Sync 109-resource atlas from GitHub

resources.csv

@@ -4,20 +4,20 @@ age-0002,Start Here,Architecture guide,Blog,Building effective agents,https://ww
 age-0003,Research Foundations,Agent foundations,Paper,Agent-Oriented Programming,https://doi.org/10.1016/0004-3702(93)90034-9,Artificial Intelligence,1993,Yoav Shoham,Defines a programming paradigm in which agents are first-class components described through mental state and governed by explicit interaction rules.,Establishes the intellectual lineage for treating an agent role as a programmable organizational unit.,Peer-reviewed research,Roles
 age-0004,Research Foundations,Agent foundations,Paper,Intelligent Agents: Theory and Practice,https://doi.org/10.1017/S0269888900008122,The Knowledge Engineering Review,1995,Michael Wooldridge; Nicholas R. Jennings,"Surveys the properties, architectures, and engineering approaches that distinguish autonomous agents from ordinary software modules.",Grounds the agency-at-the-nodes boundary that separates an agent graph from a deterministic workflow.,Peer-reviewed research,Roles
 age-0005,Research Foundations,Shared-state architectures,Paper,The Blackboard Model of Problem Solving and the Evolution of Blackboard Architectures,https://doi.org/10.1609/aimag.v7i2.537,AI Magazine,1986,H. Penny Nii,Describes systems in which independent specialists coordinate opportunistically through a shared problem state and a control component.,Supplies a durable model for shared state without requiring every node to exchange its full context directly.,Peer-reviewed research,State
-age-0006,Research Foundations,Learned communication,Paper,Learning to Communicate with Deep Multi-Agent Reinforcement Learning,https://proceedings.neurips.cc/paper_files/paper/2016/hash/c7635bfd99248a2cdef8249ef7bfbef4-Abstract.html,NeurIPS,2016,Jakob Foerster et al.,"Introduces reinforcement-learning methods that let agents learn communication protocols alongside their task policies, including discrete messages for execution.",Shows that edge content and communication policy can be engineered or learned rather than treated as free-form chat.,Peer-reviewed research,Handoffs
+age-0006,Research Foundations,Learned communication,Paper,Learning to Communicate with Deep Multi-Agent Reinforcement Learning,https://arxiv.org/abs/1605.06676,NeurIPS,2016,Jakob Foerster et al.,"Introduces reinforcement-learning methods that let agents learn communication protocols alongside their task policies, including discrete messages for execution.",Shows that edge content and communication policy can be engineered or learned rather than treated as free-form chat.,Peer-reviewed research,Handoffs
 age-0007,Research Foundations,Learned communication,Paper,TarMAC: Targeted Multi-Agent Communication,https://proceedings.mlr.press/v97/das19a.html,ICML,2019,Abhishek Das et al.,Uses attention to let agents address different messages to selected recipients instead of broadcasting the same information to the whole team.,"Motivates selective, recipient-aware handoffs when all-to-all communication is wasteful or distracting.",Peer-reviewed research,Handoffs
 age-0008,Research Foundations,LLM multi-agent systems,Paper,AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation,https://arxiv.org/abs/2308.08155,COLM,2024,Qingyun Wu et al.,"Presents a framework for composing customizable conversational agents that can combine language models, tools, code execution, and human input.","An early, influential demonstration that agent roles and conversation links can be expressed as an executable topology.",Peer-reviewed research,Topology
 age-0009,Research Foundations,Topology optimization,Paper,GPTSwarm: Language Agents as Optimizable Graphs,https://proceedings.mlr.press/v235/zhuge24a.html,ICML,2024,Mingchen Zhuge et al.,Represents language-agent systems as computational graphs and optimizes graph components from task feedback.,Makes the graph itself an optimization target rather than a fixed orchestration diagram.,Peer-reviewed research,Evolution
 age-0010,Research Foundations,Dynamic topology,Paper,A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration,https://openreview.net/forum?id=XII0Wp1XA9,COLM,2024,Zijun Liu et al.,Constructs task-specific collaboration networks that can vary which agents participate and how they communicate instead of relying on one fixed team.,Provides evidence for adapting the work graph to the task while keeping the available agent roles reusable.,Peer-reviewed research,Evolution
-age-0011,Research Foundations,Communication efficiency,Paper,Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems,https://proceedings.iclr.cc/paper_files/paper/2025/hash/bbc461518c59a2a8d64e70e2c38c4a0e-Abstract-Conference.html,ICLR,2025,Guibin Zhang et al.,Studies an economical communication pipeline that reduces redundant information exchanged among language-model agents.,Treats edge traffic as a measurable cost and tests whether less communication can preserve useful collaboration.,Peer-reviewed research,Observability & cost
+age-0011,Research Foundations,Communication efficiency,Paper,Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems,https://openreview.net/forum?id=LkzuPorQ5L,ICLR,2025,Guibin Zhang et al.,Studies an economical communication pipeline that reduces redundant information exchanged among language-model agents.,Treats edge traffic as a measurable cost and tests whether less communication can preserve useful collaboration.,Peer-reviewed research,Observability & cost
 age-0012,Research Foundations,Topology optimization,Paper,G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks,https://proceedings.mlr.press/v267/zhang25cu.html,ICML,2025,Guibin Zhang et al.,Uses graph neural networks to design communication structures for multi-agent systems instead of assuming a complete or manually chosen graph.,Connects task performance to explicit topology search and exposes communication structure as an engineering variable.,Peer-reviewed research,Evolution
-age-0013,Research Foundations,System search,Paper,Automated Design of Agentic Systems,https://proceedings.iclr.cc/paper_files/paper/2025/hash/36b7acf6f6010652b3f2a433774a66fe-Abstract-Conference.html,ICLR,2025,Shengran Hu; Cong Lu; Jeff Clune,"Uses a meta-agent to propose, evaluate, and iteratively improve code-defined agentic systems across tasks.",Demonstrates automated search over coordination logic while retaining executable artifacts that engineers can inspect.,Peer-reviewed research,Evolution
+age-0013,Research Foundations,System search,Paper,Automated Design of Agentic Systems,https://openreview.net/forum?id=t9U3LW7JVX,ICLR,2025,Shengran Hu; Cong Lu; Jeff Clune,"Uses a meta-agent to propose, evaluate, and iteratively improve code-defined agentic systems across tasks.",Demonstrates automated search over coordination logic while retaining executable artifacts that engineers can inspect.,Peer-reviewed research,Evolution
 age-0014,Research Foundations,Workflow search,Paper,AFlow: Automating Agentic Workflow Generation,https://openreview.net/forum?id=z5uVAKwmjf,ICLR,2025,Jiayi Zhang et al.,Searches over reusable workflow operators to generate task-specific agentic workflows and improve them from evaluation results.,Offers a concrete method for evolving work graphs against measurable objectives rather than intuition alone.,Peer-reviewed research,Evolution
 age-0015,Research Foundations,Joint optimization,Paper,Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies,https://openreview.net/forum?id=I05H9RUzHB,ICLR,2026,Han Zhou et al.,Studies joint optimization of agent prompts and communication topology instead of tuning either component in isolation.,Shows that node behavior and graph structure interact and may need to evolve together.,Peer-reviewed research,Evolution
 age-0016,Research Foundations,Debate and councils,Paper,Improving Factuality and Reasoning in Language Models through Multiagent Debate,https://proceedings.mlr.press/v235/du24e.html,ICML,2024,Yilun Du et al.,Tests rounds of proposal and critique among multiple language-model instances as a way to improve factual and reasoning answers.,Supplies an empirical basis for debate-style gates while leaving room to examine correlated errors and added cost.,Peer-reviewed research,Gates
 age-0017,Research Foundations,Debate and councils,Paper,Improving Multi-Agent Debate with Sparse Communication Topology,https://aclanthology.org/2024.findings-emnlp.427/,Findings of EMNLP,2024,Yunxuan Li et al.,Examines multi-agent debate under sparse communication structures rather than defaulting to full information exchange among every participant.,Isolates topology as a factor in debate quality and communication efficiency.,Peer-reviewed research,Topology
-age-0018,Research Foundations,Feedback and memory,Paper,Reflexion: Language Agents with Verbal Reinforcement Learning,https://proceedings.neurips.cc/paper_files/paper/2023/hash/1b44b878bb782e6954cd888628510e90-Abstract-Conference.html,NeurIPS,2023,Noah Shinn et al.,Lets an agent convert feedback into textual reflections stored in episodic memory and reused on later attempts.,Clarifies how a node loop can persist learning across retries without changing model weights.,Peer-reviewed research,State
-age-0019,Research Foundations,Tool-grounded verification,Paper,CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing,https://proceedings.iclr.cc/paper_files/paper/2024/hash/fef126561bbf9d4467dbb8d27334b8fe-Abstract-Conference.html,ICLR,2024,Zhibin Gou et al.,Uses external tools to obtain feedback that a language model can apply when critiquing and revising its outputs.,Supports verification gates grounded in observable evidence instead of another ungrounded model opinion.,Peer-reviewed research,Gates
+age-0018,Research Foundations,Feedback and memory,Paper,Reflexion: Language Agents with Verbal Reinforcement Learning,https://openreview.net/forum?id=vAElhFcKW6,NeurIPS,2023,Noah Shinn et al.,Lets an agent convert feedback into textual reflections stored in episodic memory and reused on later attempts.,Clarifies how a node loop can persist learning across retries without changing model weights.,Peer-reviewed research,State
+age-0019,Research Foundations,Tool-grounded verification,Paper,CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing,https://openreview.net/forum?id=Sx038qxjek,ICLR,2024,Zhibin Gou et al.,Uses external tools to obtain feedback that a language model can apply when critiquing and revising its outputs.,Supports verification gates grounded in observable evidence instead of another ungrounded model opinion.,Peer-reviewed research,Gates
