Sync 80-resource CSV atlas

resources.csv

@@ -25,7 +25,7 @@ age-0023,Frameworks & SDKs,Agent orchestration,Docs,OpenAI Agents SDK: Agent orc
 age-0024,Frameworks & SDKs,Pattern catalog,Docs,Strands Agents: Multi-Agent Patterns,https://strandsagents.com/docs/user-guide/concepts/multi-agent/multi-agent-patterns/,Strands Agents Documentation,2026,Strands Agents; AWS,"Documents several coordination shapes for composing agents, including supervisor, swarm, workflow, and graph-oriented patterns.",Lets builders compare topology choices within one SDK instead of treating one pattern as universal.,Official documentation,Topology
 age-0025,Frameworks & SDKs,Typed agents,Docs,Pydantic AI: Multi-Agent Applications,https://pydantic.dev/docs/ai/guides/multi-agent-applications/,Pydantic AI Documentation,2026,Pydantic,"Shows delegation, programmatic control flow, and graph-based state machines for composing typed Python agents.","Useful for expressing node inputs, outputs, dependencies, and orchestration boundaries in ordinary application code.",Official documentation,Topology
 age-0026,Frameworks & SDKs,Multi-agent orchestration,Docs,LlamaIndex: Multi-Agent Patterns,https://developers.llamaindex.ai/python/framework/understanding/agent/multi_agent/,LlamaIndex Documentation,2026,LlamaIndex,"Covers agent workflow, orchestrator, and planner-oriented approaches for coordinating specialized agents.",Provides implementation patterns for choosing who owns delegation and how results return to the coordinating node.,Official documentation,Handoffs
-age-0027,Frameworks & SDKs,Deterministic workflows,Docs,Google Agent Development Kit: Workflows,https://adk.dev/workflows/,Google ADK Documentation,2025,Google,"Documents sequential, parallel, and loop workflow agents that provide deterministic control around model-driven agents.",Supplies composable work-graph operators while keeping deterministic orchestration distinct from model judgment.,Official documentation,Work graphs
+age-0027,Frameworks & SDKs,Graph workflows,Docs,Google ADK: Graph-based Agent Workflows,https://adk.dev/graphs/,Google ADK Documentation,2026,Google,"Documents declarative workflows whose nodes combine agents, tools, functions, and human input through explicit edges, typed data passing, routing, branching, state, fan-out and join, loops, escalation, and nesting.",Makes the graph load-bearing and inspectable while separating deterministic process control from model reasoning.,Official documentation,Work graphs
 age-0028,Frameworks & SDKs,Graph workflows,Docs,Microsoft Agent Framework: Workflows,https://learn.microsoft.com/en-us/agent-framework/workflows/,Microsoft Learn,2026,Microsoft,"Describes workflows built from executors and explicit edges, with support for branching, aggregation, state, and checkpointing.",Exposes the work graph as an inspectable program rather than hiding coordination inside prompts.,Official documentation,Work graphs
 age-0029,Frameworks & SDKs,Graph runtime,Docs,LangGraph overview,https://docs.langchain.com/oss/python/langgraph/overview,LangGraph Documentation,2025,LangChain,"Introduces a low-level runtime for stateful agent graphs with durable execution, streaming, memory, and human intervention.","A widely used substrate for implementing explicit nodes, edges, state transitions, and resumable work graphs.",Official documentation,Work graphs
 age-0030,Protocols & Handoffs,In-process transfer,Docs,OpenAI Agents SDK: Handoffs,https://openai.github.io/openai-agents-python/handoffs/,OpenAI Agents SDK Documentation,2026,OpenAI,"Documents transfers from one agent to another, including tool-shaped handoff schemas, input filters, and callbacks.",Turns an edge into an explicit contract controlling when ownership moves and what context crosses with it.,Official documentation,Handoffs
@@ -50,3 +50,32 @@ age-0048,Critiques & Limits,Self-correction limits,Paper,Large Language Models C
 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
+age-0052,Research Foundations,Blackboard coordination,Paper,"A Multi-Level Organization for Problem Solving Using Many, Diverse, Cooperating Sources of Knowledge",https://www.ijcai.org/Proceedings/75/Papers/072.pdf,IJCAI,1975,Lee D. Erman; Victor R. Lesser,"Presents the Hearsay-II multi-level blackboard, where independent knowledge sources react to shared hypotheses, create explicit structural dependencies, and verify or revise one another's contributions.",Provides an early architecture for loosely coupled specialist nodes coordinating through inspectable shared state instead of direct all-to-all calls.,Peer-reviewed research,State
+age-0053,Research Foundations,Negotiated delegation,Paper,The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver,https://doi.org/10.1109/TC.1980.1675516,IEEE Transactions on Computers,1980,Reid G. Smith,"Defines a negotiation protocol in which managers announce tasks, potential contractors bid, and awards establish temporary problem-solving relationships.",Supplies the classic contract for capability-aware delegation and auditable assignment edges between autonomous nodes.,Peer-reviewed research,Handoffs
