AI workflows vs. AI agents

When building applications with large language models (LLMs), it’s tempting to reach for complex frameworks or fully autonomous agents. But Anthropic’s recent reflections show that simpler, well-structured systems often outperform heavyweight setups. A key takeaway: success doesn’t come from sophistication, but from clarity and fit-for-purpose design.

One major distinction Anthropic highlights is between workflows and agents:
Workflows follow predefined, reliable paths and “offer predictability and consistency for well-defined tasks”. Agents, in contrast, act dynamically, choosing their own tools and planning execution in real time and “are the better option when flexibility and model-driven decision-making are needed at scale”. This flexibility is powerful, but it comes with increased cost, latency, and the risk of cascading errors.

Simplicity vs. Frameworks

Crucially, developers are often better off starting without frameworks. While tools like LangGraph or Rivet help prototype quickly, they can obscure important details like prompt logic and make debugging harder. Many effective workflows can be built with just a few lines of code using raw LLM APIs. Manual implementation not only gives more control, but also encourages better understanding and fewer surprises.

Ultimately, whether using prompt chaining, routing, or autonomous agents, Anthropic advises adding complexity only when simpler methods no longer suffice. Build lean, measure impact, and layer complexity deliberately—not automatically.

Langgraph – If you still want to go the way with a framework

Source:
https://www.anthropic.com/engineering/building-effective-agents