Table of Contents

🧠 Understanding AI Agent Reasoning in LM-Kit.NET


📄 TL;DR

AI Agent Reasoning refers to the cognitive processes agents use to analyze problems, draw conclusions, and make decisions. Rather than jumping directly to answers, reasoning agents break down complex problems through Chain-of-Thought (CoT), step-by-step analysis, and logical inference. In LM-Kit.NET, reasoning capabilities are enhanced through prompting strategies, planning algorithms like ReAct, and structured thinking patterns that improve accuracy, explainability, and problem-solving on complex tasks.


📚 What is Agent Reasoning?

Definition: Agent Reasoning is the systematic process by which AI agents analyze information, decompose problems, evaluate options, and arrive at conclusions. It encompasses:

  • Deductive Reasoning: Drawing specific conclusions from general principles
  • Inductive Reasoning: Forming general rules from specific observations
  • Abductive Reasoning: Inferring the most likely explanation
  • Analogical Reasoning: Applying solutions from similar problems

Why Reasoning Matters

Without explicit reasoning:

  • Agents jump to conclusions without justification
  • Complex problems are oversimplified
  • Errors accumulate without correction
  • Decisions cannot be explained or verified

With reasoning:

  • Problems are systematically decomposed
  • Intermediate steps can be verified
  • Errors are caught through self-checking
  • Responses are explainable and trustworthy

🔗 Chain-of-Thought (CoT) Reasoning

The CoT Principle

+---------------------------------------------------------------------------+
|                   Chain-of-Thought vs Direct Answer                       |
+---------------------------------------------------------------------------+
|                                                                           |
|  WITHOUT CoT:                                                             |
|  +------------+                              +------------+               |
|  |  Question  |------------------------------->|   Answer   |               |
|  +------------+                              +------------+               |
|                                                                           |
|  "What's 15% of 240 plus tax?" --------------> "$41.76" (possibly wrong)    |
|                                                                           |
+---------------------------------------------------------------------------+
|                                                                           |
|  WITH CoT:                                                                |
|  +------------+   +--------+   +--------+   +--------+   +------------+  |
|  |  Question  |---->| Step 1 |---->| Step 2 |---->| Step 3 |---->|   Answer   |  |
|  +------------+   +--------+   +--------+   +--------+   +------------+  |
|                                                                           |
|  "What's 15% of 240 plus 8% tax?"                                        |
|      |                                                                    |
|      +----> "First, 15% of 240 = 240 × 0.15 = 36"                          |
|      +----> "Then, tax on 36 = 36 × 0.08 = 2.88"                           |
|      +----> "Total = 36 + 2.88 = $38.88"  (correct, verifiable)            |
|                                                                           |
+---------------------------------------------------------------------------+

Implementing CoT in LM-Kit.NET

using LMKit.Model;
using LMKit.Agents;

var model = LM.LoadFromModelID("gemma3:12b");

var agent = Agent.CreateBuilder(model)
    .WithSystemPrompt("""
        You are a logical reasoning assistant.

        For every question, follow this thinking process:

        1. UNDERSTAND: Restate the problem in your own words
        2. IDENTIFY: List the key information and constraints
        3. PLAN: Outline the steps needed to solve it
        4. EXECUTE: Work through each step, showing your work
        5. VERIFY: Check your answer makes sense
        6. CONCLUDE: State the final answer clearly

        Always show your reasoning before giving the answer.
        """)
    .Build();

var response = await agent.ExecuteAsync(
    "A train travels 120 miles at 60 mph, then 80 miles at 40 mph. What's the average speed for the entire journey?",
    CancellationToken.None
);

🎯 Reasoning Patterns

1. Zero-Shot CoT

Add "Let's think step by step" to trigger reasoning:

var prompt = """
    Question: If a shirt originally costs $80 and is on sale for 25% off,
    and you have an additional 10% coupon, what's the final price?