+age-0054,Start Here,Communication survey,Paper,"The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why – A Survey from MARL to Emergent Language and LLMs",https://openreview.net/pdf?id=LGsed0QQVq,Transactions on Machine Learning Research,2026,Jingdi Chen; Hanqing Yang; Zongjun Liu; Carlee Joe-Wong,"Synthesizes multi-agent communication across reinforcement learning, emergent language, and LLM systems through sender, recipient, timing, content, and purpose decisions.","Provides a design-oriented map for engineering edge selection, message timing, payloads, grounding, scalability, and interpretability.",Peer-reviewed research,Handoffs
+age-0055,Research Foundations,Collaboration scaling,Paper,Scaling Large Language Model-based Multi-Agent Collaboration,https://arxiv.org/abs/2406.07155,ICLR,2025,Chen Qian et al.,"Introduces MacNet, a DAG-based collaboration architecture executed in topological order, and studies communication structure while scaling experiments beyond 1,000 agents.",Makes topology a causal scaling variable rather than assuming that larger teams or denser communication automatically help.,Peer-reviewed research,Topology
+age-0056,Research Foundations,Holistic orchestration,Paper,MAS-Orchestra: Understanding and Improving Multi-Agent Reasoning Through Holistic Orchestration and Controlled Benchmarks,https://openreview.net/forum?id=3fGXBm4c3S,ICML,2026,Zixuan Ke et al.,"Formulates orchestration as reinforcement-learned generation of a complete multi-agent program and evaluates it across controlled dimensions including depth, horizon, breadth, parallelism, and robustness.",Tests when whole-system graph structure helps instead of attributing gains to coordination without controlled task evidence.,Peer-reviewed research,Work graphs
+age-0057,Research Foundations,Conditional topology,Paper,CARD: Towards Conditional Design of Multi-agent Topological Structures,https://openreview.net/forum?id=JgvJdICc6P,ICLR,2026,Tongtong Wu et al.,"Generates communication graphs conditioned on agent roles, models, tools, and data sources, and adapts topology as task resources change.",Treats the available capabilities and directed edges as a versionable organizational artifact rather than a fixed team template.,Peer-reviewed research,Evolution
+age-0058,Reliability & Durable Execution,Resilient topology,Paper,ResMAS: Resilience Optimization in LLM-based Multi-agent Systems,https://ojs.aaai.org/index.php/AAAI/article/view/40824,AAAI,2026,Zhilun Zhou et al.,Learns task-specific resilient communication topologies and topology-aware prompts after measuring how graph structure and node instructions affect performance under agent failures and other perturbations.,Moves resilience from reactive recovery into the design of the graph itself and evaluates transfer to new tasks and models.,Peer-reviewed research,Reliability
+age-0059,Research Foundations,System evolution,Paper,EvoMAS: Evolutionary Generation of Multi-Agent Systems,https://openreview.net/forum?id=ic0AGRIkmY,ICML,2026,Yuntong Hu et al.,"Evolves structured multi-agent configurations through trace-guided mutation, crossover, selection, and an experience memory across reasoning, coding, and tool-use tasks.",Shows how an inspectable team specification can evolve from execution evidence while retaining executability and runtime robustness.,Peer-reviewed research,Evolution
+age-0060,Critiques & Limits,Error propagation,Paper,Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems,https://aclanthology.org/2025.emnlp-main.623/,EMNLP,2025,Xu Shen et al.,Causally studies correct and erroneous information propagation across communication densities and finds that moderately sparse structures can preserve useful diffusion while suppressing errors.,Provides evidence against defaulting to dense graphs and links topology decisions to measured error amplification.,Peer-reviewed research,Topology
+age-0061,Frameworks & SDKs,Graph-centric orchestration,Paper,MASFactory: A Graph-centric Framework for Orchestrating LLM-Based Multi-Agent Systems with Vibe Graphing,https://aclanthology.org/2026.acl-demo.35/,ACL System Demonstrations,2026,Yang Liu et al.,"Compiles natural-language intent into an editable workflow specification and executable directed graph, with reusable components, topology preview, runtime tracing, multimodal messages, and human interaction.",Provides a direct implementation path from an inspectable organizational graph to execution and evaluates it on seven public benchmarks.,Peer-reviewed research,Work graphs