 age-0020,Production Case Studies,Generalist agent team,Paper,Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks,https://www.microsoft.com/en-us/research/publication/magentic-one-a-generalist-multi-agent-system-for-solving-complex-tasks/,Microsoft Research MSR-TR-2024-47,2024,Adam Fourney et al.,"Documents an orchestrator-led team of specialized agents for web, file, coding, and terminal tasks, with progress tracking and replanning.",Provides a concrete generalist topology whose role boundaries and recovery behavior can be examined as a system.,Research preprint,Roles
 age-0021,Production Case Studies,Parallel research,Blog,How we built our multi-agent research system,https://www.anthropic.com/engineering/multi-agent-research-system,Anthropic Engineering,2025,Jeremy Hadfield et al.,"Describes a lead research agent that creates parallel subagents, delegates searches, and synthesizes their findings, including operational lessons from production.","A detailed case study of dynamic fan-out and fan-in, context separation, evaluation, and the token cost of an adaptive work graph.",Practitioner analysis,Work graphs
 age-0022,Frameworks & SDKs,Role-based teams,Docs,CrewAI Crews,https://docs.crewai.com/en/concepts/crews,CrewAI Documentation,2026,CrewAI,"Documents role-based agent crews, assigned tasks, delegation, and sequential or hierarchical execution processes.",Offers accessible primitives for testing explicit ownership and manager-worker coordination.,Official documentation,Roles
@@ -46,7 +46,7 @@ age-0044,Observability & Cost,Cost attribution,Docs,Arize Phoenix: Cost Tracking
 age-0045,Observability & Cost,Observability platform,Docs,LangSmith Observability,https://docs.langchain.com/langsmith/observability,LangSmith Documentation,2026,LangChain,"Documents tracing, dashboards, alerts, feedback, and evaluation views for language-model and agent applications.",Provides operational views for following execution across nodes and locating failures on the critical path.,Official documentation,Observability & cost
 age-0046,Benchmarks & Datasets,Collaboration benchmark,Benchmark,MultiAgentBench: Evaluating Collaboration and Competition of LLM Agents,https://aclanthology.org/2025.acl-long.421/,ACL,2025,Kunlun Zhu et al.,Benchmarks language-model agents in collaborative and competitive settings while examining coordination processes as well as task outcomes.,Measures properties of the team interaction that single-agent benchmarks cannot expose.,Benchmark/dataset,Observability & cost
 age-0047,Benchmarks & Datasets,Failure diagnosis,Benchmark,Why Do Multi-Agent LLM Systems Fail?,https://nips.cc/virtual/2025/poster/121528,NeurIPS Datasets & Benchmarks,2025,Mert Cemri et al.,Provides a structured taxonomy and evaluation approach for diagnosing coordination failures in multi-agent language-model systems.,"Turns reliability incidents into recurring, attributable failure classes that can guide graph redesign.",Benchmark/dataset,Reliability
-age-0048,Critiques & Limits,Self-correction limits,Paper,Large Language Models Cannot Self-Correct Reasoning Yet,https://proceedings.iclr.cc/paper_files/paper/2024/hash/8b4add8b0aa8749d80a34ca5d941c355-Abstract-Conference.html,ICLR,2024,Jie Huang et al.,Finds that intrinsic self-correction without reliable external feedback often fails to improve reasoning and can reduce accuracy.,Warns against using an ungrounded critic node as evidence simply because it is separate from the producing node.,Peer-reviewed research,Gates
+age-0048,Critiques & Limits,Self-correction limits,Paper,Large Language Models Cannot Self-Correct Reasoning Yet,https://openreview.net/forum?id=IkmD3fKBPQ,ICLR,2024,Jie Huang et al.,Finds that intrinsic self-correction without reliable external feedback often fails to improve reasoning and can reduce accuracy.,Warns against using an ungrounded critic node as evidence simply because it is separate from the producing node.,Peer-reviewed research,Gates
 age-0049,Critiques & Limits,Scaling evidence,Paper,Towards a Science of Scaling Agent Systems,https://arxiv.org/abs/2512.08296,arXiv; Google Research,2025,Yubin Kim et al.,"Studies how agent-system performance changes across tasks, models, coordination structures, and scaling choices under controlled experiments.",Tests the assumption that adding agents reliably helps and frames scaling as an empirical topology decision.,Research preprint,Topology
 age-0050,Critiques & Limits,Token-budget comparison,Paper,Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets,https://arxiv.org/abs/2604.02460,arXiv,2026,Dat Tran; Douwe Kiela,Compares single-agent and multi-agent approaches to multi-hop reasoning while holding the total thinking-token budget constant.,"Provides the cost-controlled baseline needed before claiming that coordination, rather than extra inference, caused an improvement.",Research preprint,Topology
 age-0051,Start Here,Contemporary framing,Blog,From Loop Engineering to Graph Engineering?,https://x.com/IntuitMachine/status/2078419526354378975,X Articles,2026,Carlos E. Perez,"Frames the shift as loop architecture: networks of improvement cycles that monitor, feed, constrain, and correct one another, with reliability located in their edges.","Adds the grounding requirement missing from topology-only accounts: independent counter-metrics, frozen tests or rules, external anchors, and human ownership of root objectives.",Practitioner analysis,Gates
@@ -79,3 +79,32 @@ age-0077,Benchmarks & Datasets,Failure attribution,Benchmark,Seeing the Whole El
 age-0078,Production Case Studies,Secure delegated access,Blog,Creating AI agent solutions for warehouse data access and security,https://engineering.fb.com/2025/08/13/data-infrastructure/agentic-solution-for-warehouse-data-access/,Engineering at Meta,2025,Can Lin; Uday Ramesh Savagaonkar; Iuliu Rus; Komal Mangtani,"Describes collaborating data-user and data-owner agents, specialized subagents, triage, permission negotiation, human oversight, access budgets, analytical risk rules, output guardrails, traces, and daily regression evaluation.",Shows plural bounded agency governed by independent rule-based gates rather than relying on model judgment for security decisions.,Practitioner analysis,Gates
 age-0079,Production Case Studies,Staged agent swarm,Blog,How Meta Used AI to Map Tribal Knowledge in Large-Scale Data Pipelines,https://engineering.fb.com/2026/04/06/developer-tools/how-meta-used-ai-to-map-tribal-knowledge-in-large-scale-data-pipelines/,Engineering at Meta,2026,Krishna Ganeriwal; Plawan Rath; Ashwini Verma,"Reports a staged swarm of more than 50 explorer, analyst, writer, critic, fixer, upgrader, tester, and gap-filling tasks, with repeated review rounds and recurring automated refresh runs.","Provides a concrete fan-out, critique, repair, integration, and recurrence graph with explicit artifacts and quality gates; efficiency figures are company-reported and preliminary.",Practitioner analysis,Work graphs
 age-0080,Production Case Studies,Multi-hop identity and provenance,Blog,Solving the Identity Crisis for AI Agents,https://www.uber.com/by/en/blog/solving-the-agent-identity-crisis/,Uber Engineering,2026,Matt Mathew; Prasad Borole; Meng Huang; Sergey Burykin; Gaurav Goel; Bayard Walsh,"Describes an internal agent mesh with registered identities, SPIRE-backed workload attestation, short-lived audience-scoped tokens for every hop, actor-chain provenance, MCP gateway enforcement, and a standardized A2A client.","Treats identity, delegated authority, and provenance as mandatory edge state across a multi-agent graph; adoption and latency metrics are company-reported.",Practitioner analysis,Handoffs
+age-0081,Research Foundations,Task-adaptive topology synthesis,Paper,Dynamic Generation of Multi LLM Agents Communication Topologies with Graph Diffusion Models,https://aclanthology.org/2026.acl-long.1764/,ACL,2026,Eric Hanchen Jiang et al.,"Introduces Guided Topology Diffusion, which iteratively synthesizes sparse, task-adaptive communication graphs using a proxy model for objectives such as accuracy, utility, and cost.","Treats topology as a multi-objective, per-task design artifact rather than a static collaboration template.",Peer-reviewed research,Evolution
+age-0082,Reliability & Durable Execution,Topology-conditioned memory leakage,Paper,Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs,https://aclanthology.org/2026.findings-acl.1980/,Findings of ACL,2026,Jinbo Liu et al.,"Measures private-information leakage across six communication topologies, agent counts, and attacker-target placements, finding higher leakage with denser connectivity, shorter graph distance, and greater target centrality.","Makes privacy a measurable consequence of edge structure and node placement, motivating topology-aware access controls.",Peer-reviewed research,Reliability
+age-0083,Critiques & Limits,Collaboration-induced diversity collapse,Paper,Diversity Collapse in Multi-Agent LLM Systems: Structural Coupling and Collective Failure in Open-Ended Idea Generation,https://aclanthology.org/2026.findings-acl.13/,Findings of ACL,2026,Nuo Chen et al.,"Studies multi-agent ideation across model, cognition, and system levels, finding diminishing group-size returns, authority effects, and faster premature convergence under dense communication.","Shows that interaction can contract a search space, so graph designs for exploration must preserve independence and meaningful disagreement.",Peer-reviewed research,Topology
+age-0084,Research Foundations,Dynamic node and edge elimination,Paper,AgentDropout: Dynamic Agent Elimination for Token-Efficient and High-Performance LLM-Based Multi-Agent Collaboration,https://aclanthology.org/2025.acl-long.1170/,ACL,2025,Zhexuan Wang; Yutong Wang; Xuebo Liu; Liang Ding; Miao Zhang; Jie Liu; Min Zhang,"Optimizes communication-graph adjacency matrices across collaboration rounds to remove redundant agents and messages, reducing both prompt and completion token consumption in its evaluations.",Makes node participation and edge density explicit cost-quality decisions instead of assuming every role should run on every task.,Peer-reviewed research,Evolution
