Agentic systems vs. GenAI: when generation isn't enough

Generative AI (GenAI) and agentic systems represent two distinct approaches in AI architecture, each suited to different tasks. GenAI, built on foundational models like transformers, excels in generating new content such as text, images, or code, using a prediction loop that powers applications like chatbots and code completion. However, it operates statelessly, often requiring retrieval-augmented generation to ground responses in real data. Agentic systems, on the other hand, extend beyond single-call patterns to involve planning, acting, and adapting across multiple steps, making them ideal for workflows that require state changes rather than mere content generation. These systems rely on components like reasoning models, tools for interacting with external systems, and persistent memory to maintain state across sessions. The shift from GenAI to agentic systems introduces additional complexity and infrastructure requirements, such as multi-tier memory storage, hybrid retrieval, semantic caching, and event-driven coordination, which platforms like Redis can support. The decision to use GenAI or agentic systems hinges on whether the task fundamentally involves generating content or managing state changes, with the latter necessitating the investment in agentic systems for more complex, autonomous workflows.

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