The article explores the evolution from simple function-based LLM applications to complex multi-agent systems, demonstrating how specialized AI agents with specific tools can enhance system performanc
e and scalability. It provides a practical implementation using OpenAI's API, showing both basic function calling and advanced multi-agent orchestration with four specialized agents handling different aspects of data processing and analysis.
Reasons to Read -- Learn:
how to implement a practical multi-agent system using OpenAI's API, with concrete code examples showing how to create and orchestrate specialized agents for data processing, analysis, and visualization tasks.
progression from simple function-based systems to complex multi-agent architectures, including detailed explanations of how to structure and organize tools for optimal AI system performance.
how to use OpenAI's Structured Outputs feature to enforce strict schemas on model outputs, making AI systems more reliable and predictable in real-world applications.
author: Cobus Greyling
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