+age-0085,Benchmarks & Datasets,Process-level collaboration,Benchmark,Collab-Overcooked: Benchmarking and Evaluating Large Language Models as Collaborative Agents,https://aclanthology.org/2025.emnlp-main.249/,EMNLP,2025,Haochen Sun; Shuwen Zhang; Lujie Niu; Lei Ren; Hao Xu; Hao Fu; Fangkun Zhao; Caixia Yuan; Xiaojie Wang,"Provides 30 open-ended cooperative tasks and process-oriented measures for goal interpretation, active collaboration, communication, and continuous adaptation across 13 language models.","Distinguishes reaching an answer from coordinating well, exposing collaboration failures that final-outcome metrics can hide.",Benchmark/dataset,Observability & cost
+age-0086,Critiques & Limits,Inter-agent communication attacks,Paper,Red-Teaming LLM Multi-Agent Systems via Communication Attacks,https://aclanthology.org/2025.findings-acl.349/,Findings of ACL,2025,Pengfei He; Yuping Lin; Shen Dong; Han Xu; Yue Xing; Hui Liu,"Introduces an Agent-in-the-Middle attack that intercepts and manipulates inter-agent messages, evaluated across multiple frameworks, communication structures, and applications.","Shows that graph edges can compromise the whole system without altering its nodes, making message integrity and provenance part of the handoff contract.",Peer-reviewed research,Handoffs
+age-0087,Research Foundations,Query-dependent architecture search,Paper,Multi-agent Architecture Search via Agentic Supernet,https://proceedings.mlr.press/v267/zhang25bi.html,ICML,2025,Guibin Zhang; Luyang Niu; Junfeng Fang; Kun Wang; Lei Bai; Xiang Wang,"Represents possible agentic architectures as a probabilistic supernet and samples query-dependent systems with tailored language-model calls, tool calls, and token costs across six benchmarks.",Replaces a one-size-fits-all graph with per-query architecture and resource allocation while keeping the design space explicit.,Peer-reviewed research,Evolution
+age-0088,Critiques & Limits,Unequal agent contribution,Paper,Unlocking the Power of Multi-Agent LLM for Reasoning: From Lazy Agents to Deliberation,https://openreview.net/forum?id=5J6u03ObRZ,ICLR,2026,Zhiwei Zhang et al.,"Identifies lazy-agent behavior in which one role dominates a nominally collaborative system, then measures causal influence and uses verifiable rewards to encourage deliberation and selective reasoning restarts.",Requires builders to verify that each node contributes causal value rather than allowing a multi-agent graph to collapse into one effective agent.,Peer-reviewed research,Roles
+age-0089,Benchmarks & Datasets,Multi-party negotiation,Benchmark,"Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation",https://openreview.net/forum?id=59E19c6yrN,NeurIPS,2024,Sahar Abdelnabi; Amr Gomaa; Sarath Sivaprasad; Lea Schönherr; Mario Fritz,"Provides scorable, multi-agent, multi-issue negotiation games with metrics for task performance and role alignment, including cooperative, competitive, greedy, and adversarial participants.",Tests communication and collective decisions when graph nodes hold conflicting objectives or attempt manipulation rather than cooperating by default.,Benchmark/dataset,Handoffs
+age-0090,Reliability & Durable Execution,Agentic application security risks,Standard,OWASP Top 10 for Agentic Applications for 2026,https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/,OWASP GenAI Security Project,2025,OWASP GenAI Security Project; Agentic Security Initiative,"Provides a globally peer-reviewed operational framework for critical risks in autonomous and agentic applications, developed with contributions from more than 100 security experts, researchers, and practitioners.","Supplies a practical threat checklist for node authority, tool permissions, memory provenance, inter-agent trust, and cascading failures across an agent graph.",Industry standard,Reliability
+age-0091,Research Foundations,Task-adaptive graph routing,Paper,AMAS: Adaptively Determining Communication Topology for LLM-based Multi-agent System,https://aclanthology.org/2025.emnlp-industry.144/,EMNLP Industry Track,2025,Hui Yi Leong; Yuheng Li; Yuqing Wu; Wenwen Ouyang; Wei Zhu; Jiechao Gao; Wei Han,Introduces a dynamic graph selector that uses lightweight LLM adaptation to choose task-specific communication structures and route each query through a matching agent pathway.,"Operationalizes topology as an input-conditioned decision rather than a fixed template; evidence is currently concentrated in question answering, mathematics, and code generation.",Peer-reviewed research,Evolution
+age-0092,Research Foundations,"Collaboration-mode, role, and model routing",Paper,MasRouter: Learning to Route LLMs for Multi-Agent Systems,https://aclanthology.org/2025.acl-long.757/,ACL,2025,Yanwei Yue; Guibin Zhang; Boyang Liu; Guancheng Wan; Kun Wang; Dawei Cheng; Yiyan Qi,"Defines multi-agent system routing as a joint decision over collaboration mode, role allocation, and language-model selection, implemented with a cascaded controller that progressively constructs a task-specific system.","Makes node roles, model assignment, and whether collaboration is needed explicit routing decisions with measurable cost-quality trade-offs.",Peer-reviewed research,Evolution
+age-0093,Research Foundations,Multi-hop evidence propagation,Paper,MOC: Multi-Order Communication in LLM-based Multi-Agent Systems,https://openreview.net/forum?id=wyynWicO5s,ICML,2026,Yao Guan; Lin Wang; Zhihui Lu; Ziyi Wang; Wenzhu Yan; Qiang Duan,"Constructs structured multi-order evidence streams so agents can receive relevant information from multiple upstream hops, then applies semantic-topological merging under token constraints.","Shows that graph engineering includes what evidence survives across paths, not only which nodes and edges exist; it improves communication over a supplied topology rather than selecting that topology.",Peer-reviewed research,Handoffs
+age-0094,Research Foundations,Hierarchical node and topology optimization,Paper,HieraMAS: Optimizing Intra-Node LLM Mixtures and Inter-Node Topology for Multi-Agent Systems,https://openreview.net/forum?id=p7p5foAXaB,ICML,2026,Tianjun Yao; Zhaoyi Li; Zhiqiang Shen,"Models each functional role as a supernode containing heterogeneous language models in a propose-synthesis structure, then uses multi-level reward attribution and graph classification to select the inter-supernode topology.","Jointly engineers node composition, role capability, credit assignment, and communication edges instead of optimizing each dimension independently.",Peer-reviewed research,Evolution
+age-0095,"State, Memory & Artifacts",Hierarchical graph memory,Paper,GAM: Hierarchical Graph-based Agentic Memory for LLM Agents,https://aclanthology.org/2026.acl-long.1600/,ACL,2026,Zhaofen Wu; Hanrong Zhang; Fulin Lin; Wujiang Xu; Xinran Xu; Yankai Chen; Henry Peng Zou; Shaowen Chen; Weizhi Zhang; Xue Liu; Philip S. Yu; Hongwei Wang,"Separates rapid memory encoding from stable consolidation through an event-progression graph, a topic-associative network, and graph-guided multi-factor retrieval.","Provides a concrete graph contract for encoding, consolidating, connecting, and retrieving state; it does not address shared-memory permissions or concurrent writes among agents.",Peer-reviewed research,State
+age-0096,"State, Memory & Artifacts",Selective shared memory,Paper,Learning to Share: Selective Memory for Efficient Parallel Agentic Systems,https://openreview.net/forum?id=cCFyY2LmF5,ICML,2026,Joseph Fioresi; Parth Parag Kulkarni; Ashmal Vayani; Song Wang; Mubarak Shah,Adds a global memory bank to parallel agent teams and trains an admission controller with stepwise reinforcement learning and usage-aware credit assignment to retain reusable intermediate work.,Treats cross-team memory writes as governed graph operations that can reduce duplicate work while limiting indiscriminate context growth.,Peer-reviewed research,State
+age-0097,Observability & Cost,Graph-aware cache reuse,Paper,Accelerating Language Model Workflows with Prompt Choreography,https://aclanthology.org/2026.tacl-1.13/,TACL,2026,TJ Bai; Jason Eisner,"Executes multi-agent workflows with a dynamic global key-value cache in which each call can attend to a reordered subset of previously encoded messages, including parallel branches.","Makes workflow dependencies and reusable message state explicit execution concerns; its primary result concerns speed, and cache reuse can alter model behavior.",Peer-reviewed research,Work graphs
+age-0098,Benchmarks & Datasets,Enterprise workflow benchmark,Benchmark,Can LLMs Help You at Work? A Sandbox for Evaluating LLM Agents in Enterprise Environments,https://aclanthology.org/2025.emnlp-main.466/,EMNLP,2025,Harsh Vishwakarma; Ankush Agarwal; Ojas Patil; Chaitanya Devaguptapu; Mahesh Chandran,"Introduces EnterpriseBench with 500 tasks across software engineering, HR, finance, and administration, including fragmented data, access-control hierarchies, and cross-functional workflows.","Tests work graphs that retrieve, modify, and transfer artifacts across services while respecting authority boundaries; the organization and services are simulated.",Benchmark/dataset,Work graphs
+age-0099,Benchmarks & Datasets,Stateful tool-use evaluation,Benchmark,"ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities",https://aclanthology.org/2025.findings-naacl.65/,Findings of NAACL,2025,Jiarui Lu; Thomas Holleis; Yizhe Zhang; Bernhard Aumayer; Feng Nan; Haoping Bai; Shuang Ma; Shen Ma; Mengyu Li; Guoli Yin; Zirui Wang; Ruoming Pang,"Evaluates stateful tool execution, implicit dependencies between tools, on-policy user interaction, and intermediate and final milestones over arbitrary trajectories.","Transfers milestone and state-transition testing to graph prerequisites, although the evaluated system is an agent-user-tool loop rather than an agent-agent topology.",Benchmark/dataset,State
+age-0100,Benchmarks & Datasets,Trajectory-level evaluation,Benchmark,AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents,https://openreview.net/forum?id=4S8agvKjle,NeurIPS Datasets & Benchmarks,2024,Chang Ma; Junlei Zhang; Zhihao Zhu; Cheng Yang; Yujiu Yang; Yaohui Jin; Zhenzhong Lan; Lingpeng Kong; Junxian He,"Unifies partially observable, multi-round agent environments and adds a fine-grained progress-rate metric plus interactive trajectory analysis beyond final success.","Supplies milestone-level evaluation that can localize incomplete work, while its primarily single-agent tasks do not identify the responsible graph component by themselves.",Benchmark/dataset,Gates
