Building Ai Agents with Shell Scripts by Laurent Doguin

AI Agents allow LLMs to use tools and external functions rather than just generating text responses

Key components of AI Agents:

• Tool definitions that specify available functions/capabilities
• Prompt engineering to guide the model’s reasoning
• Loop of thought, action, observation until reaching completion
• Temperature setting (0-1) to control creativity/reproducibility

Benefits of using AI Agents:

• Reduced hallucination by constraining responses to defined tools
• More accurate results for math/calculations
• Ability to interact with external systems and data
• Structured function calling instead of free-form text generation

Modern LLMs have built-in tool support:

• New models understand tool usage natively
• Can decompose complex questions into function calls
• Maintain conversation history and context
• Return structured responses with function calls and reasoning

Implementation considerations:

• Need exit conditions to prevent infinite loops
• Convert inputs/outputs to vectors for LLM processing
• RAG (Retrieval Augmented Generation) helps provide context
• Important to handle function parameters and return values properly
• Can use shell scripts and CLI tools rather than just Python

Common patterns include:

• System role for model configuration
• User role for queries
• Tool role for function calls
• Assistant role for model responses
• Structured message history tracking