Summary
The shift from monolithic LLMs to connected systems represents a fundamental advancement in AI architecture. These systems address scalability and knowledge fragmentation through modular designs, hierarchical pipelines, agentic workflows (e.g., AutoGPT), and distributed networks enabled by orchestration tools such as LangChain and optimization techniques such as parallel inference. Advanced patterns such as self-correction loops, ToT reasoning, and neuro-symbolic integration further enhance reliability and capability. Together, they form a foundation for more efficient, adaptable, and human-aligned AI systems.
As AI systems evolve from isolated LLMs to interconnected, modular ecosystems, ensuring their reliability becomes both more challenging and more critical. The next chapter examines how to monitor and maintain these complex systems in production, focusing on the tools, metrics, and human oversight needed to keep them accurate, safe, and aligned over time.