+age-0101,Benchmarks & Datasets,Agent security benchmark,Benchmark,Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents,https://openreview.net/forum?id=V4y0CpX4hK,ICLR,2025,Hanrong Zhang; Jingyuan Huang; Kai Mei; Yifei Yao; Zhenting Wang; Chenlu Zhan; Hongwei Wang; Yongfeng Zhang,"Benchmarks prompt injection, memory poisoning, backdoors, mixed attacks, and defenses across 10 scenarios, more than 400 tools, 13 model backbones, and seven metrics.","Maps security failures to prompts, plans, tools, and memory surfaces that graph designs must isolate separately; inter-agent trust propagation is outside its main evaluation target.",Benchmark/dataset,Reliability
+age-0102,Benchmarks & Datasets,Prompt-injection evaluation,Benchmark,AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents,https://openreview.net/forum?id=m1YYAQjO3w,NeurIPS Datasets & Benchmarks,2024,Edoardo Debenedetti; Jie Zhang; Mislav Balunovic; Luca Beurer-Kellner; Marc Fischer; Florian Tramèr,Provides an extensible adversarial environment with 97 realistic tasks and 629 security test cases for agents executing tools over untrusted data.,Tests the instruction-data boundary that every external-input and artifact edge must preserve; compromised peer-agent messages require separate evaluation.,Benchmark/dataset,Reliability
+age-0103,Benchmarks & Datasets,Agent misuse benchmark,Benchmark,AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents,https://openreview.net/forum?id=AC5n7xHuR1,ICLR,2025,Maksym Andriushchenko; Alexandra Souly; Mateusz Dziemian; Derek Duenas; Maxwell Lin; Justin Wang; Dan Hendrycks; Andy Zou; J. Zico Kolter; Matt Fredrikson; Yarin Gal; Xander Davies,Evaluates refusal and retained task capability on 110 explicitly malicious multi-stage agent tasks with 440 augmented variants across 11 harm categories.,Tests whether safety gates reject prohibited work and stop a compromised node from completing a harmful path; the score is not a general measure of agent-system safety.,Benchmark/dataset,Gates
+age-0104,Reliability & Durable Execution,Structural prompt-injection defense,Paper,IPIGuard: A Novel Tool Dependency Graph-Based Defense Against Indirect Prompt Injection in LLM Agents,https://aclanthology.org/2025.emnlp-main.53/,EMNLP,2025,Hengyu An; Jinghuai Zhang; Tianyu Du; Chunyi Zhou; Qingming Li; Tao Lin; Shouling Ji,Models task execution as traversal over a planned Tool Dependency Graph and separates action planning from interaction with untrusted external data.,Shows how structural constraints on allowed tool edges can block unauthorized actions instead of relying only on prompts or classifiers.,Peer-reviewed research,Work graphs
+age-0105,Protocols & Handoffs,Agent identity and delegated authorization,Paper,"Identity Management for Agentic AI: The new frontier of authorization, authentication, and security for an AI agent world",https://openid.net/wp-content/uploads/2025/10/Identity-Management-for-Agentic-AI.pdf,OpenID Foundation,2025,OpenID Foundation Artificial Intelligence Identity Management Community Group,"Maps identity, authentication, delegated authority, consent, governance, and accountability gaps for autonomous agents against OAuth and OpenID Connect foundations.","Provides a standards-grounded vocabulary for identity-bearing nodes and auditable delegation across agent and tool edges; it is a non-normative landscape whitepaper, not a protocol.",Official documentation,Handoffs
+age-0106,Protocols & Handoffs,Agent capability and discovery schema,Standard,Open Agentic Schema Framework (OASF) v1.1.0,https://github.com/agntcy/oasf/releases/tag/v1.1.0,"AGNTCY Project, Linux Foundation",2026,AGNTCY Project,"Defines versioned schemas and taxonomies for agent identity metadata, capabilities, skills, modules, interactions, and discovery records, including MCP and A2A alignment modules.",Gives graph builders a portable node descriptor for capability-based discovery and routing validation; it describes capabilities but does not execute a graph or authorize an edge.,Industry standard,Roles
+age-0107,"State, Memory & Artifacts",Artifact provenance,Standard,PROV-DM: The PROV Data Model,https://www.w3.org/TR/prov-dm/,W3C Recommendation,2013,Luc Moreau; Paolo Missier,"Defines entities, activities, agents, derivation, attribution, responsibility, bundles, and collections for interoperable provenance records.","Maps artifacts, execution steps, and responsible nodes into a portable lineage model; storage, access control, and cryptographic integrity remain implementation concerns.",Industry standard,State
+age-0108,"State, Memory & Artifacts",Cryptographic artifact provenance,Paper,in-toto: Providing farm-to-table guarantees for bits and bytes,https://www.usenix.org/conference/usenixsecurity19/presentation/torres-arias,USENIX Security,2019,Santiago Torres-Arias; Hammad Afzali; Trishank Karthik Kuppusamy; Reza Curtmola; Justin Cappos,"Introduces signed material and product links for supply-chain steps and verifies them against an expected layout, supported by analysis of 30 historical compromises and deployed integrations.","Supplies a transferable receipt pattern that binds each artifact transformation to an authorized step and identity; it verifies process integrity, not semantic correctness.",Peer-reviewed research,State
+age-0109,Reliability & Durable Execution,Portable workflow semantics,Standard,Serverless Workflow Specification v1.0.0,https://serverlessworkflow.io/blog/releases/release-100/,"Serverless Workflow, Cloud Native Computing Foundation",2025,Serverless Workflow Project,"Defines a vendor-neutral workflow language with sequential and concurrent tasks, event correlation, service calls, error handling, retries, and timeouts.","Provides an inspectable execution contract for production work graphs; conformance does not itself supply a durable runtime, an agent protocol, or model-level correctness.",Industry standard,Reliability

resources.jsonl

@@ -3,20 +3,20 @@
 {"id":"age-0003","section":"Research Foundations","subcategory":"Agent foundations","rtype":"Paper","title":"Agent-Oriented Programming","url":"https://doi.org/10.1016/0004-3702(93)90034-9","venue":"Artificial Intelligence","year":1993,"authors":"Yoav Shoham","description":"Defines a programming paradigm in which agents are first-class components described through mental state and governed by explicit interaction rules.","why":"Establishes the intellectual lineage for treating an agent role as a programmable organizational unit.","evidence":"Peer-reviewed research","layer":"Roles"}
 {"id":"age-0004","section":"Research Foundations","subcategory":"Agent foundations","rtype":"Paper","title":"Intelligent Agents: Theory and Practice","url":"https://doi.org/10.1017/S0269888900008122","venue":"The Knowledge Engineering Review","year":1995,"authors":"Michael Wooldridge; Nicholas R. Jennings","description":"Surveys the properties, architectures, and engineering approaches that distinguish autonomous agents from ordinary software modules.","why":"Grounds the agency-at-the-nodes boundary that separates an agent graph from a deterministic workflow.","evidence":"Peer-reviewed research","layer":"Roles"}
 {"id":"age-0005","section":"Research Foundations","subcategory":"Shared-state architectures","rtype":"Paper","title":"The Blackboard Model of Problem Solving and the Evolution of Blackboard Architectures","url":"https://doi.org/10.1609/aimag.v7i2.537","venue":"AI Magazine","year":1986,"authors":"H. Penny Nii","description":"Describes systems in which independent specialists coordinate opportunistically through a shared problem state and a control component.","why":"Supplies a durable model for shared state without requiring every node to exchange its full context directly.","evidence":"Peer-reviewed research","layer":"State"}
-{"id":"age-0006","section":"Research Foundations","subcategory":"Learned communication","rtype":"Paper","title":"Learning to Communicate with Deep Multi-Agent Reinforcement Learning","url":"https://proceedings.neurips.cc/paper_files/paper/2016/hash/c7635bfd99248a2cdef8249ef7bfbef4-Abstract.html","venue":"NeurIPS","year":2016,"authors":"Jakob Foerster et al.","description":"Introduces reinforcement-learning methods that let agents learn communication protocols alongside their task policies, including discrete messages for execution.","why":"Shows that edge content and communication policy can be engineered or learned rather than treated as free-form chat.","evidence":"Peer-reviewed research","layer":"Handoffs"}
+{"id":"age-0006","section":"Research Foundations","subcategory":"Learned communication","rtype":"Paper","title":"Learning to Communicate with Deep Multi-Agent Reinforcement Learning","url":"https://arxiv.org/abs/1605.06676","venue":"NeurIPS","year":2016,"authors":"Jakob Foerster et al.","description":"Introduces reinforcement-learning methods that let agents learn communication protocols alongside their task policies, including discrete messages for execution.","why":"Shows that edge content and communication policy can be engineered or learned rather than treated as free-form chat.","evidence":"Peer-reviewed research","layer":"Handoffs"}
 {"id":"age-0007","section":"Research Foundations","subcategory":"Learned communication","rtype":"Paper","title":"TarMAC: Targeted Multi-Agent Communication","url":"https://proceedings.mlr.press/v97/das19a.html","venue":"ICML","year":2019,"authors":"Abhishek Das et al.","description":"Uses attention to let agents address different messages to selected recipients instead of broadcasting the same information to the whole team.","why":"Motivates selective, recipient-aware handoffs when all-to-all communication is wasteful or distracting.","evidence":"Peer-reviewed research","layer":"Handoffs"}
 {"id":"age-0008","section":"Research Foundations","subcategory":"LLM multi-agent systems","rtype":"Paper","title":"AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation","url":"https://arxiv.org/abs/2308.08155","venue":"COLM","year":2024,"authors":"Qingyun Wu et al.","description":"Presents a framework for composing customizable conversational agents that can combine language models, tools, code execution, and human input.","why":"An early, influential demonstration that agent roles and conversation links can be expressed as an executable topology.","evidence":"Peer-reviewed research","layer":"Topology"}
