Anthropic has nearly stopped hiring junior staff because AI agents now work six times as many hours

Introduction

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In this tutorial, you'll learn how to create a simple AI agent using Python that can automate routine tasks. This tutorial is inspired by the news about Anthropic's AI agents working six times as many hours as humans. We'll build a basic AI agent that can perform tasks like data analysis, logging, and simple decision-making. This is a beginner-friendly guide that requires no prior AI experience.

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Prerequisites

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Before starting this tutorial, you'll need:

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• A computer with internet access
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• Python 3.7 or higher installed
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• A code editor (like VS Code or PyCharm)
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• Basic understanding of Python syntax
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Step-by-Step Instructions

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Step 1: Set Up Your Python Environment

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Install Required Libraries

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We'll use several Python libraries to build our AI agent. First, create a new folder for this project and open your terminal or command prompt in that folder.

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pip install pandas numpy openai python-dotenv

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Why: These libraries provide the core functionality we need - pandas and numpy for data handling, openai for AI capabilities, and python-dotenv for managing API keys securely.

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Step 2: Create Your AI Agent Class

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Build the Foundation

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Let's create the basic structure for our AI agent. Create a file called ai_agent.py and add this code:

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import pandas as pd\nimport numpy as np\nfrom datetime import datetime\nimport os\nfrom dotenv import load_dotenv\n\n# Load environment variables\nload_dotenv()\n\nclass SimpleAIAgent:\n    def __init__(self, name=\"AI_Agent\"):\n        self.name = name\n        self.work_hours = 0\n        self.tasks_completed = 0\n        \n    def log_work_hours(self, hours):\n        self.work_hours += hours\n        print(f\"{self.name} worked {hours} hours. Total: {self.work_hours} hours\")\n        \n    def complete_task(self):\n        self.tasks_completed += 1\n        print(f\"Task completed. Total tasks: {self.tasks_completed}\")\n        \n    def get_status(self):\n        return {\n            \"name\": self.name,\n            \"work_hours\": self.work_hours,\n            \"tasks_completed\": self.tasks_completed\n        }

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Why: This creates the basic framework for our AI agent with core functions for tracking work hours and tasks. The class structure allows us to easily expand functionality later.

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Step 3: Add Data Processing Capabilities

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Enhance Your Agent's Functionality

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Now let's add the ability for our agent to process data - a key capability mentioned in the article about AI agents working more hours:

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class SimpleAIAgent(SimpleAIAgent):\n    def analyze_data(self, data):\n        if isinstance(data, list):\n            df = pd.DataFrame(data)\n        else:\n            df = data\n        \n        print(f\"Analyzing {len(df)} rows of data\")\n        print(f\"Data shape: {df.shape}\")\n        \n        # Basic statistics\n        if df.select_dtypes(include=[np.number]).shape[1] > 0:\n            print(\"Numeric column statistics:\")\n            print(df.describe())\n        \n        return df\n    \n    def generate_report(self):\n        report = f\"{self.name} Report\\n\"\n        report += f\"Generated on: {datetime.now()}\\n\"\n        report += f\"Work hours: {self.work_hours}\\n\"\n        report += f\"Tasks completed: {self.tasks_completed}\\n\"\n        \n        return report

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Why: This enhancement allows our AI agent to process and analyze data, similar to how AI systems might work in large organizations like Anthropic. The agent can now handle the kind of work that would normally require human attention.

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Step 4: Create a Task Automation System

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Automate Routine Work

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Let's add a function that simulates automated task execution - a core concept from the news article:

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class SimpleAIAgent(SimpleAIAgent):\n    def auto_execute_tasks(self, task_list):\n        print(f\"{self.name} starting automated task execution\")\n        \n        for i, task in enumerate(task_list):\n            print(f\"Executing task {i+1}: {task}\")\n            # Simulate work\n            self.log_work_hours(0.5)\n            self.complete_task()\n            \n        print(f\"{self.name} completed {len(task_list)} tasks automatically\")\n        \n    def work_around_clock(self):\n