Graph-centric agentic intelligence

Graph-centric agentic intelligence - Amazon Science

Close

Close

Social

bluesky

threads

twitter

instagram

youtube

facebook

linkedin

github

rss

Menu

Research

Research areas

Automated reasoning

Cloud and systems

Computer vision

Conversational AI

Economics

Information and knowledge management

Machine learning

Operations research and optimization

Quantum technologies

Robotics

Search and information retrieval

Security, privacy, and abuse prevention

Sustainability

Our scientific contributions

Publications

Research from our scientists and collaborators.

Conferences

Our experts present and discuss cutting-edge research at scientific meetings globally.

Research areas

Automated reasoning

Cloud and systems

Computer vision

Conversational AI

Economics

Information and knowledge management

Machine learning

Operations research and optimization

Quantum technologies

Robotics

Search and information retrieval

Security, privacy, and abuse prevention

Sustainability

Our scientific contributions

Publications

Research from our scientists and collaborators.

Conferences

Our experts present and discuss cutting-edge research at scientific meetings globally.

News & blog

The latest from Amazon researchers

Amazon Science Blog

Technical deep-dives and perspectives from our scientists.

News

Research milestones and recent achievements.

The latest from Amazon researchers

Amazon Science Blog

Technical deep-dives and perspectives from our scientists.

News

Research milestones and recent achievements.

Collaborations

Amazon Research Awards

Overview

Call for proposals

Latest news

Research stories

Recipients

Amazon Nova AI Challenge

Overview

Rules

FAQs

Teams

Research collaborations

Overview

Carnegie Mellon University

Columbia University

Hampton University

Howard University

IIT Bombay

Johns Hopkins University

Max Planck Society

MIT

Tennessee State University

University of California, Los Angeles

University of Illinois Urbana-Champaign

University of Southern California

University of Texas at Austin

Virginia Tech

University of Washington

Amazon Research Awards

Overview

Call for proposals

Latest news

Research stories

Recipients

Amazon Nova AI Challenge

Overview

Rules

FAQs

Teams

Research collaborations

Overview

Carnegie Mellon University

Columbia University

Hampton University

Howard University

IIT Bombay

Johns Hopkins University

Max Planck Society

MIT

Tennessee State University

University of California, Los Angeles

University of Illinois Urbana-Champaign

University of Southern California

University of Texas at Austin

Virginia Tech

University of Washington

Resources

Code and datasets

Amazon Nova

Try Amazon’s frontier foundation models.

Code and datasets

Amazon Nova

Try Amazon’s frontier foundation models.

Careers

Careers

Explore our open roles.

Amazon Scholars

Faculty research opportunities on industry-scale technical challenges.

Postdoctoral Science Program

Early-career research opportunities alongside experienced industry scientists.

Careers

Explore our open roles.

Amazon Scholars

Faculty research opportunities on industry-scale technical challenges.

Postdoctoral Science Program

Early-career research opportunities alongside experienced industry scientists.

Search

Submit Search

Cloud and systems

Graph-centric agentic intelligence

Augmenting a network graph with agentic AI produces a “digital twin” that can help isolate network failures.

By Imen Grida Ben Yahya , Nameet Dutia

October 1, 2026

10 min read

Share

Share

Copy link

Email

X

LinkedIn

Facebook

Line

Reddit

QZone

Sina Weibo

WeChat

WhatsApp

分享到微信

x

Graphs encode relationships. In a world where AI agents need to reason about complex systems, not just retrieve information, graph structure provides the scaffolding for causal inference, dependency tracking, and compositional reasoning. Unlike tabular or unstructured data, graphs preserve the topology of real-world systems: what connects to what and how influence flows. For networks, this is not a metaphor. Networks are natively graphs. Every device, link, open communication channel, and service dependency is a vertex or edge in a structure that can span millions of elements. The question facing operational teams today is not whether to represent networks as graphs but how to let AI agents reason over that structure autonomously, adaptively, and at speed. This post traces how graph intelligence evolved across scientific research on networks, from passive modeling to active agentic reasoning, and demonstrates agentic reasoning through cascaded graph analytics for root cause identification. For the solution architecture and a guide to common routines (a runbook), see our companion post, “ Beyond correlation: Finding root causes using a network digital twin graph and agentic AI ”.

Graphs for networks

The earliest use of graphs for networks was to represent topology — to model physical connectivity so that operators could compute paths and isolate failures in seconds rather than hours. Knowledge graphs and ontologies enhanced topological models by adding semantics, defining, for instance, what a "cell", an "SLA breach", or an "escalation procedure" is and how these concepts relate to each other across vendors and network generations. This enabled machine-readable operations and multivendor interoperability. Once the graph carried both structure and semantics, the next step was encoding causation. Alarm correlation graphs, which map relationships among network-generated alerts about phenomena like lost connections or network breaches, introduced hierarchical relationships between network events. Walking upstream through the graph condensed tens of thousands of raw alarms into a single causal chain within minutes, a significant advance over manual correlation but one that required predefined alarm hierarchies. Dependency graphs removed that requirement: autogenerated in real time from software-defined-networking (SDN) and network functions virtualization (NFV) controllers, they enabled Bayesian fault localization at 95% accuracy in under 30 seconds with no manually authored rules. Causal subgraphs answered not just where but why. Live alarms converted into directed acyclic graphs revealed the propagation paths of key performance indicators (KPIs), giving operators evidence rather than predictions. Graph neural networks (GNNs), which produce vector representations of...