 {"id":"age-0009","section":"Research Foundations","subcategory":"Topology optimization","rtype":"Paper","title":"GPTSwarm: Language Agents as Optimizable Graphs","url":"https://proceedings.mlr.press/v235/zhuge24a.html","venue":"ICML","year":2024,"authors":"Mingchen Zhuge et al.","description":"Represents language-agent systems as computational graphs and optimizes graph components from task feedback.","why":"Makes the graph itself an optimization target rather than a fixed orchestration diagram.","evidence":"Peer-reviewed research","layer":"Evolution"}
 {"id":"age-0010","section":"Research Foundations","subcategory":"Dynamic topology","rtype":"Paper","title":"A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration","url":"https://openreview.net/forum?id=XII0Wp1XA9","venue":"COLM","year":2024,"authors":"Zijun Liu et al.","description":"Constructs task-specific collaboration networks that can vary which agents participate and how they communicate instead of relying on one fixed team.","why":"Provides evidence for adapting the work graph to the task while keeping the available agent roles reusable.","evidence":"Peer-reviewed research","layer":"Evolution"}
-{"id":"age-0011","section":"Research Foundations","subcategory":"Communication efficiency","rtype":"Paper","title":"Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems","url":"https://proceedings.iclr.cc/paper_files/paper/2025/hash/bbc461518c59a2a8d64e70e2c38c4a0e-Abstract-Conference.html","venue":"ICLR","year":2025,"authors":"Guibin Zhang et al.","description":"Studies an economical communication pipeline that reduces redundant information exchanged among language-model agents.","why":"Treats edge traffic as a measurable cost and tests whether less communication can preserve useful collaboration.","evidence":"Peer-reviewed research","layer":"Observability & cost"}
+{"id":"age-0011","section":"Research Foundations","subcategory":"Communication efficiency","rtype":"Paper","title":"Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems","url":"https://openreview.net/forum?id=LkzuPorQ5L","venue":"ICLR","year":2025,"authors":"Guibin Zhang et al.","description":"Studies an economical communication pipeline that reduces redundant information exchanged among language-model agents.","why":"Treats edge traffic as a measurable cost and tests whether less communication can preserve useful collaboration.","evidence":"Peer-reviewed research","layer":"Observability & cost"}
 {"id":"age-0012","section":"Research Foundations","subcategory":"Topology optimization","rtype":"Paper","title":"G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks","url":"https://proceedings.mlr.press/v267/zhang25cu.html","venue":"ICML","year":2025,"authors":"Guibin Zhang et al.","description":"Uses graph neural networks to design communication structures for multi-agent systems instead of assuming a complete or manually chosen graph.","why":"Connects task performance to explicit topology search and exposes communication structure as an engineering variable.","evidence":"Peer-reviewed research","layer":"Evolution"}
-{"id":"age-0013","section":"Research Foundations","subcategory":"System search","rtype":"Paper","title":"Automated Design of Agentic Systems","url":"https://proceedings.iclr.cc/paper_files/paper/2025/hash/36b7acf6f6010652b3f2a433774a66fe-Abstract-Conference.html","venue":"ICLR","year":2025,"authors":"Shengran Hu; Cong Lu; Jeff Clune","description":"Uses a meta-agent to propose, evaluate, and iteratively improve code-defined agentic systems across tasks.","why":"Demonstrates automated search over coordination logic while retaining executable artifacts that engineers can inspect.","evidence":"Peer-reviewed research","layer":"Evolution"}
+{"id":"age-0013","section":"Research Foundations","subcategory":"System search","rtype":"Paper","title":"Automated Design of Agentic Systems","url":"https://openreview.net/forum?id=t9U3LW7JVX","venue":"ICLR","year":2025,"authors":"Shengran Hu; Cong Lu; Jeff Clune","description":"Uses a meta-agent to propose, evaluate, and iteratively improve code-defined agentic systems across tasks.","why":"Demonstrates automated search over coordination logic while retaining executable artifacts that engineers can inspect.","evidence":"Peer-reviewed research","layer":"Evolution"}
 {"id":"age-0014","section":"Research Foundations","subcategory":"Workflow search","rtype":"Paper","title":"AFlow: Automating Agentic Workflow Generation","url":"https://openreview.net/forum?id=z5uVAKwmjf","venue":"ICLR","year":2025,"authors":"Jiayi Zhang et al.","description":"Searches over reusable workflow operators to generate task-specific agentic workflows and improve them from evaluation results.","why":"Offers a concrete method for evolving work graphs against measurable objectives rather than intuition alone.","evidence":"Peer-reviewed research","layer":"Evolution"}
 {"id":"age-0015","section":"Research Foundations","subcategory":"Joint optimization","rtype":"Paper","title":"Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies","url":"https://openreview.net/forum?id=I05H9RUzHB","venue":"ICLR","year":2026,"authors":"Han Zhou et al.","description":"Studies joint optimization of agent prompts and communication topology instead of tuning either component in isolation.","why":"Shows that node behavior and graph structure interact and may need to evolve together.","evidence":"Peer-reviewed research","layer":"Evolution"}
 {"id":"age-0016","section":"Research Foundations","subcategory":"Debate and councils","rtype":"Paper","title":"Improving Factuality and Reasoning in Language Models through Multiagent Debate","url":"https://proceedings.mlr.press/v235/du24e.html","venue":"ICML","year":2024,"authors":"Yilun Du et al.","description":"Tests rounds of proposal and critique among multiple language-model instances as a way to improve factual and reasoning answers.","why":"Supplies an empirical basis for debate-style gates while leaving room to examine correlated errors and added cost.","evidence":"Peer-reviewed research","layer":"Gates"}
 {"id":"age-0017","section":"Research Foundations","subcategory":"Debate and councils","rtype":"Paper","title":"Improving Multi-Agent Debate with Sparse Communication Topology","url":"https://aclanthology.org/2024.findings-emnlp.427/","venue":"Findings of EMNLP","year":2024,"authors":"Yunxuan Li et al.","description":"Examines multi-agent debate under sparse communication structures rather than defaulting to full information exchange among every participant.","why":"Isolates topology as a factor in debate quality and communication efficiency.","evidence":"Peer-reviewed research","layer":"Topology"}
-{"id":"age-0018","section":"Research Foundations","subcategory":"Feedback and memory","rtype":"Paper","title":"Reflexion: Language Agents with Verbal Reinforcement Learning","url":"https://proceedings.neurips.cc/paper_files/paper/2023/hash/1b44b878bb782e6954cd888628510e90-Abstract-Conference.html","venue":"NeurIPS","year":2023,"authors":"Noah Shinn et al.","description":"Lets an agent convert feedback into textual reflections stored in episodic memory and reused on later attempts.","why":"Clarifies how a node loop can persist learning across retries without changing model weights.","evidence":"Peer-reviewed research","layer":"State"}
-{"id":"age-0019","section":"Research Foundations","subcategory":"Tool-grounded verification","rtype":"Paper","title":"CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing","url":"https://proceedings.iclr.cc/paper_files/paper/2024/hash/fef126561bbf9d4467dbb8d27334b8fe-Abstract-Conference.html","venue":"ICLR","year":2024,"authors":"Zhibin Gou et al.","description":"Uses external tools to obtain feedback that a language model can apply when critiquing and revising its outputs.","why":"Supports verification gates grounded in observable evidence instead of another ungrounded model opinion.","evidence":"Peer-reviewed research","layer":"Gates"}
+{"id":"age-0018","section":"Research Foundations","subcategory":"Feedback and memory","rtype":"Paper","title":"Reflexion: Language Agents with Verbal Reinforcement Learning","url":"https://openreview.net/forum?id=vAElhFcKW6","venue":"NeurIPS","year":2023,"authors":"Noah Shinn et al.","description":"Lets an agent convert feedback into textual reflections stored in episodic memory and reused on later attempts.","why":"Clarifies how a node loop can persist learning across retries without changing model weights.","evidence":"Peer-reviewed research","layer":"State"}
+{"id":"age-0019","section":"Research Foundations","subcategory":"Tool-grounded verification","rtype":"Paper","title":"CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing","url":"https://openreview.net/forum?id=Sx038qxjek","venue":"ICLR","year":2024,"authors":"Zhibin Gou et al.","description":"Uses external tools to obtain feedback that a language model can apply when critiquing and revising its outputs.","why":"Supports verification gates grounded in observable evidence instead of another ungrounded model opinion.","evidence":"Peer-reviewed research","layer":"Gates"}
 {"id":"age-0020","section":"Production Case Studies","subcategory":"Generalist agent team","rtype":"Paper","title":"Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks","url":"https://www.microsoft.com/en-us/research/publication/magentic-one-a-generalist-multi-agent-system-for-solving-complex-tasks/","venue":"Microsoft Research MSR-TR-2024-47","year":2024,"authors":"Adam Fourney et al.","description":"Documents an orchestrator-led team of specialized agents for web, file, coding, and terminal tasks, with progress tracking and replanning.","why":"Provides a concrete generalist topology whose role boundaries and recovery behavior can be examined as a system.","evidence":"Research preprint","layer":"Roles"}
 {"id":"age-0021","section":"Production Case Studies","subcategory":"Parallel research","rtype":"Blog","title":"How we built our multi-agent research system","url":"https://www.anthropic.com/engineering/multi-agent-research-system","venue":"Anthropic Engineering","year":2025,"authors":"Jeremy Hadfield et al.","description":"Describes a lead research agent that creates parallel subagents, delegates searches, and synthesizes their findings, including operational lessons from production.","why":"A detailed case study of dynamic fan-out and fan-in, context separation, evaluation, and the token cost of an adaptive work graph.","evidence":"Practitioner analysis","layer":"Work graphs"}
