Most LLM Apps Need Workflows Not Agent Frameworks
Most LLM apps ship faster and more reliably as deterministic workflows than autonomous agents. Plain Python with structured outputs and local functions beats CrewAI and LangGraph for debugging. Map control flow in code before importing any agent framework.
Most production LLM apps deliver better reliability as deterministic workflows than as autonomous agents. Instead of letting models dynamically plan and tool-call, define explicit control flow graphs where code owns the path and LLMs act as bounded reasoning nodes inside specific steps. Plain Python with structured outputs and local functions often outperforms CrewAI or LangGraph for transparency. Before importing a heavy framework, map your problem as a graph with deterministic edges and logic owned by your code.
Read the original → Towards Data Science
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