Graph-Based Agentic AI with LangGraph: Workflow Pathways for Long-Running Stateful Business Processes
- source_url: https://arxiv.org/abs/2607.19297
- source_type: paper
- platform: arxiv
- author: Daniel Pearson, Sidney Shapiro, Emiliano Sebastian Gonzalez Venegas, Sanad Al-Khatib, Aurora Pinzón Arzola
- original_date: 2026-07-21
- added_date: 2026-07-23
- category: agents
- tags: langgraph, orchestration, stateful-workflow, rag, hitl, business-process, arxiv
- quality_score: 4
- arxiv_id: 2607.19297
- arxiv_categories: cs.AI, cs.SE
摘要（中文）
面向业务长流程的图工作流实践指南：把 LangGraph 当低层有状态 Agent 编排框架，而非模型质量基准三份可执行配方带修复环的 SQL 分析带证据门控的 agentic RAG带 interrupt/checkpoint 恢复的人在回路策略评审说明 typed state条件路由确定性工具重试中断检查点与 trace 如何把路径/暂停/审计变成显式产品行为按工作流复杂度选型：简单工具调用可用 ReAct/SDK；结构化抽取用 schema-first；提示/程序优化用 DSPy；更复杂状态机再上 LangGrapharXiv:2607.19297，2026-07-21
Summary (English)
Practitioner guide to graph-based workflow pathways for long-running, stateful, multi-step generative AI in business processes. Uses LangGraph as a low-level stateful-agent orchestrator, not a model-quality benchmark: three executable recipesSQL analytics with repair loops; agentic RAG with evidence gating; human-in-the-loop policy review with interrupt/checkpoint recovery. Shows how typed state, conditional routing, deterministic tools, retries, interrupts, checkpoints, and traces make routes/pauses/audit trails explicit product behavior. Positions LangGraph by workflow-complexity fit vs simpler ReAct/SDK loops, schema-first tools, or DSPy for prompt/program optimization. arXiv:2607.19297, 2026-07-21.
One-liner
LangGraph 的价值在把长流程 Agent 的状态、暂停、恢复与审计显式产品化。
Source body / metadata
Fetched via opencli arxiv paper <id> -f json during AAIF content-fetcher backfill. The content note is grounded in the arXiv metadata and abstract.
Abstract
This paper is a practitioner guide to graph-based workflow pathways for long-running, stateful, multi-step generative AI systems in business processes. Rather than treating LangGraph, a low-level orchestration framework for stateful agents, as a model-quality benchmark target, we present three executable recipes -- SQL analytics with repair loops, agentic retrieval-augmented generation with evidence gating, and human-in-the-loop policy review with interrupt and checkpoint recovery -- to show how typed state, conditional routing, deterministic tools, retries, interrupts, checkpoints, and traces fit together. LangGraph is positioned by workflow-complexity fit, not as a universal default: simpler ReAct-style or plain SDK loops may be better for basic tool use, schema-first tools for structured extraction and validation, and DSPy when prompt or program optimization is the main goal. Each recipe explains when LangGraph is worth the extra structure and which implementation patterns make routes, pauses, and audit trails explicit product behavior rather than hidden prompt logic.
Metadata
- arXiv: 2607.19297
- authors: Daniel Pearson, Sidney Shapiro, Emiliano Sebastian Gonzalez Venegas, Sanad Al-Khatib, Aurora Pinzón Arzola
- published: 2026-07-21
- categories: cs.AI, cs.SE
- url: https://arxiv.org/abs/2607.19297