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AI Agents in Practice
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Since the emergence of large language models and the explosion of AI applications, developers have faced a growing challenge: how to effectively manage and coordinate increasingly complex AI systems. As AI agents become more capable and autonomous, their behaviors must be structured, monitored, and optimized—often across multiple tools, services, and data sources. This growing complexity has created an urgent need for orchestration: a way to ensure these intelligent components work together seamlessly toward a common goal.
AI orchestrators emerged in response to this need. Rather than simply offering pre-built components, they provide a framework for structuring interactions, managing dependencies, and maintaining control over multi-agent or modular workflows—all while accelerating development and reducing operational risk.
In this chapter, we are going to cover the following topics: