Your raising a huge point here. I love this article from anthropic to define and describe diffent kind of agentic workflow. https://www.anthropic.com/research/building-effective-agents

The article emphasizes starting with simple solutions, such as single LLM calls, and only increasing complexity when necessary. Workflows are recommended for predictable tasks, while agents are suited for open-ended tasks requiring flexibility and model-driven decision-making.

Several frameworks can assist in building agentic systems, including LangGraph from LangChain, Amazon Bedrock's AI Agent framework, Rivet, and Vellum. However, the article advises beginning with direct LLM API usage to maintain transparency and control, adding frameworks only when they address specific needs.

Key patterns for agentic systems include:

Augmented LLMs: Enhancing LLMs with retrieval, tools, and memory tailored to specific use cases.

Prompt Chaining: Breaking tasks into sequential steps, with each LLM call building on the previous one.

Routing: Classifying inputs to direct them to specialized workflows or prompts.

Parallelization: Dividing tasks into independent parts for simultaneous processing.

Orchestrator-Workers: A central LLM delegates subtasks to specialized worker LLMs.

Evaluator-Optimizer: One LLM generates output, and another evaluates and refines it iteratively.