    Let's think step by step.
    """;

var response = await agent.ExecuteAsync(prompt, CancellationToken.None);

2. Few-Shot CoT

Provide reasoning examples for the model to follow:

var agent = Agent.CreateBuilder(model)
    .WithSystemPrompt("""
        Solve problems by showing your reasoning step by step.

        Example:
        Q: A store has 50 apples. They sell 30% and receive a shipment of 25 more. How many do they have?
        A: Let me work through this:
           - Starting amount: 50 apples
           - Sold 30%: 50 × 0.30 = 15 apples sold
           - Remaining: 50 - 15 = 35 apples
           - New shipment: 35 + 25 = 60 apples
           - Final answer: 60 apples

        Now solve the following problem using the same approach.
        """)
    .Build();

3. Self-Consistency

Generate multiple reasoning paths and select the most common answer:

var results = new List<string>();

// Generate multiple reasoning attempts
for (int i = 0; i < 5; i++)
{
    var response = await agent.ExecuteAsync(
        $"[Attempt {i + 1}] {question}",
        CancellationToken.None
    );
    results.Add(ExtractAnswer(response.Text));
}

// Select the most frequent answer
var finalAnswer = results
    .GroupBy(x => x)
    .OrderByDescending(g => g.Count())
    .First()
    .Key;

4. Tree of Thoughts (ToT)

Explore multiple reasoning branches:

var agent = Agent.CreateBuilder(model)
    .WithSystemPrompt("""
        For complex problems, explore multiple approaches:

        APPROACH A:
        - Describe the first approach
        - Work through it step by step
        - Note the result

        APPROACH B:
        - Describe an alternative approach
        - Work through it step by step
        - Note the result

        COMPARISON:
        - Compare the approaches
        - Identify which is more reliable
        - Explain your choice

        FINAL ANSWER:
        - State the answer from the best approach
        """)
    .Build();

🔄 ReAct: Reasoning + Acting

ReAct interleaves reasoning with tool use:

+---------------------------------------------------------------------------+
|                            ReAct Pattern                                  |
+---------------------------------------------------------------------------+
|                                                                           |
|  +----------+                                                             |
|  | Question |                                                             |
|  +----+-----+                                                             |
|       |                                                                   |
|       v                                                                   |
|  +----------+     +----------+     +----------+                          |
|  | Thought  |------>|  Action  |------>|Observation|------+                  |
|  |          |     |          |     |          |      |                   |
|  |"I need   |     |WebSearch |     |"Results: |      |                   |
|  | to find  |     |("GDP     |     | $25.5T"  |      |                   |
|  | GDP..."  |     | USA")    |     |          |      |                   |
|  +----------+     +----------+     +----------+      |                   |
|       ▲                                              |                   |
|       |                                              |                   |
|       +----------------------------------------------+                   |
|                         (Loop until done)                                 |
|       |                                                                   |
|       v                                                                   |
|  +----------+                                                             |
|  |  Answer  |                                                             |
|  +----------+                                                             |
|                                                                           |
+---------------------------------------------------------------------------+

ReAct Implementation

using LMKit.Agents;
using LMKit.Agents.Tools.BuiltIn;

var agent = Agent.CreateBuilder(model)
    .WithPlanningStrategy(PlanningStrategy.ReAct)
    .WithSystemPrompt("""
        You are a research assistant that thinks carefully before acting.

        For each step:
        THOUGHT: Explain what you're thinking and why
        ACTION: Choose a tool and explain what you'll do with it
        OBSERVATION: [System will provide tool results]
        ... (repeat as needed)
        ANSWER: Provide your final, well-reasoned answer
        """)
    .WithTools(tools =>
    {
        tools.Register(BuiltInTools.WebSearch);
        tools.Register(BuiltInTools.Calculator);
    })
    .Build();

var response = await agent.ExecuteAsync(
    "Which country has the higher GDP per capita: Norway or Switzerland? And by how much?",
    CancellationToken.None
);

🔍 Reasoning Types

Deductive Reasoning

From general rules to specific conclusions:

var agent = Agent.CreateBuilder(model)
    .WithSystemPrompt("""
        Apply deductive reasoning:
        1. State the general rule or premise
        2. Identify the specific case
        3. Apply the rule to reach the conclusion

