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Developing automated systems using LLM APIs for specific tasks

Core Idea: API Agents are autonomous software systems that leverage large language model APIs to perform specific tasks, make decisions, and interact with other systems without constant human supervision.

Key Elements

Agent Architecture

• Core LLM: Foundation model providing reasoning capabilities (e.g., Gemini 2.5 Pro)
• API Integration: Connection to model provider's services
• Task Framework: Structure for handling specific workflows
• Memory System: Storage of conversation history and context
• Error Handling: Mechanisms for addressing API limitations and failures

Implementation Steps

1. Set up API access

   • Create account with provider (Google, OpenAI, Anthropic)
   • Generate API keys
   • Install necessary client libraries

2. Design agent behavior

   • Define specific tasks and capabilities
   • Create prompt templates
   • Establish decision-making criteria
   • Plan error recovery mechanisms

3. Implement core functionality

   import google.generativeai as genai
   import time
   
   # Configure API access
   genai.configure(api_key="YOUR_API_KEY")
   
   # Create agent with retry logic
   def agent_response(prompt, max_retries=3, delay=2):
       for attempt in range(max_retries):
           try:
               model = genai.GenerativeModel('gemini-2.5-pro-20250101')
               response = model.generate_content(prompt)
               return response.text
           except Exception as e:
               print(f"Attempt {attempt+1} failed: {e}")
               if attempt < max_retries - 1:
                   time.sleep(delay)
               else:
                   return f"Failed after {max_retries} attempts: {e}"
   
   # Example usage
   result = agent_response("Create a marketing strategy for a new eco-friendly product")
   print(result)
   

4. **Add specialized capabilities**
    - Tool integration (databases, APIs)
    - Multi-agent coordination
    - User feedback processing
    - Learning mechanisms

### Common Agent Types

- **Research agents**: Gather and synthesize information
- **Creative agents**: Generate content and ideas
- **Assistant agents**: Help with specific user needs
- **Automation agents**: Perform recurring tasks
- **Decision agents**: Evaluate options and recommend actions

## Additional Connections

- **Broader Context**: AI Agent Frameworks (systems for building complex agents)
- **Applications**: Task Automation (practical implementations of agents)
- **See Also**: LangChain (popular framework for building LLM-powered applications)

## References

1. Google Generative AI Python SDK Documentation
2. Building LLM-powered Applications (2024)

#api #agents #ai #automation #development

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**Connections:**
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**Sources:**
- From: David Ondrej - Crea cualquier cosa con Gemini 2.5 Pro aquí te explicamos cómo