This article provides a systematic decomposition of LLM-based agent systems, covering three essential modules: task planning, tool invocation, and memory management. It explains how agents decompose complex tasks into sub-steps, select and call external tools, and maintain context through short-term and long-term memory. The content is practical for engineers designing autonomous agents but reads as a consolidated overview rather than a novel contribution. Key takeaways include the importance of robust planning strategies, efficient tool orchestration, and memory architectures that balance recall and performance. While not groundbreaking, it serves as a useful reference for teams starting agent development.
A structured breakdown of LLM agent core components—task planning, tool calling, and memory mechanisms—for developers building autonomous agents.