explainability might be integrated into an LLM agent's output

def explainable_decision(llm_agent, input_data, decision_context):
    decision = llm_agent.make_decision(input_data, decision_context)

    explanation_prompt = f"""
    Explain the reasoning behind the following decision:
    Decision: {decision}
    Input data: {input_data}
    Context: {decision_context}

    Provide:
    1. Key factors influencing the decision
    2. Alternative options considered
    3. Potential risks and mitigations
    4. Confidence level in the decision (Low, Medium, High)
    """

    explanation = llm_agent.generate(explanation_prompt)

    return {
        "decision": decision,
        "explanation": explanation,
        "confidence": llm_agent.extract_confidence(explanation)
    }

# Usage
result = explainable_decision(executive_llm_agent,
                              "Q3 sales data and market projections",
                              "Strategic decision on expanding product line")