasoning and regular LLM inference?

Regular LLM inference generates responses based on pattern matching from training data — the model produces output tokens directly from the input prompt. LLM reasoning adds explicit intermediate thinking steps before generating the final answer. The model allocates additional computation (more tokens) to analyze the problem, consider multiple approaches, and verify its logic before responding.

What is chain-of-thought prompting?

Chain-of-thought (CoT) prompting instructs a language model to show its reasoning step by step rather than jumping directly to an answer. By generating intermediate reasoning tokens, the model can solve complex problems that require multi-step logic, mathematical calculations, or causal reasoning. CoT can be triggered by adding phrases like "think step by step" to prompts.

How does ReAct work in AI agents?

ReAct (Reasoning + Acting) is a framework where AI agents alternate between reasoning steps and action steps. In each cycle, the agent: (1) reasons about the current state and what to do next, (2) selects and executes an action (tool call, API request, database query), (3) observes the result, and (4) reasons about the next step based on the new information. This loop continues until the task is complete.

What is test-time compute scaling?

Test-time compute scaling is the practice of allocating more computational resources during inference (when the model generates responses) to improve output quality. Instead of making the model larger or training it longer, you let it think longer on each request. Techniques like chain-of-thought, self-consistency, and self-reflection all scale test-time compute to produce better results.

Can reasoning be used with any LLM?

Most modern LLMs support some form of reasoning through chain-of-thought prompting. However, models specifically trained for reasoning (like DeepSeek-R1, o1, o3) perform significantly better on complex reasoning tasks. Smaller models can benefit from reasoning techniques but may produce less reliable intermediate steps compared to larger, reasoning-optimized models.