+age-0062,Frameworks & SDKs,Directed agent graphs,Docs,AutoGen GraphFlow (Workflows),https://microsoft.github.io/autogen/dev/user-guide/agentchat-user-guide/graph-flow.html,Microsoft AutoGen Documentation,2026,Microsoft,"Implements directed multi-agent execution graphs with sequential, parallel, conditional, fan-in, and cyclic paths, edge conditions, activation groups, safe loop exits, and separately configurable message filtering. The feature is explicitly experimental.","Distinguishes the execution graph from the message graph, exposing both who acts next and what context each agent receives.",Official documentation,Work graphs
+age-0063,Protocols & Handoffs,Durable remote tasks,Standard,Model Context Protocol: Tasks,https://modelcontextprotocol.io/specification/2025-11-25/basic/utilities/tasks,MCP Specification 2025-11-25,2025,Model Context Protocol project; Agentic AI Foundation,"Specifies experimental durable asynchronous request state machines with capability negotiation, polling, deferred results, progress, input-required states, cancellation, TTLs, and task-message correlation.",Turns a remote tool or context edge into a recoverable task contract that can outlive one synchronous request.,Official documentation,Reliability
+age-0064,Protocols & Handoffs,Secure agent messaging,Docs,Secure Low-Latency Interactive Messaging (SLIM),https://datatracker.ietf.org/doc/draft-mpsb-agntcy-slim/,IETF Datatracker; individual Internet-Draft,2026,Luca Muscariello; Michele Papalini; Mauro Sardara; Sam Betts,"Proposes a transport layer for A2A and MCP using gRPC over HTTP/2 and HTTP/3 with stream multiplexing, flow control, group communication, native RPC semantics, and MLS end-to-end encryption. It is an individual informational Internet-Draft with no formal IETF standing.",Adds a concrete secure transport substrate for high-volume graph edges that must cross process and organizational boundaries.,Official documentation,Handoffs
+age-0065,Protocols & Handoffs,Agent identity and authorization,Docs,Accelerating the Adoption of Software and Artificial Intelligence Agent Identity and Authorization,https://csrc.nist.gov/pubs/other/2026/02/05/accelerating-the-adoption-of-software-and-ai-agent/ipd,NIST NCCoE Initial Public Draft,2026,Harold Booth; William Fisher; Ryan Galluzzo; Joshua Roberts,"Outlines considerations and open questions for standards-based identity, authorization, auditing, and non-repudiation when software and AI agents access enterprise systems and take actions. The concept paper remains an initial public draft under review.",Grounds graph roles and permissions in identity practice so delegation does not silently transfer more authority than an edge contract allows.,Official documentation,Roles
+age-0066,"State, Memory & Artifacts",Versioned work products,Docs,Google ADK: Artifacts,https://adk.dev/artifacts/,Google ADK Documentation,2026,Google,"Defines named, automatically versioned binary work products that agents and tools can save, load, list, and exchange within session-scoped or persistent user-scoped namespaces.","Provides explicit, inspectable edge artifacts instead of forcing large or structured outputs through conversational context.",Official documentation,State
+age-0067,"State, Memory & Artifacts",Procedural memory,Paper,LEGOMem: Modular Procedural Memory for Multi-agent LLM Systems for Workflow Automation,https://www.microsoft.com/en-us/research/publication/legomem-modular-procedural-memory-for-multi-agent-llm-systems-for-workflow-automation/,AAMAS,2026,Dongge Han; Camille Couturier; Daniel Madrigal; Xuchao Zhang; Victor Ruehle; Saravan Rajmohan,Decomposes execution trajectories into reusable procedural memories and allocates them to an orchestrator and specialist agents for workflow automation.,Shows how durable experience can improve decomposition and delegation without collapsing every node's memory into one undifferentiated store.,Peer-reviewed research,State
+age-0068,Verification & Evals,Adversarial agent detection,Paper,When Agents Go Rogue: Activation-Based Detection of Malicious Behaviors in Multi-Agent Systems,https://openreview.net/forum?id=BnduUW8izq,ICML,2026,Haowen Xu et al.,"Evaluates activation-space detection and restorative steering of compromised agents across five attack scenarios, multiple models, and synchronous and asynchronous multi-agent interactions.",Tests a topology-agnostic gate for locating and repairing a malicious node when its messages appear superficially benign.,Peer-reviewed research,Reliability
+age-0069,Reliability & Durable Execution,Intervention-driven debugging,Paper,DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems,https://iclr.cc/virtual/2026/poster/10007537,ICLR,2026,Ming Ma et al.,Tests failure hypotheses by editing messages or plans and measuring whether each intervention repairs the outcome or advances execution in branching multi-agent traces.,Turns graph debugging into causal repair experiments instead of relying on plausible but unverified post-hoc explanations.,Peer-reviewed research,Reliability
