DEV Community

Bartolomeo Hart
Bartolomeo Hart

Posted on

Building an AI Agent Is Easy! Here’s Proof..

#ai #agent #automation #anthropic

AI Agent Banner
I’ve created a simple AI agent that shows how anyone with minimal Python knowledge can get started. Let me show you how straightforward it can be!

(Note: Full code example provided below)

4 Core Components🎯

The Brain (AI Model Connection)

At its core, an AI agent just needs to talk to a language model. Here’s how you establish a connection using the Anthropic API:

def __init__(self, anthropic_api_key): self.anthropic_client = anthropic.Client(api_key=anthropic_api_key) self.models = { "haiku": "claude-3-haiku-20240307", "sonnet": "claude-3-sonnet-20240229", }
Enter fullscreen mode Exit fullscreen mode

The Memory (Action Management)

Register all possible actions that are available for your agent to use. No rocket science here:

# Register single actions self.actions = { "search_wikipedia": self.search_wikipedia, "calculate": self.calculate, "get_weather": self.get_weather, "get_crypto": self.get_crypto_data, } # Register automated actions self.automated_actions = { "crypto_tracking": self.start_crypto_scheduler }
Enter fullscreen mode Exit fullscreen mode

The Instructions (System Prompt)

Here’s where the magic happens. Just tell your AI what it can do:

self.system_prompt = """You are an AI assistant that can perform both automated and one-time actions. Response Format: - For automation: 'AUTOMATE: action_name' - For one-time actions: 'ACTION: action_name: parameter' - For general queries: Respond naturally """
Enter fullscreen mode Exit fullscreen mode

The Executor (Command Processing)

Simply watch for commands and execute them:

# Check for automation command automate_match = re.search(r'AUTOMATE: (\w+)', assistant_message) if automate_match: action_name = automate_match.group(1) result = self.automated_actions[action_name]()
Enter fullscreen mode Exit fullscreen mode

Piecing It All Together 🔄

Let’s see what this could look like:

# Init Anthropic self.anthropic_client = anthropic.Client(api_key=anthropic_api_key) self.models = { "haiku": "claude-3-haiku-20240307", "sonnet": "claude-3-sonnet-20240229", } # Create system prompt self.system_prompt = """You are an AI assistant that ...more""" # Create automated action def start_crypto_scheduler(self): if not self.scheduler_running: self.scheduler_running = True # Schedule task for every 10 seconds  schedule.every(10).seconds.do(self.save_crypto_data) # Initial run  self.save_crypto_data() # Start scheduler in separate thread  scheduler_thread = threading.Thread(target=self.run_scheduler) scheduler_thread.daemon = True scheduler_thread.start() # Register action self.automated_actions = { "crypto_tracking": self.start_crypto_scheduler } # Excute def execute(self, user_input): # User input : "Can you monitor crypto prices?"  self.messages.append({"role": "user", "content": user_input}) conversation_history = [msg for msg in self.messages if msg['role'] != 'system'] selected_model = self.models.get(model_choice, self.models["haiku"]) # Get AI response  response = self.anthropic_client.messages.create( model=selected_model, messages=conversation_history, system=self.system_prompt, max_tokens=1000, temperature=0.2, top_p=1.0, ) assistant_message = response.content[0].text self.messages.append({"role": "assistant", "content": assistant_message}) # Check for automation command  automate_match = re.search(r'AUTOMATE: (\w+)', assistant_message) if automate_match: action_name = automate_match.group(1) if action_name in self.automated_actions: result = self.automated_actions[action_name]() return f"Automated task started: {result}" return f"Error: Unknown automated action {action_name}"
Enter fullscreen mode Exit fullscreen mode

That’s it! A few simple steps to go from user input to automated task execution.

Real-World Example🚀

Want to try it yourself? Check out the full project code and get started within minutes: https://github.com/unameit10000000/reallysimpleagent

Takeaway 💡

Building an AI agent does not need to be complex. With just basic Python knowledge and a clear structure, you can start building your own agents incrementally and create something powerfull.

Top comments (0)