Cognitive architectures are essential for AI reasoning, encompassing the system's "thinking" process involving code, prompts, and LLM calls to handle input and generate actions or responses. AI assistants operate on a spectrum of autonomy, ranging from simple single LLM calls to complex autonomous agents, with higher autonomy demanding robust infrastructure and careful design. Agentic infrastructure supports AI development by providing tools and features for building and running agentic systems, though this should not be confused with the cognitive architecture itself. For sophisticated AI, tailored cognitive architectures are crucial, as complex agents require custom architectures tailored to their specific state management and control flows for optimal performance and reliability, whereas generic architectures might suffice for simpler tasks.
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