Agentic Systems: A Practitioner's Guide to 6 Advanced Architectural Patterns

Agentic systems use LLMs to plan, reason, and act with external tools, moving beyond simple conversational AI. Building production-ready agents requires robust architectural patterns, with core components including a reasoning LLM, tools, planning mechanisms, and memory. A critical success factor is 'Context Engineering,' which involves techniques like memory pruning and prompt caching to manage costs and latency. The guide details six advanced patterns, beginning with the foundational ReAct (Reason + Act) loop, an iterative process of thinking and acting that is well-suited for open-ended research tasks.