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The author shares practical experience in graph engineering using Orca, demonstrating how to orchestrate tasks through AI agents, including handling long-running task splitting and precondition verification.
Andrew Ng has released a comprehensive 2-hour course on Graph Engineering, covering the progression from single agents to full automation systems.
Graph engineering has become a standard method for building AI agents, with frameworks like LangGraph, CrewAI, and AutoGen highlighted for their use in production by companies such as Uber and LinkedIn.
Google has released a free 1-hour course on Graph Engineering, detailing the transition from single agents to a full 24/7 system with practical guides and self-improving graphs.
This article elaborates on the key concepts in AI engineering, emphasizing that prompts are the core of engineering aspects such as context, tool integration, loops, evaluation, and graphs, and explains the application of these terms in AI Agent development.
The paper introduces ReActNet, a training-free framework that synthesizes task-conditioned temporal workflow graphs for multi-agent LLM systems, enhancing coordination and performance over fixed or learned topology baselines.
Andrew Ng's playbook for AI agents emphasizes that architectural choices like loops and graph engineering are more critical than model selection, outlining stages such as reflection, tools, planning, and state management for building effective systems.
A curated collection of research papers, benchmarks, and open-source projects on Graph Engineering in the era of LLM Agents, accompanying an arXiv survey paper to advance research from individual to system intelligence.
The tweet explains the limitations of spawning multiple AI agents and introduces graph engineering as a technique to enhance coverage and avoid redundancy by strategically managing agent contexts and workflows.
A tweet from @cyrilXBT praises a paper on graph engineering for agentic systems, claiming it significantly improves AI responses, and promotes a spammy bidding website called RankNow.
Anthropic engineers shared insights on building long-running agents using graph engineering in a workshop, highlighting that over 30% of their code is written by agentic graphs to accelerate development.
This paper introduces Graph Engineering as an emerging paradigm for organizing multi-agent LLM systems using dynamic graph structures to coordinate specialized agents and manage complex tasks, advancing from individual to system intelligence.
The Head of Claude Code at Anthropic discusses how 85% of engineers are using AI agents and graph engineering to perform the work of entire teams, highlighting internal practices and future directions.
Explores how multi-layered AI agent delegation affects reliability, arguing that the downstream influence of errors matters more than the number of layers.
Introduces GraphARC, an MIT-licensed open-source tool that lets a model author agent graph topologies at runtime, with a deterministic admission gate for auditable execution, built on LangGraph and running locally via ollama or against cloud APIs.
A tweet claims that Graph Engineering, developed by two Anthropic seniors, has made Karpathy's loop 1000x better by wiring agents into a graph, resulting in significantly improved Claude responses. It also notes that Andrej Karpathy joined Anthropic five weeks ago.
GraphArc is a new open-source tool that transforms AI agent workflows into interactive, real-time graphs, enabling developers to visualize, inspect, debug, and control agent execution before approving actions.
Author ErwinWu000 shares how to implement lightweight Graph Engineering using only Codex's native features, a single prompt, and stage documentation. Through a "general contractor" Agent managing multiple independent Worker tasks, it implements a staged multi-Agent workflow, and provides a ready-to-run template.
GraphArc is an open-source tool that visualizes AI agent workflows as interactive, real-time graphs, enabling users to inspect, debug, and approve agent actions before execution to make agentic AI more explainable and controllable.
A new 20-page paper formalizes 'Graph Engineering' as a replacement for prompt engineering, advocating for building agent graphs (planner → specialists → verifier) evaluated against LangGraph, DSPy, AutoGen, CrewAI, Prompt Flow, and Claude Code.