Why observability is critical for agents in Agentic Ai - Experiment to Prove It

Experiment - Why observability is critical for agents

Problem:You have built an AI agent that performs tasks autonomously, but you cannot see how it makes decisions or what internal states it has during operation.

Current Metrics:Agent completes 90% of tasks but sometimes fails silently without clear reasons.

Issue:Lack of observability causes difficulty in debugging and improving the agent's behavior because internal decision processes are hidden.

Your Task

Add observability features to the agent so you can track its decisions and internal states during task execution, improving debugging and performance analysis.

Do not change the agent's core decision-making logic.

Add only lightweight logging and monitoring to avoid slowing down the agent.

Hint 1

Hint 2

Hint 3

Solution

Agentic_ai

import logging

class Agent:
    def __init__(self):
        self.state = {}
        logging.basicConfig(level=logging.INFO, format='%(message)s')

    def decide(self, input_data):
        # Example decision logic
        decision = 'action_a' if input_data > 0.5 else 'action_b'
        self.state['last_input'] = input_data
        self.state['last_decision'] = decision
        logging.info(f'Decision made: {decision} for input {input_data}')
        return decision

    def execute_task(self, inputs):
        results = []
        for i, inp in enumerate(inputs):
            logging.info(f'Starting task {i+1} with input {inp}')
            decision = self.decide(inp)
            # Simulate task execution
            success = decision == 'action_a' and inp > 0.5
            logging.info(f'Task {i+1} success: {success}')
            results.append(success)
        return results

# Example usage
agent = Agent()
inputs = [0.6, 0.4, 0.8, 0.3]
results = agent.execute_task(inputs)
logging.info(f'Overall success rate: {sum(results)/len(results):.2f}')

Added logging to track decisions and inputs.

Logged success or failure of each task.

Logged overall success rate after tasks complete.

Results Interpretation

Before observability: Agent completed 90% tasks but failures were silent and hard to diagnose.

After observability: Agent still completes 90% tasks but now logs show exactly what decisions were made and when failures occurred.

Observability lets us see inside the agent's decision process, making it easier to find and fix problems, improving trust and performance.

Bonus Experiment

Add a dashboard to visualize the agent's decision logs and success rates in real time.

💡 Hint

Use a simple web app or notebook visualization library to plot decision counts and success over time.