Agentic AI system components for production

Agentic AI systems are designed to operate autonomously and achieve goals without explicit instructions, making them distinct from traditional scripted AI systems. These systems rely on five core components: perception, reasoning, memory, action, and feedback loops, all working in continuous cycles to handle tasks dynamically and adaptively. The perception module processes raw data into structured context, while the reasoning engine uses patterns like ReAct and Tree-of-Thoughts for decision-making. Memory systems manage various types of information, ensuring context is maintained across interactions, and the action module bridges reasoning to real-world execution, using dynamic decisions and structured function calls. Feedback loops allow agents to learn and adapt through reflection and tool-driven feedback, enhancing performance over time. Infrastructure choices, including GPU compute and event streaming platforms, are critical for production-ready systems, and unified platforms like Redis offer a consolidated solution with capabilities such as vector search, semantic caching, and message queuing, reducing latency and operational complexity while supporting efficient agent deployment.

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