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# © 2025 Elena Marziali — Code released under Apache 2.0 license.
# See LICENSE in the repository for details.
# Removal of this copyright is prohibited.
# This function simulates an intentional decision-making process by the AI agent.
# It analyzes the proposed action in relation to the goal, available alternatives, and context.
# Metacognition functions that adapt to the system
def execute_intentional_choice(action, goal, alternatives, context):
ai_explanation = choice_with_intention(action, goal, alternatives, context)
explanation_content = getattr(ai_explanation, "content", str(ai_explanation)).strip()
intentional_log.append({
"action": action,
"reason": explanation_content,
"impact": f"Expected outcome for goal: {goal}",
"timestamp": datetime.datetime.utcnow().isoformat()
})
return explanation_content
# Generates a response with intentionality by combining reasoning, AI response, and extracted text
def generate_response_with_intention(prompt, action, goal, alternatives, context):
reasoning = execute_intentional_choice(action, goal, alternatives, context)
ai_response = llm.invoke(prompt)
response_text = getattr(ai_response, "content", str(ai_response)).strip()
return f"{response_text}\n\n*Agent's intentional explanation:*\n{reasoning}" |