cy0307/awesome-graph-engineering huggingface dataset:
Multi-Agentic System Threat Modeling Guide v1.0 (Docs, OWASP GenAI Security Project, 2025)
Authors: OWASP GenAI Security Project
Applies an agentic threat taxonomy to multi-agent architectures and examines attack surfaces introduced by delegation, messaging, shared memory, tools, and cross-agent actions.
Why it matters: Provides a structured way to identify graph-specific trust boundaries and abuse paths; it is community security guidance rather than a certification or formal standard.
AgentOps: Operationalize agentic AI at scale with Amazon Bedrock AgentCore (Blog, AWS Machine Learning Blog, 2026)
Authors: Anastasia Tzeveleka; Anna Grüebler; Antonio Rodriguez; Sergio Garcés Vitale; Aris Tsakpinis
Organizes production agent operations around governance, build and release, multi-level evaluation, observability, identity propagation, and cost attribution in a reference architecture.
Why it matters: Adds concrete system measures such as orchestration accuracy and exchange quality while keeping the vendor-specific implementation context visible.
Powering agentic AI sales strategy with Amazon Bedrock AgentCore (Blog, AWS Machine Learning Blog, 2026)
Authors: Nicolle Belaunde; Umesh Mohan; Xinrui Nie
Describes an internal production system with more than 20 domain agents, supervisor routing, OAuth propagation, MCP tools, approvals, memory, distributed tracing, and evaluation.
Why it matters: Offers a rare detailed account of an enterprise supervisor graph at operating scale; its usage, latency, and outcome figures are self-reported by AWS.
Designing Autonomous AI Agents: How We Built a Multi-Agent Swarm for UI Development (Blog, EPAM Insights, 2026)
Authors: Stanislau Shandrokha; Pavel Golub; Andrey Voroshkov
Describes a seven-agent state-machine workflow with resumable state, explicit roles, adversarial visual and code reviewers, and verification gates for interface development.
Why it matters: Reports practical failures such as overlapping work, skipped steps, and rubber-stamping alongside mitigations; the evidence is a narrow, self-reported implementation account.
FIPA ACL Message Structure Specification (Standard, Foundation for Intelligent Physical Agents, 2002)
Authors: FIPA TC Communication; FIPA Architecture Board
Defines interoperable agent messages with performatives, sender and receiver, ontology, protocol, conversation identifiers, reply correlation, and deadlines.
Why it matters: Supplies the historical standards basis for message-envelope and conversation concepts that reappear in current agent handoff protocols.
Red-teaming a network of agents: Understanding what breaks when AI agents interact at scale (Blog, Microsoft Research Blog, 2026)
Authors: Gagan Bansal; Shujaat Mirza; Keegan Hines; Will Epperson; Zachary Huang; Whitney Maxwell; Pete Bryan; Tyler Payne; Adam Fourney; Amanda Swearngin; Wenyue Hua; Tori Westerhoff; Amanda Minnich; Maya Murad; Ece Kamar; Ram Shankar Siva Kumar; Saleema Amershi
Reports a red-team exercise on a live internal platform with more than 100 persistent agents and identifies propagation, amplification, trust capture, and invisibility failures.
Why it matters: Demonstrates interaction risks that do not appear in isolated-agent tests; the environment and findings are company-reported rather than independently reproduced.
HyperAgent: Leveraging Hypergraphs for Topology Optimization in Multi-Agent Communication (Paper, AAMAS, 2026)
Authors: Heng Zhang; Yuling Shi; Xiaodong Gu; Zijian Zhang; Haochen You; Lubin Gan; Yilei Yuan; Jin Huang
Represents collaboration groups as hyperedges and uses a variational hypergraph autoencoder with sparsity regularization to generate task-adaptive communication topology.
Why it matters: Adds group-level topology learning beyond pairwise edges and differs from evolving-hypergraph work by focusing on generated sparse structures for each task.
D³MAS: Decompose, Deduce, and Distribute for Enhanced Knowledge Sharing in Multi-Agent Systems (Paper, AAMAS, 2026)
Authors: Heng Zhang; Yuling Shi; Xiaodong Gu; Haochen You; Zijian Zhang; Lubin Gan; Yilei Yuan; Jin Huang
Connects task decomposition, collaborative reasoning, and distributed memory as coordinated layers in a heterogeneous graph designed to reduce duplicated retrieval, inference, and assignment.
Why it matters: Unifies work, reasoning, and state dependencies in one inspectable organization rather than optimizing those coordination surfaces separately.
µACP: A Formal Calculus for Expressive, Resource-Constrained Agent Communication (Paper, AAMAS, 2026)
Authors: Arnab Mallick; Indraveni Chebolu
Formalizes resource-bounded agent communication with PING, TELL, ASK, and OBSERVE operations, proves finite-state expressiveness and message bounds, and checks properties with TLA+ and Coq.
Why it matters: Provides a precise substrate for reasoning about protocol behavior and communication budgets; it is a general multi-agent calculus rather than an LLM-specific runtime.
Multi-Agent Collaboration via Evolving Orchestration (Paper, NeurIPS, 2025)
Authors: Yufan Dang; Chen Qian; Xueheng Luo; Jingru Fan; Zihao Xie; Ruijie Shi; Weize Chen; Cheng Yang; Xiaoyin Che; Ye Tian; Xuantang Xiong; Lei Han; Zhiyuan Liu; Maosong Sun
Trains a centralized orchestrator with reinforcement learning to select and sequence agents from evolving task state, producing dynamic inference graphs and compact cyclic reasoning paths.
Why it matters: Learns runtime activation and ordering rather than fixing a workflow in advance, providing a centralized contrast to decentralized evolutionary coordination.