import time
from typing import List, Dict

CYCLE_SECONDS = 60  # how often the agent “wakes up”

goal_store: List[Goal] = []
running = True

def read_system_state() -> Dict:
    # In practice, load from logs, DB, metrics, vector stores, etc.
    return {
        "domains": {
            "pricing_research": {
                "curiosity_reward": 0.8,
                "fully_explored": False,
                "impact_score": 0.9,
                "cost_score": 0.4,
                "risk_score": 0.2,
            },
            # ... other domains
        }
    }

def store_goals(new_goals: List[Goal]):
    goal_store.extend(new_goals)

def get_all_goals() -> List[Goal]:
    return goal_store

def prioritize(goals: List[Goal]) -> List[Goal]:
    return sorted(goals, key=lambda g: (g.priority, g.expected_value), reverse=True)

def llm_plan(goal: Goal, state: Dict) -> str:
    # In reality, call your LLM with tools and context.
    return f"Plan steps to achieve goal: {goal.description}"

def execute_plan(plan: str):
    # Tool calling, API usage, agents, etc.
    print(f"[EXECUTING] {plan}")

def autonomous_cycle():
    global goal_store

    while running:
        state = read_system_state()

        # 1. Generate new internal goals
        new_goals = generate_internal_goals(state)
        store_goals(new_goals)

        # 2. Prioritize
        goals = get_all_goals()
        prioritized = prioritize(goals)

        # 3. Pick best goal, plan, act
        if prioritized:
            best_goal = prioritized[0]
            plan = llm_plan(best_goal, state)
            execute_plan(plan)

            # Remove after acting once (naive completion)
            goal_store = [g for g in goal_store if g.id != best_goal.id]

        # 4. Sleep
        time.sleep(CYCLE_SECONDS)