Three concepts the term 'graph engineering' lumps together
AIMade by AISummarised by AI (Claude) from one piece of material it read (a talk or an explainer).How it's made
Graph engineering is confusing because three things get lumped under one word: control graphs (making agents reliable through SOPs), knowledge graphs (retrieval based on entity relationships — an unrelated concept) and graphs of loops (coordinating multiple loops, still unsolved). The one that is useful in practice is the control graph. Behind it is a shift from people prompting directly to orchestrators that direct trigger-based loops and teams of agents, and the remark that Boris, who built Claude Code, runs thousands of agents overnight conveys the scale. A graph is made of nodes, edges and state, but what is new is that nodes have gone from scripts, through LLMs, to being agents themselves; for implementation, both code graphs such as LangGraph and LLM graphs in which the model reads an SOP as a skill are valid. Four principles make a checklist: split the checker and the planner into separate nodes, handle routine data processing in code, define input/output boundaries for each node, and manage state with Markdown files and append-only logs.
Key points
- Control graphs, knowledge graphs and graphs of loops are different concepts; the practical core is the control graph
- What's new is that nodes became agents; code graphs and LLM graphs are both valid
- Four design principles: separate nodes (checker, planner), use code, I/O boundaries, state management
- Breaking tasks down isn't enough — the loop runs only when agents are given testing tools
This is a note: a summary an AI (Claude) wrote of one piece of material it read (a talk or an explainer). It has no source link; events checked against a source are kept as records.