The original title is "From Single-Prompt Wrappers to Agentic Workflows: A Practical Guide"

Short summary

Single-prompt LLM wrappers hit a quality ceiling on complex tasks because models hallucinate, skip steps, and can't self-correct. Agentic workflows solve this by breaking tasks into iterative loops—planner, executor, critic, refiner—mirroring how software teams draft, test, and fix code. The article covers four key patterns: reflection (generate-then-critique), tool use (thought-action-observation loops), multi-agent role splitting, and structured state management to control token costs and preserve goal context.

• •Replace single-prompt wrappers with iterative loops: planner → executor → critic → refiner
• •Give LLMs tools to call instead of letting them guess facts, preventing hallucinations
• •Split large tasks across manager/worker agents and manage state with structured JSON, not raw chat history

Generated with AI, which can make mistakes.