+age-0070,Reliability & Durable Execution,Durable graph workflows,Docs,Microsoft Agent Framework: Durable Extension,https://learn.microsoft.com/en-us/agent-framework/integrations/durable-extension,Microsoft Learn,2026,Microsoft,"Adds persisted sessions, checkpointed progress, failure recovery, external-event waits, and distributed hosting to agents, multi-agent orchestrations, and graph workflows. The documented packages remain prerelease.",Shows how an agent graph can resume without losing context or repeating completed work after interruption.,Official documentation,Reliability
+age-0071,Critiques & Limits,Control-flow security,Paper,Breaking and Fixing Defenses Against Control Flow Hijacking in Multi-Agent Systems,https://openreview.net/forum?id=PNU9Rj5RDQ,ICLR,2026,Rishi Dev Jha; Harold Triedman; Justin Wagle; Vitaly Shmatikov,"Demonstrates attacks against alignment-check defenses and introduces ControlValve, which generates permitted control-flow graphs and enforces least privilege for each agent invocation.",Makes invocation authority and contextual permissions explicit on every edge instead of trusting a separate checker that can also be hijacked.,Peer-reviewed research,Reliability
+age-0072,Observability & Cost,Budget-aware topology,Paper,BAMAS: Structuring Budget-Aware Multi-Agent Systems,https://ojs.aaai.org/index.php/AAAI/article/view/40226,AAAI,2026,Liming Yang; Junyu Luo; Xuanzhe Liu; Yiling Lou; Zhenpeng Chen,"Selects an LLM team with integer programming and then learns its collaboration topology under an explicit budget, reporting comparable performance with cost reductions of up to 86% on three tasks.","Jointly engineers node selection, edge structure, and spend instead of optimizing quality while treating inference cost as an afterthought.",Peer-reviewed research,Observability & cost
+age-0073,Observability & Cost,Workflow reconstruction,Paper,AgentXRay: White-Boxing Agentic Systems via Workflow Reconstruction,https://arxiv.org/abs/2602.05353,ICML,2026,Ruijie Shi et al.,Reconstructs an editable explicit stand-in workflow for a black-box agentic system from input-output behavior using iterative search and evaluation.,"Offers a path to inspect, compare, and modify systems whose load-bearing workflow is hidden behind an API.",Peer-reviewed research,Observability & cost
+age-0074,Benchmarks & Datasets,Distributed coordination,Benchmark,SILO-BENCH: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems,https://aclanthology.org/2026.acl-long.1354/,ACL,2026,Yuzhe Zhang et al.,"Benchmarks free-form coordination under information silos across 30 exact-answer tasks, three communication protocols, six agent scales, and three models while recording success, tokens, and communication density.",Tests whether more nodes and denser edges overcome distributed information constraints under explicit coordination and cost measures.,Benchmark/dataset,Topology
+age-0075,Benchmarks & Datasets,Dynamic asynchronous evaluation,Benchmark,Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments,https://openreview.net/forum?id=9gw03JpKK4,ICLR,2026,Romain Froger et al.,"Evaluates agents in asynchronous simulated environments across execution, search, ambiguity, adaptation, temporal reasoning, noise, and agent-to-agent collaboration, with action-level verifiers and structured traces.","Makes delays, environmental change, peer communication, and externally checked writes first-class work-graph conditions.",Benchmark/dataset,Work graphs
+age-0076,Benchmarks & Datasets,Adversarial multi-agent safety,Benchmark,TAMAS: Benchmarking Adversarial Risks in Multi-Agent LLM Systems,https://aclanthology.org/2026.acl-long.1442/,ACL,2026,Ishan Kavathekar; Hemang Jain; Ameya Rathod; Ponnurangam Kumaraguru; Tanuja Ganu,"Provides five scenarios with 300 adversarial instances, six attack types, 211 tools, 100 harmless tasks, and multiple AutoGen and CrewAI interaction configurations, plus an Effective Robustness Score.",Evaluates whether safety controls preserve useful work while attacks exploit the distinctive trust and communication surfaces of a multi-agent graph.,Benchmark/dataset,Reliability
+age-0077,Benchmarks & Datasets,Failure attribution,Benchmark,Seeing the Whole Elephant: A Benchmark for Failure Attribution in LLM-based Multi-Agent Systems,https://aclanthology.org/2026.acl-long.912/,ACL,2026,Mengzhuo Chen et al.,Introduces TraceElephant with full execution traces and reproducible environments for attributing failures to responsible agents and decisive steps.,"Measures causal diagnosis over nodes, messages, plans, and dependencies instead of evaluating only the final team output.",Benchmark/dataset,Observability & cost
+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