        Example:
        - Rule: All mammals are warm-blooded
        - Case: A dolphin is a mammal
        - Conclusion: Therefore, a dolphin is warm-blooded
        """)
    .Build();

Inductive Reasoning

From specific observations to general patterns:

var agent = Agent.CreateBuilder(model)
    .WithSystemPrompt("""
        Apply inductive reasoning:
        1. List the specific observations
        2. Identify patterns across observations
        3. Form a general hypothesis
        4. Acknowledge uncertainty in the generalization

        Example:
        - Observation: Swan 1 is white, Swan 2 is white, Swan 3 is white
        - Pattern: All observed swans are white
        - Hypothesis: Swans are generally white
        - Uncertainty: But we haven't seen all swans
        """)
    .Build();

Analogical Reasoning

Apply solutions from similar problems:

var agent = Agent.CreateBuilder(model)
    .WithSystemPrompt("""
        Use analogical reasoning:
        1. Identify a similar problem you've seen before
        2. Describe the key similarities
        3. Note any important differences
        4. Adapt the known solution to the new problem
        5. Verify the adapted solution makes sense
        """)
    .Build();

📊 Reasoning Strategies Comparison

Strategy Best For Strengths Limitations
Zero-Shot CoT Quick reasoning Simple, no examples needed Less reliable for complex tasks
Few-Shot CoT Consistent format Higher accuracy Requires good examples
Self-Consistency Critical decisions Reduces errors Slower, more tokens
Tree of Thoughts Complex problems Explores alternatives Computationally expensive
ReAct Tool-using agents Combines reasoning + action Requires tool integration

🎯 Best Practices

1. Encourage Explicit Reasoning

var agent = Agent.CreateBuilder(model)
    .WithSystemPrompt("""
        ALWAYS show your reasoning:
        - Don't just state conclusions
        - Explain HOW you reached each conclusion
        - If you're uncertain, say so
        - If you made an assumption, state it
        """)
    .Build();

2. Verify Intermediate Steps

var agent = Agent.CreateBuilder(model)
    .WithSystemPrompt("""
        After each reasoning step:
        - Check: Does this step follow logically?
        - Verify: Are the numbers/facts correct?
        - Question: Am I missing anything?

        If you find an error, backtrack and correct it.
        """)
    .Build();

3. Handle Uncertainty

var agent = Agent.CreateBuilder(model)
    .WithSystemPrompt("""
        Express confidence appropriately:
        - "I'm confident that..." for well-supported conclusions
        - "It's likely that..." for probable inferences
        - "I'm uncertain, but..." for speculative reasoning
        - "I cannot determine..." when information is insufficient
        """)
    .Build();

📖 Key Terms

  • Chain-of-Thought (CoT): Explicit step-by-step reasoning before answering
  • Zero-Shot CoT: Triggering reasoning without examples ("Let's think step by step")
  • Few-Shot CoT: Providing reasoning examples for the model to follow
  • Self-Consistency: Generating multiple reasoning paths and selecting the consensus
  • Tree of Thoughts: Exploring branching reasoning paths
  • ReAct: Interleaving reasoning (Thought) with actions (Act)
  • Inference: Drawing conclusions from available information

📚 Related API Documentation


🔗 Related Glossary Topics


🌐 External Resources


📝 Summary

AI Agent Reasoning enables agents to tackle complex problems through systematic analysis rather than impulsive responses. Techniques like Chain-of-Thought (CoT) make reasoning explicit and verifiable, while Self-Consistency improves reliability through multiple reasoning attempts. Tree of Thoughts explores alternative solution paths, and ReAct combines reasoning with tool use for grounded problem-solving. In LM-Kit.NET, reasoning capabilities are enhanced through system prompts that encourage step-by-step thinking, planning strategies like ReAct, and patterns that verify intermediate conclusions. Well-designed reasoning transforms agents from black-box answer generators into transparent, logical assistants that users can trust and verify.