Graph-Based Agentic AI with LangGraph: Workflow Pathways for Long-Running Stateful Business Processes

• URL: http://arxiv.org/abs/2607.19297v1
• Date: Tue, 21 Jul 2026 17:07:13 GMT
• Title: Graph-Based Agentic AI with LangGraph: Workflow Pathways for Long-Running Stateful Business Processes
• Abstract summary: We present three executable recipes to show how typed state, conditional routing, deterministic tools, retries, interrupts, checkpoints, and traces fit together.<n>LangGraph is positioned by workflow-complexity fit, not as a universal default.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Procedural Graphs: Self-Evolving Execution Structures for LLM Agents [11.810729293911622]
  Procedural Graph organizes procedural knowledge into (procedure, relation, procedure) triplets for what-to-do questions.<n>At each decision step, the framework localizes the agent's active node, and a guidance model translates the surrounding subgraph into step-level situational guidance.<n>The graph is self-evolving: an LLM refiner contrasts failed trajectories with successful ones and edits the graph's topology and attributes.
  arXiv  Detail & Related papers  (2026-09-08T17:59:41Z)
• Inference-Time Graph Engineering for Multi-Agent LLM Workflows [51.56038180639833]
  We introduce ReActNet, a training-free framework that compiles a query and a set of role-specialized agents into a sequence of directed communication graphs.<n>We show that ReActNet consistently improves over fixed-topology and learned-topology baselines while maintaining competitive inference cost.
  arXiv  Detail & Related papers  (2026-09-04T23:32:11Z)
• GTA: Generating Long-Horizon Tasks for Web Agents at Scale [82.43869456830664]
  We introduce a scalable framework, GTA, that integrates crawling, retrieval-based seeding, in-context generation, and automated quality control.<n>We instantiate the pipeline on over 50 websites covering e-commerce, government, forums, and news, with multilingual and multi-hop coverage.<n>Our contributions are three-fold: (i) formalizing multi-hop web-agent task generation, (ii) proposing an efficient and validated pipeline for automatic data creation, and (iii) releasing a dynamic benchmark with reproducible evaluation.
  arXiv  Detail & Related papers  (2026-05-28T01:05:50Z)
• GraphMind: From Operational Traces to Self-Evolving Workflow Automation [8.929269898719822]
  We present GraphMind, a system that constructs, executes, and evolves action-centric workflow graphs with minimal human effort.<n>The system has been deployed across four production cloud database services for incident investigation.<n>A 12-week field study confirms practical value: 97% of scored conversations yield actionable results within interactive latency.
  arXiv  Detail & Related papers  (2026-05-17T19:22:22Z)
• GraphFlow: An Architecture for Formally Verifiable Visual Workflows Enabling Reliable Agentic AI Automation [0.0]
  GraphFlow is a visual workflow system designed to improve the reliability of agentic AI automation in mission-critical processes.<n>Existing workflow platforms provide durable execution and observability but offer few semantic correctness guarantees.<n>A year-long pilot across three clinical sites executed 8,728 cohort-enrolled workflow runs with a 97.08% completion rate.
  arXiv  Detail & Related papers  (2026-05-14T15:33:05Z)
• GraphWalk: Enabling Reasoning in Large Language Models through Tool-Based Graph Navigation [0.2999888908665658]
  We present GraphWalk, a problem-agnostic, training-free, tool-based framework that allows off-the-shelf LLMs to reason through sequential graph navigation.<n>We evaluate whether models equipped with GraphWalk can compose these operations into correct multi-step reasoning chains.
  arXiv  Detail & Related papers  (2026-04-02T04:43:01Z)
• GraphBit: A Graph-based Agentic Framework for Non-Linear Agent Orchestration [34.588097323063074]
  GraphBit is an engine-orchestrated framework that defines explicitly and deterministically as a directed acyclic graph (DAG)<n>Unlike prompted orchestration, agents in GraphBit operate as typed functions, while a Rust-based engine governs routing, state transitions, and tool invocation, ensuring auditability.<n>A three-tier memory architecture consisting of ephemeral scratch space, structured state, and external connectors isolates context across stages, preventing cascading context that degrades reasoning in long-running pipelines.
  arXiv  Detail & Related papers  (2026-03-08T18:32:28Z)
• Semi-supervised Instruction Tuning for Large Language Models on Text-Attributed Graphs [62.544129365882014]
  We propose a novel Semi-supervised Instruction Tuning pipeline for Graph Learning, named SIT-Graph.<n> SIT-Graph is model-agnostic and can be seamlessly integrated into any graph instruction tuning method that utilizes LLMs as the predictor.<n>Extensive experiments demonstrate that when incorporated into state-of-the-art graph instruction tuning methods, SIT-Graph significantly enhances their performance on text-attributed graph benchmarks.
  arXiv  Detail & Related papers  (2026-01-19T08:10:53Z)
• AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction [60.51319139563509]
  We introduce AutoGraph-R1, the first framework to directly optimize KG construction for task performance using Reinforcement Learning (RL)<n>We design two novel, task-aware reward functions, one for graphs as knowledge carriers and another as knowledge indices.<n>Our work shows it is possible to close the loop between construction and application, shifting the paradigm from building intrinsically good'' graphs to building demonstrably useful'' ones.
  arXiv  Detail & Related papers  (2025-10-17T06:03:36Z)
• GraphRunner: A Multi-Stage Framework for Efficient and Accurate Graph-Based Retrieval [3.792463570467098]
  GraphRunner is a novel graph-based retrieval framework that operates in three distinct stages: planning, verification, and execution.<n>It significantly reduces reasoning errors and detects hallucinations before execution.<n>Our evaluation using the GRBench dataset shows that GraphRunner consistently outperforms existing approaches.
  arXiv  Detail & Related papers  (2025-07-11T18:10:01Z)
• Follow the Flow: Fine-grained Flowchart Attribution with Neurosymbolic Agents [106.04963073116468]
  Flowcharts are a critical tool for visualizing decision-making processes.<n> vision-language models frequently hallucinate nonexistent connections and decision paths when analyzing these diagrams.<n>We introduce Fine-grained Flowchart, which traces specific components grounding a flowchart referring LLM response.<n>We propose FlowPathAgent, a neurosymbolic agent that performs fine-grained post hoc attribution through graph-based reasoning.
  arXiv  Detail & Related papers  (2025-06-02T06:02:41Z)
• Plan-over-Graph: Towards Parallelable LLM Agent Schedule [53.834646147919436]
  Large Language Models (LLMs) have demonstrated exceptional abilities in reasoning for task planning.<n>This paper introduces a novel paradigm, plan-over-graph, in which the model first decomposes a real-life textual task into executable subtasks and constructs an abstract task graph.<n>The model then understands this task graph as input and generates a plan for parallel execution.
  arXiv  Detail & Related papers  (2025-02-20T13:47:51Z)
• Large Language Models as Realistic Microservice Trace Generators [48.730974361862366]
  This paper proposes a first-of-a-kind approach that relies on training a large language model (LLM) to generate synthetic workload traces.<n>We show that TraceLLM produces diverse, realistic traces under varied conditions, outperforming existing approaches in both accuracy and validity.<n>TraceLLM adapts to downstream trace-related tasks, such as predicting key trace features and infilling missing data.
  arXiv  Detail & Related papers  (2024-12-16T12:48:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.