The AI Interview - Master AI/ML Interviews

Implement an Observation-Action cycle logger that validates proper sequence:

log_observation(): Starts new cycle with observation
log_thought(): Adds thought to current cycle (must have observation first)
log_action(): Completes cycle with action (must have thought first)
get_cycles(): Returns list of completed cycles

Validation Rules:

Must log in order: observation → thought → action
Skip logging and return False if sequence violated
Complete cycle only when action is logged
Each cycle: {'observation': ..., 'thought': ..., 'action': ...}

Return Values:

log_* methods return True if logged successfully, False if sequence error

Examples

Example 1:

Input:

logger = OALogger(); logger.log_observation('User asks weather'); logger.log_thought('Need weather API'); logger.log_action('call_weather_api()'); len(logger.get_cycles())

Output: 1

Explanation: Complete cycle logged, one cycle in history

Example 2:

Input: logger = OALogger(); logger.log_thought('No observation')

Output: False

Explanation: Thought without observation returns False

Starter Code

class OALogger:
    """
    Logger for Observation → Thought → Action cycles.
    Validates and records agent reasoning traces.
    """
    
    def __init__(self):
        self.cycles = []
        self.current_cycle = {}
    
    def log_observation(self, observation):
        """Log observation, starting new cycle"""
        # Your implementation here
        pass
    
    def log_thought(self, thought):
        """Log thought for current cycle"""
        # Your implementation here
        pass
    
    def log_action(self, action):
        """Log action and complete cycle"""
        # Your implementation here
        pass
    
    def get_cycles(self):
        """Return all completed cycles"""
        # Your implementation here
        pass

Observation-Action Cycle Logger

Examples

Starter Code