Prompt Engineering vs Loop Engineering vs Graph Engineering: What Changes at Each Layer
Anthropic
LangChain
OpenAI
The article distinguishes three layers of AI engineering: prompt engineering (controlling a single model response), loop engineering (controlling an agent's behavior cycle), and graph engineering (organizing multiple agents). Each layer builds on the previous one, and the choice depends on whether a human reviews every output, whether a stop condition can be automated, and whether the task fits one agent or requires parallel branches.
The article explains that prompt engineering, loop engineering, and graph engineering are not competing techniques but stacked layers of control. Prompt engineering handles a single model response and assumes human iteration. Loop engineering adds a cycle of observe-act-verify-recover, making agent behavior programmable. Graph engineering organizes multiple agents via two simultaneous graphs: a stable org graph (who owns what) and an ephemeral work graph (what to do now). The article provides guidance on choosing the layer: if a person reads every output, prompt is sufficient; if "done" can be checked mechanically, a loop can be built; if independent branches must run concurrently, a graph is needed. Key takeaways include that a loop is only as good as its stop condition, and most tasks never need the top layer.
- Сокращения
- MCP = Model Context Protocol — Протокол контекста модели
- XML = Extensible Markup Language — Расширяемый язык разметки
Source: MarkTechPost —
original