 {"id":"age-0022","section":"Frameworks & SDKs","subcategory":"Role-based teams","rtype":"Docs","title":"CrewAI Crews","url":"https://docs.crewai.com/en/concepts/crews","venue":"CrewAI Documentation","year":2026,"authors":"CrewAI","description":"Documents role-based agent crews, assigned tasks, delegation, and sequential or hierarchical execution processes.","why":"Offers accessible primitives for testing explicit ownership and manager-worker coordination.","evidence":"Official documentation","layer":"Roles"}
@@ -45,7 +45,7 @@
 {"id":"age-0045","section":"Observability & Cost","subcategory":"Observability platform","rtype":"Docs","title":"LangSmith Observability","url":"https://docs.langchain.com/langsmith/observability","venue":"LangSmith Documentation","year":2026,"authors":"LangChain","description":"Documents tracing, dashboards, alerts, feedback, and evaluation views for language-model and agent applications.","why":"Provides operational views for following execution across nodes and locating failures on the critical path.","evidence":"Official documentation","layer":"Observability & cost"}
 {"id":"age-0046","section":"Benchmarks & Datasets","subcategory":"Collaboration benchmark","rtype":"Benchmark","title":"MultiAgentBench: Evaluating Collaboration and Competition of LLM Agents","url":"https://aclanthology.org/2025.acl-long.421/","venue":"ACL","year":2025,"authors":"Kunlun Zhu et al.","description":"Benchmarks language-model agents in collaborative and competitive settings while examining coordination processes as well as task outcomes.","why":"Measures properties of the team interaction that single-agent benchmarks cannot expose.","evidence":"Benchmark/dataset","layer":"Observability & cost"}
 {"id":"age-0047","section":"Benchmarks & Datasets","subcategory":"Failure diagnosis","rtype":"Benchmark","title":"Why Do Multi-Agent LLM Systems Fail?","url":"https://nips.cc/virtual/2025/poster/121528","venue":"NeurIPS Datasets & Benchmarks","year":2025,"authors":"Mert Cemri et al.","description":"Provides a structured taxonomy and evaluation approach for diagnosing coordination failures in multi-agent language-model systems.","why":"Turns reliability incidents into recurring, attributable failure classes that can guide graph redesign.","evidence":"Benchmark/dataset","layer":"Reliability"}
-{"id":"age-0048","section":"Critiques & Limits","subcategory":"Self-correction limits","rtype":"Paper","title":"Large Language Models Cannot Self-Correct Reasoning Yet","url":"https://proceedings.iclr.cc/paper_files/paper/2024/hash/8b4add8b0aa8749d80a34ca5d941c355-Abstract-Conference.html","venue":"ICLR","year":2024,"authors":"Jie Huang et al.","description":"Finds that intrinsic self-correction without reliable external feedback often fails to improve reasoning and can reduce accuracy.","why":"Warns against using an ungrounded critic node as evidence simply because it is separate from the producing node.","evidence":"Peer-reviewed research","layer":"Gates"}
+{"id":"age-0048","section":"Critiques & Limits","subcategory":"Self-correction limits","rtype":"Paper","title":"Large Language Models Cannot Self-Correct Reasoning Yet","url":"https://openreview.net/forum?id=IkmD3fKBPQ","venue":"ICLR","year":2024,"authors":"Jie Huang et al.","description":"Finds that intrinsic self-correction without reliable external feedback often fails to improve reasoning and can reduce accuracy.","why":"Warns against using an ungrounded critic node as evidence simply because it is separate from the producing node.","evidence":"Peer-reviewed research","layer":"Gates"}
 {"id":"age-0049","section":"Critiques & Limits","subcategory":"Scaling evidence","rtype":"Paper","title":"Towards a Science of Scaling Agent Systems","url":"https://arxiv.org/abs/2512.08296","venue":"arXiv; Google Research","year":2025,"authors":"Yubin Kim et al.","description":"Studies how agent-system performance changes across tasks, models, coordination structures, and scaling choices under controlled experiments.","why":"Tests the assumption that adding agents reliably helps and frames scaling as an empirical topology decision.","evidence":"Research preprint","layer":"Topology"}
 {"id":"age-0050","section":"Critiques & Limits","subcategory":"Token-budget comparison","rtype":"Paper","title":"Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets","url":"https://arxiv.org/abs/2604.02460","venue":"arXiv","year":2026,"authors":"Dat Tran; Douwe Kiela","description":"Compares single-agent and multi-agent approaches to multi-hop reasoning while holding the total thinking-token budget constant.","why":"Provides the cost-controlled baseline needed before claiming that coordination, rather than extra inference, caused an improvement.","evidence":"Research preprint","layer":"Topology"}
 {"id":"age-0051","section":"Start Here","subcategory":"Contemporary framing","rtype":"Blog","title":"From Loop Engineering to Graph Engineering?","url":"https://x.com/IntuitMachine/status/2078419526354378975","venue":"X Articles","year":2026,"authors":"Carlos E. Perez","description":"Frames the shift as loop architecture: networks of improvement cycles that monitor, feed, constrain, and correct one another, with reliability located in their edges.","why":"Adds the grounding requirement missing from topology-only accounts: independent counter-metrics, frozen tests or rules, external anchors, and human ownership of root objectives.","evidence":"Practitioner analysis","layer":"Gates"}
@@ -78,3 +78,32 @@
 {"id":"age-0078","section":"Production Case Studies","subcategory":"Secure delegated access","rtype":"Blog","title":"Creating AI agent solutions for warehouse data access and security","url":"https://engineering.fb.com/2025/08/13/data-infrastructure/agentic-solution-for-warehouse-data-access/","venue":"Engineering at Meta","year":2025,"authors":"Can Lin; Uday Ramesh Savagaonkar; Iuliu Rus; Komal Mangtani","description":"Describes collaborating data-user and data-owner agents, specialized subagents, triage, permission negotiation, human oversight, access budgets, analytical risk rules, output guardrails, traces, and daily regression evaluation.","why":"Shows plural bounded agency governed by independent rule-based gates rather than relying on model judgment for security decisions.","evidence":"Practitioner analysis","layer":"Gates"}
 {"id":"age-0079","section":"Production Case Studies","subcategory":"Staged agent swarm","rtype":"Blog","title":"How Meta Used AI to Map Tribal Knowledge in Large-Scale Data Pipelines","url":"https://engineering.fb.com/2026/04/06/developer-tools/how-meta-used-ai-to-map-tribal-knowledge-in-large-scale-data-pipelines/","venue":"Engineering at Meta","year":2026,"authors":"Krishna Ganeriwal; Plawan Rath; Ashwini Verma","description":"Reports a staged swarm of more than 50 explorer, analyst, writer, critic, fixer, upgrader, tester, and gap-filling tasks, with repeated review rounds and recurring automated refresh runs.","why":"Provides a concrete fan-out, critique, repair, integration, and recurrence graph with explicit artifacts and quality gates; efficiency figures are company-reported and preliminary.","evidence":"Practitioner analysis","layer":"Work graphs"}
 {"id":"age-0080","section":"Production Case Studies","subcategory":"Multi-hop identity and provenance","rtype":"Blog","title":"Solving the Identity Crisis for AI Agents","url":"https://www.uber.com/by/en/blog/solving-the-agent-identity-crisis/","venue":"Uber Engineering","year":2026,"authors":"Matt Mathew; Prasad Borole; Meng Huang; Sergey Burykin; Gaurav Goel; Bayard Walsh","description":"Describes an internal agent mesh with registered identities, SPIRE-backed workload attestation, short-lived audience-scoped tokens for every hop, actor-chain provenance, MCP gateway enforcement, and a standardized A2A client.","why":"Treats identity, delegated authority, and provenance as mandatory edge state across a multi-agent graph; adoption and latency metrics are company-reported.","evidence":"Practitioner analysis","layer":"Handoffs"}
+{"id":"age-0081","section":"Research Foundations","subcategory":"Task-adaptive topology synthesis","rtype":"Paper","title":"Dynamic Generation of Multi LLM Agents Communication Topologies with Graph Diffusion Models","url":"https://aclanthology.org/2026.acl-long.1764/","venue":"ACL","year":2026,"authors":"Eric Hanchen Jiang et al.","description":"Introduces Guided Topology Diffusion, which iteratively synthesizes sparse, task-adaptive communication graphs using a proxy model for objectives such as accuracy, utility, and cost.","why":"Treats topology as a multi-objective, per-task design artifact rather than a static collaboration template.","evidence":"Peer-reviewed research","layer":"Evolution"}
+{"id":"age-0082","section":"Reliability & Durable Execution","subcategory":"Topology-conditioned memory leakage","rtype":"Paper","title":"Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs","url":"https://aclanthology.org/2026.findings-acl.1980/","venue":"Findings of ACL","year":2026,"authors":"Jinbo Liu et al.","description":"Measures private-information leakage across six communication topologies, agent counts, and attacker-target placements, finding higher leakage with denser connectivity, shorter graph distance, and greater target centrality.","why":"Makes privacy a measurable consequence of edge structure and node placement, motivating topology-aware access controls.","evidence":"Peer-reviewed research","layer":"Reliability"}
+{"id":"age-0083","section":"Critiques & Limits","subcategory":"Collaboration-induced diversity collapse","rtype":"Paper","title":"Diversity Collapse in Multi-Agent LLM Systems: Structural Coupling and Collective Failure in Open-Ended Idea Generation","url":"https://aclanthology.org/2026.findings-acl.13/","venue":"Findings of ACL","year":2026,"authors":"Nuo Chen et al.","description":"Studies multi-agent ideation across model, cognition, and system levels, finding diminishing group-size returns, authority effects, and faster premature convergence under dense communication.","why":"Shows that interaction can contract a search space, so graph designs for exploration must preserve independence and meaningful disagreement.","evidence":"Peer-reviewed research","layer":"Topology"}
