Build Agents, Not Pipelines (www.seangoedecke.com)

Recent discussions in AI development emphasize the distinction between using large language models (LLMs) in programmatic workflows as either pipelines or agents. Pipelines follow a structured control flow defined by the developer, enabling predictable execution but often requiring extensive context-gathering efforts. Alternatively, agents possess the flexibility to manage their control flow by utilizing various tools, allowing them to adaptively source and process information without being constrained by pre-defined sequences. This approach is particularly beneficial for complex tasks where context may be extensive or unpredictable. The implications for the AI/ML community are significant, as the choice between pipelines and agents affects scalability, cost, and the overall efficacy of AI applications. Although pipelines are predictable and can be optimized for cost control—crucial for large-scale applications—they often fall short in dynamic context-gathering scenarios. In contrast, agents leverage their ability to think adaptively, making them particularly suited for intricate and evolving tasks like coding, which have previously been beyond the scope of traditional pipelines. As LLMs continue to evolve, agents may prove to be more resilient and future-proof, suggesting a broader trend towards embracing agent-based architectures in AI applications.