AI agents are transforming how we build intelligent applications, but their architecture can seem complex. This analysis breaks down the essential pillars that make agents work: the LLM as the reasoning core, tools that extend capabilities, memory for context and learning, and planning to sequence actions. Each pillar plays a distinct role, yet they must work in harmony. For example, memory informs planning, which triggers tool use, all guided by the LLM's reasoning. Understanding these interactions is crucial for developers aiming to build robust agents that can handle real-world tasks. This framework provides a mental model for designing, debugging, and scaling agent systems, whether you're building a simple chatbot or a complex automation pipeline.
Explore the core architecture of AI agents through four key components: LLM reasoning, tool integration, memory systems, and planning strategies. Understand how they synergize to create intelligent autonomous systems.