+{"id":"age-0084","section":"Research Foundations","subcategory":"Dynamic node and edge elimination","rtype":"Paper","title":"AgentDropout: Dynamic Agent Elimination for Token-Efficient and High-Performance LLM-Based Multi-Agent Collaboration","url":"https://aclanthology.org/2025.acl-long.1170/","venue":"ACL","year":2025,"authors":"Zhexuan Wang; Yutong Wang; Xuebo Liu; Liang Ding; Miao Zhang; Jie Liu; Min Zhang","description":"Optimizes communication-graph adjacency matrices across collaboration rounds to remove redundant agents and messages, reducing both prompt and completion token consumption in its evaluations.","why":"Makes node participation and edge density explicit cost-quality decisions instead of assuming every role should run on every task.","evidence":"Peer-reviewed research","layer":"Evolution"}
+{"id":"age-0085","section":"Benchmarks & Datasets","subcategory":"Process-level collaboration","rtype":"Benchmark","title":"Collab-Overcooked: Benchmarking and Evaluating Large Language Models as Collaborative Agents","url":"https://aclanthology.org/2025.emnlp-main.249/","venue":"EMNLP","year":2025,"authors":"Haochen Sun; Shuwen Zhang; Lujie Niu; Lei Ren; Hao Xu; Hao Fu; Fangkun Zhao; Caixia Yuan; Xiaojie Wang","description":"Provides 30 open-ended cooperative tasks and process-oriented measures for goal interpretation, active collaboration, communication, and continuous adaptation across 13 language models.","why":"Distinguishes reaching an answer from coordinating well, exposing collaboration failures that final-outcome metrics can hide.","evidence":"Benchmark/dataset","layer":"Observability & cost"}
+{"id":"age-0086","section":"Critiques & Limits","subcategory":"Inter-agent communication attacks","rtype":"Paper","title":"Red-Teaming LLM Multi-Agent Systems via Communication Attacks","url":"https://aclanthology.org/2025.findings-acl.349/","venue":"Findings of ACL","year":2025,"authors":"Pengfei He; Yuping Lin; Shen Dong; Han Xu; Yue Xing; Hui Liu","description":"Introduces an Agent-in-the-Middle attack that intercepts and manipulates inter-agent messages, evaluated across multiple frameworks, communication structures, and applications.","why":"Shows that graph edges can compromise the whole system without altering its nodes, making message integrity and provenance part of the handoff contract.","evidence":"Peer-reviewed research","layer":"Handoffs"}
+{"id":"age-0087","section":"Research Foundations","subcategory":"Query-dependent architecture search","rtype":"Paper","title":"Multi-agent Architecture Search via Agentic Supernet","url":"https://proceedings.mlr.press/v267/zhang25bi.html","venue":"ICML","year":2025,"authors":"Guibin Zhang; Luyang Niu; Junfeng Fang; Kun Wang; Lei Bai; Xiang Wang","description":"Represents possible agentic architectures as a probabilistic supernet and samples query-dependent systems with tailored language-model calls, tool calls, and token costs across six benchmarks.","why":"Replaces a one-size-fits-all graph with per-query architecture and resource allocation while keeping the design space explicit.","evidence":"Peer-reviewed research","layer":"Evolution"}
+{"id":"age-0088","section":"Critiques & Limits","subcategory":"Unequal agent contribution","rtype":"Paper","title":"Unlocking the Power of Multi-Agent LLM for Reasoning: From Lazy Agents to Deliberation","url":"https://openreview.net/forum?id=5J6u03ObRZ","venue":"ICLR","year":2026,"authors":"Zhiwei Zhang et al.","description":"Identifies lazy-agent behavior in which one role dominates a nominally collaborative system, then measures causal influence and uses verifiable rewards to encourage deliberation and selective reasoning restarts.","why":"Requires builders to verify that each node contributes causal value rather than allowing a multi-agent graph to collapse into one effective agent.","evidence":"Peer-reviewed research","layer":"Roles"}
+{"id":"age-0089","section":"Benchmarks & Datasets","subcategory":"Multi-party negotiation","rtype":"Benchmark","title":"Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation","url":"https://openreview.net/forum?id=59E19c6yrN","venue":"NeurIPS","year":2024,"authors":"Sahar Abdelnabi; Amr Gomaa; Sarath Sivaprasad; Lea Schönherr; Mario Fritz","description":"Provides scorable, multi-agent, multi-issue negotiation games with metrics for task performance and role alignment, including cooperative, competitive, greedy, and adversarial participants.","why":"Tests communication and collective decisions when graph nodes hold conflicting objectives or attempt manipulation rather than cooperating by default.","evidence":"Benchmark/dataset","layer":"Handoffs"}
+{"id":"age-0090","section":"Reliability & Durable Execution","subcategory":"Agentic application security risks","rtype":"Standard","title":"OWASP Top 10 for Agentic Applications for 2026","url":"https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/","venue":"OWASP GenAI Security Project","year":2025,"authors":"OWASP GenAI Security Project; Agentic Security Initiative","description":"Provides a globally peer-reviewed operational framework for critical risks in autonomous and agentic applications, developed with contributions from more than 100 security experts, researchers, and practitioners.","why":"Supplies a practical threat checklist for node authority, tool permissions, memory provenance, inter-agent trust, and cascading failures across an agent graph.","evidence":"Industry standard","layer":"Reliability"}
+{"id":"age-0091","section":"Research Foundations","subcategory":"Task-adaptive graph routing","rtype":"Paper","title":"AMAS: Adaptively Determining Communication Topology for LLM-based Multi-agent System","url":"https://aclanthology.org/2025.emnlp-industry.144/","venue":"EMNLP Industry Track","year":2025,"authors":"Hui Yi Leong; Yuheng Li; Yuqing Wu; Wenwen Ouyang; Wei Zhu; Jiechao Gao; Wei Han","description":"Introduces a dynamic graph selector that uses lightweight LLM adaptation to choose task-specific communication structures and route each query through a matching agent pathway.","why":"Operationalizes topology as an input-conditioned decision rather than a fixed template; evidence is currently concentrated in question answering, mathematics, and code generation.","evidence":"Peer-reviewed research","layer":"Evolution"}
+{"id":"age-0092","section":"Research Foundations","subcategory":"Collaboration-mode, role, and model routing","rtype":"Paper","title":"MasRouter: Learning to Route LLMs for Multi-Agent Systems","url":"https://aclanthology.org/2025.acl-long.757/","venue":"ACL","year":2025,"authors":"Yanwei Yue; Guibin Zhang; Boyang Liu; Guancheng Wan; Kun Wang; Dawei Cheng; Yiyan Qi","description":"Defines multi-agent system routing as a joint decision over collaboration mode, role allocation, and language-model selection, implemented with a cascaded controller that progressively constructs a task-specific system.","why":"Makes node roles, model assignment, and whether collaboration is needed explicit routing decisions with measurable cost-quality trade-offs.","evidence":"Peer-reviewed research","layer":"Evolution"}
+{"id":"age-0093","section":"Research Foundations","subcategory":"Multi-hop evidence propagation","rtype":"Paper","title":"MOC: Multi-Order Communication in LLM-based Multi-Agent Systems","url":"https://openreview.net/forum?id=wyynWicO5s","venue":"ICML","year":2026,"authors":"Yao Guan; Lin Wang; Zhihui Lu; Ziyi Wang; Wenzhu Yan; Qiang Duan","description":"Constructs structured multi-order evidence streams so agents can receive relevant information from multiple upstream hops, then applies semantic-topological merging under token constraints.","why":"Shows that graph engineering includes what evidence survives across paths, not only which nodes and edges exist; it improves communication over a supplied topology rather than selecting that topology.","evidence":"Peer-reviewed research","layer":"Handoffs"}
+{"id":"age-0094","section":"Research Foundations","subcategory":"Hierarchical node and topology optimization","rtype":"Paper","title":"HieraMAS: Optimizing Intra-Node LLM Mixtures and Inter-Node Topology for Multi-Agent Systems","url":"https://openreview.net/forum?id=p7p5foAXaB","venue":"ICML","year":2026,"authors":"Tianjun Yao; Zhaoyi Li; Zhiqiang Shen","description":"Models each functional role as a supernode containing heterogeneous language models in a propose-synthesis structure, then uses multi-level reward attribution and graph classification to select the inter-supernode topology.","why":"Jointly engineers node composition, role capability, credit assignment, and communication edges instead of optimizing each dimension independently.","evidence":"Peer-reviewed research","layer":"Evolution"}
+{"id":"age-0095","section":"State, Memory & Artifacts","subcategory":"Hierarchical graph memory","rtype":"Paper","title":"GAM: Hierarchical Graph-based Agentic Memory for LLM Agents","url":"https://aclanthology.org/2026.acl-long.1600/","venue":"ACL","year":2026,"authors":"Zhaofen Wu; Hanrong Zhang; Fulin Lin; Wujiang Xu; Xinran Xu; Yankai Chen; Henry Peng Zou; Shaowen Chen; Weizhi Zhang; Xue Liu; Philip S. Yu; Hongwei Wang","description":"Separates rapid memory encoding from stable consolidation through an event-progression graph, a topic-associative network, and graph-guided multi-factor retrieval.","why":"Provides a concrete graph contract for encoding, consolidating, connecting, and retrieving state; it does not address shared-memory permissions or concurrent writes among agents.","evidence":"Peer-reviewed research","layer":"State"}
+{"id":"age-0096","section":"State, Memory & Artifacts","subcategory":"Selective shared memory","rtype":"Paper","title":"Learning to Share: Selective Memory for Efficient Parallel Agentic Systems","url":"https://openreview.net/forum?id=cCFyY2LmF5","venue":"ICML","year":2026,"authors":"Joseph Fioresi; Parth Parag Kulkarni; Ashmal Vayani; Song Wang; Mubarak Shah","description":"Adds a global memory bank to parallel agent teams and trains an admission controller with stepwise reinforcement learning and usage-aware credit assignment to retain reusable intermediate work.","why":"Treats cross-team memory writes as governed graph operations that can reduce duplicate work while limiting indiscriminate context growth.","evidence":"Peer-reviewed research","layer":"State"}
