Tracing agent reasoning chains in Agentic Ai - ML Experiment: Train & Evaluate

Experiment - Tracing agent reasoning chains

Problem:You have an AI agent that makes decisions by reasoning through multiple steps. Currently, you cannot see or understand the chain of thoughts the agent uses to reach its answers.

Current Metrics:Agent accuracy: 85%, Reasoning transparency: 0% (no visible reasoning steps)

Issue:The agent's decisions are accurate but opaque. You want to trace and display the reasoning chain to improve trust and debugging.

Your Task

Modify the agent to output its reasoning chain step-by-step along with the final answer, without reducing accuracy below 80%.

Do not change the agent's core decision model.

Only add tracing/logging of reasoning steps.

Maintain or improve current accuracy.

Hint 1

Hint 2

Hint 3

Solution

Agentic_ai

class ReasoningAgent:
    def __init__(self):
        pass

    def reason(self, input_data):
        reasoning_chain = []
        # Step 1: Understand input
        step1 = f"Received input: {input_data}"
        reasoning_chain.append(step1)

        # Step 2: Process data (dummy example)
        processed = input_data.lower()
        step2 = f"Processed input to lowercase: {processed}"
        reasoning_chain.append(step2)

        # Step 3: Make decision (dummy logic)
        if 'hello' in processed:
            decision = 'Greet back'
        else:
            decision = 'No greeting'
        step3 = f"Decision based on processed input: {decision}"
        reasoning_chain.append(step3)

        # Return both reasoning chain and final decision
        return reasoning_chain, decision


# Example usage
agent = ReasoningAgent()
input_text = "Hello, how are you?"
chain, answer = agent.reason(input_text)

print("Reasoning chain:")
for step in chain:
    print(step)
print(f"Final answer: {answer}")

Added a list to store reasoning steps.

Inserted reasoning steps after each processing stage.

Returned both the reasoning chain and final decision.

Printed the reasoning chain clearly for user understanding.

Results Interpretation

Before: Accuracy 85%, Reasoning transparency 0% (no steps shown)

After: Accuracy 85%, Reasoning transparency 100% (all steps shown)

Tracing the agent's reasoning chain helps users understand how decisions are made without hurting accuracy. This builds trust and aids debugging.

Bonus Experiment

Try adding confidence scores to each reasoning step to show how sure the agent is at each stage.

💡 Hint

Modify the reasoning steps to include a confidence value between 0 and 1, and display it alongside each step.