class ReflectionTuner:
    def __init__(self, agent, reflection_steps=3):
        self.agent = agent
        self.reflection_steps = reflection_steps

    def reflect_and_tune(self, initial_task):
        response = self.agent.run(initial_task)
        for step in range(self.reflection_steps):
            # Analyzing the response and adjusting based on findings
            feedback = self.analyze_response(response)
            if feedback:
                print(f"Reflection step {step + 1}: Adjusting response based on feedback.")
                response = self.agent.run(feedback)  # Rerun with adjusted task or prompt
            else:
                print(f"No further tuning required at step {step + 1}. Final response achieved.")
                break
        return response

    def analyze_response(self, response):
        # Basic logic to analyze the response quality and determine next steps
        if "error" in response.lower() or "incomplete" in response.lower():
            return "Please refine the explanation and address missing points."
        elif "unclear" in response.lower() or "vague" in response.lower():
            return "Provide a more detailed and specific analysis."
        return None  # No adjustment required if response is satisfactory