+{"id":"age-0097","section":"Observability & Cost","subcategory":"Graph-aware cache reuse","rtype":"Paper","title":"Accelerating Language Model Workflows with Prompt Choreography","url":"https://aclanthology.org/2026.tacl-1.13/","venue":"TACL","year":2026,"authors":"TJ Bai; Jason Eisner","description":"Executes multi-agent workflows with a dynamic global key-value cache in which each call can attend to a reordered subset of previously encoded messages, including parallel branches.","why":"Makes workflow dependencies and reusable message state explicit execution concerns; its primary result concerns speed, and cache reuse can alter model behavior.","evidence":"Peer-reviewed research","layer":"Work graphs"}
+{"id":"age-0098","section":"Benchmarks & Datasets","subcategory":"Enterprise workflow benchmark","rtype":"Benchmark","title":"Can LLMs Help You at Work? A Sandbox for Evaluating LLM Agents in Enterprise Environments","url":"https://aclanthology.org/2025.emnlp-main.466/","venue":"EMNLP","year":2025,"authors":"Harsh Vishwakarma; Ankush Agarwal; Ojas Patil; Chaitanya Devaguptapu; Mahesh Chandran","description":"Introduces EnterpriseBench with 500 tasks across software engineering, HR, finance, and administration, including fragmented data, access-control hierarchies, and cross-functional workflows.","why":"Tests work graphs that retrieve, modify, and transfer artifacts across services while respecting authority boundaries; the organization and services are simulated.","evidence":"Benchmark/dataset","layer":"Work graphs"}
+{"id":"age-0099","section":"Benchmarks & Datasets","subcategory":"Stateful tool-use evaluation","rtype":"Benchmark","title":"ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities","url":"https://aclanthology.org/2025.findings-naacl.65/","venue":"Findings of NAACL","year":2025,"authors":"Jiarui Lu; Thomas Holleis; Yizhe Zhang; Bernhard Aumayer; Feng Nan; Haoping Bai; Shuang Ma; Shen Ma; Mengyu Li; Guoli Yin; Zirui Wang; Ruoming Pang","description":"Evaluates stateful tool execution, implicit dependencies between tools, on-policy user interaction, and intermediate and final milestones over arbitrary trajectories.","why":"Transfers milestone and state-transition testing to graph prerequisites, although the evaluated system is an agent-user-tool loop rather than an agent-agent topology.","evidence":"Benchmark/dataset","layer":"State"}
+{"id":"age-0100","section":"Benchmarks & Datasets","subcategory":"Trajectory-level evaluation","rtype":"Benchmark","title":"AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents","url":"https://openreview.net/forum?id=4S8agvKjle","venue":"NeurIPS Datasets & Benchmarks","year":2024,"authors":"Chang Ma; Junlei Zhang; Zhihao Zhu; Cheng Yang; Yujiu Yang; Yaohui Jin; Zhenzhong Lan; Lingpeng Kong; Junxian He","description":"Unifies partially observable, multi-round agent environments and adds a fine-grained progress-rate metric plus interactive trajectory analysis beyond final success.","why":"Supplies milestone-level evaluation that can localize incomplete work, while its primarily single-agent tasks do not identify the responsible graph component by themselves.","evidence":"Benchmark/dataset","layer":"Gates"}
+{"id":"age-0101","section":"Benchmarks & Datasets","subcategory":"Agent security benchmark","rtype":"Benchmark","title":"Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents","url":"https://openreview.net/forum?id=V4y0CpX4hK","venue":"ICLR","year":2025,"authors":"Hanrong Zhang; Jingyuan Huang; Kai Mei; Yifei Yao; Zhenting Wang; Chenlu Zhan; Hongwei Wang; Yongfeng Zhang","description":"Benchmarks prompt injection, memory poisoning, backdoors, mixed attacks, and defenses across 10 scenarios, more than 400 tools, 13 model backbones, and seven metrics.","why":"Maps security failures to prompts, plans, tools, and memory surfaces that graph designs must isolate separately; inter-agent trust propagation is outside its main evaluation target.","evidence":"Benchmark/dataset","layer":"Reliability"}
+{"id":"age-0102","section":"Benchmarks & Datasets","subcategory":"Prompt-injection evaluation","rtype":"Benchmark","title":"AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents","url":"https://openreview.net/forum?id=m1YYAQjO3w","venue":"NeurIPS Datasets & Benchmarks","year":2024,"authors":"Edoardo Debenedetti; Jie Zhang; Mislav Balunovic; Luca Beurer-Kellner; Marc Fischer; Florian Tramèr","description":"Provides an extensible adversarial environment with 97 realistic tasks and 629 security test cases for agents executing tools over untrusted data.","why":"Tests the instruction-data boundary that every external-input and artifact edge must preserve; compromised peer-agent messages require separate evaluation.","evidence":"Benchmark/dataset","layer":"Reliability"}
+{"id":"age-0103","section":"Benchmarks & Datasets","subcategory":"Agent misuse benchmark","rtype":"Benchmark","title":"AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents","url":"https://openreview.net/forum?id=AC5n7xHuR1","venue":"ICLR","year":2025,"authors":"Maksym Andriushchenko; Alexandra Souly; Mateusz Dziemian; Derek Duenas; Maxwell Lin; Justin Wang; Dan Hendrycks; Andy Zou; J. Zico Kolter; Matt Fredrikson; Yarin Gal; Xander Davies","description":"Evaluates refusal and retained task capability on 110 explicitly malicious multi-stage agent tasks with 440 augmented variants across 11 harm categories.","why":"Tests whether safety gates reject prohibited work and stop a compromised node from completing a harmful path; the score is not a general measure of agent-system safety.","evidence":"Benchmark/dataset","layer":"Gates"}
+{"id":"age-0104","section":"Reliability & Durable Execution","subcategory":"Structural prompt-injection defense","rtype":"Paper","title":"IPIGuard: A Novel Tool Dependency Graph-Based Defense Against Indirect Prompt Injection in LLM Agents","url":"https://aclanthology.org/2025.emnlp-main.53/","venue":"EMNLP","year":2025,"authors":"Hengyu An; Jinghuai Zhang; Tianyu Du; Chunyi Zhou; Qingming Li; Tao Lin; Shouling Ji","description":"Models task execution as traversal over a planned Tool Dependency Graph and separates action planning from interaction with untrusted external data.","why":"Shows how structural constraints on allowed tool edges can block unauthorized actions instead of relying only on prompts or classifiers.","evidence":"Peer-reviewed research","layer":"Work graphs"}
+{"id":"age-0105","section":"Protocols & Handoffs","subcategory":"Agent identity and delegated authorization","rtype":"Paper","title":"Identity Management for Agentic AI: The new frontier of authorization, authentication, and security for an AI agent world","url":"https://openid.net/wp-content/uploads/2025/10/Identity-Management-for-Agentic-AI.pdf","venue":"OpenID Foundation","year":2025,"authors":"OpenID Foundation Artificial Intelligence Identity Management Community Group","description":"Maps identity, authentication, delegated authority, consent, governance, and accountability gaps for autonomous agents against OAuth and OpenID Connect foundations.","why":"Provides a standards-grounded vocabulary for identity-bearing nodes and auditable delegation across agent and tool edges; it is a non-normative landscape whitepaper, not a protocol.","evidence":"Official documentation","layer":"Handoffs"}
+{"id":"age-0106","section":"Protocols & Handoffs","subcategory":"Agent capability and discovery schema","rtype":"Standard","title":"Open Agentic Schema Framework (OASF) v1.1.0","url":"https://github.com/agntcy/oasf/releases/tag/v1.1.0","venue":"AGNTCY Project, Linux Foundation","year":2026,"authors":"AGNTCY Project","description":"Defines versioned schemas and taxonomies for agent identity metadata, capabilities, skills, modules, interactions, and discovery records, including MCP and A2A alignment modules.","why":"Gives graph builders a portable node descriptor for capability-based discovery and routing validation; it describes capabilities but does not execute a graph or authorize an edge.","evidence":"Industry standard","layer":"Roles"}
+{"id":"age-0107","section":"State, Memory & Artifacts","subcategory":"Artifact provenance","rtype":"Standard","title":"PROV-DM: The PROV Data Model","url":"https://www.w3.org/TR/prov-dm/","venue":"W3C Recommendation","year":2013,"authors":"Luc Moreau; Paolo Missier","description":"Defines entities, activities, agents, derivation, attribution, responsibility, bundles, and collections for interoperable provenance records.","why":"Maps artifacts, execution steps, and responsible nodes into a portable lineage model; storage, access control, and cryptographic integrity remain implementation concerns.","evidence":"Industry standard","layer":"State"}
+{"id":"age-0108","section":"State, Memory & Artifacts","subcategory":"Cryptographic artifact provenance","rtype":"Paper","title":"in-toto: Providing farm-to-table guarantees for bits and bytes","url":"https://www.usenix.org/conference/usenixsecurity19/presentation/torres-arias","venue":"USENIX Security","year":2019,"authors":"Santiago Torres-Arias; Hammad Afzali; Trishank Karthik Kuppusamy; Reza Curtmola; Justin Cappos","description":"Introduces signed material and product links for supply-chain steps and verifies them against an expected layout, supported by analysis of 30 historical compromises and deployed integrations.","why":"Supplies a transferable receipt pattern that binds each artifact transformation to an authorized step and identity; it verifies process integrity, not semantic correctness.","evidence":"Peer-reviewed research","layer":"State"}
+{"id":"age-0109","section":"Reliability & Durable Execution","subcategory":"Portable workflow semantics","rtype":"Standard","title":"Serverless Workflow Specification v1.0.0","url":"https://serverlessworkflow.io/blog/releases/release-100/","venue":"Serverless Workflow, Cloud Native Computing Foundation","year":2025,"authors":"Serverless Workflow Project","description":"Defines a vendor-neutral workflow language with sequential and concurrent tasks, event correlation, service calls, error handling, retries, and timeouts.","why":"Provides an inspectable execution contract for production work graphs; conformance does not itself supply a durable runtime, an agent protocol, or model-level correctness.","evidence":"Industry standard","layer":"Reliability"}