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age-0001 | Start Here | Architecture guide | Blog | A practical guide to building agents | https://openai.com/business/guides-and-resources/a-practical-guide-to-building-ai-agents/ | OpenAI Guides & Resources | 2,025 | OpenAI | Explains when agents are appropriate, how to define their tools and instructions, and how to move from one agent to manager and handoff-based multi-agent patterns. | A useful first check on whether distinct roles justify the coordination cost of a graph. | Practitioner analysis | Roles |
age-0002 | Start Here | Architecture guide | Blog | Building effective agents | https://www.anthropic.com/engineering/building-effective-agents | Anthropic Engineering | 2,024 | Erik Schluntz; Barry Zhang | Distinguishes workflows from autonomous agents and presents routing, parallelization, orchestrator-worker, and evaluator-optimizer patterns. | Provides a compact topology vocabulary and a strong case for adding coordination only when the task demands it. | Practitioner analysis | Topology |
age-0003 | Research Foundations | Agent foundations | Paper | Agent-Oriented Programming | https://doi.org/10.1016/0004-3702(93)90034-9 | Artificial Intelligence | 1,993 | 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 | 1,995 | 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 | 1,986 | 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 | 2,016 | Jakob Foerster; Ioannis Alexandros Assael; Nando de Freitas; Shimon Whiteson | 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 | 2,019 | 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 Conversations | https://openreview.net/forum?id=BAakY1hNKS | COLM | 2,024 | Qingyun Wu; Gagan Bansal; Jieyu Zhang; Yiran Wu; Beibin Li; Erkang Zhu; Li Jiang; Xiaoyun Zhang; Shaokun Zhang; Jiale Liu; Ahmed Hassan Awadallah; Ryen W. White; Doug Burger; Chi Wang | 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 | 2,024 | 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 | 2,024 | 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://openreview.net/forum?id=LkzuPorQ5L | ICLR | 2,025 | 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 | 2,025 | 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://openreview.net/forum?id=t9U3LW7JVX | ICLR | 2,025 | 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 | 2,025 | 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 | 2,026 | 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 | 2,024 | 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 | 2,024 | 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://openreview.net/forum?id=vAElhFcKW6 | NeurIPS | 2,023 | 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 | 2,024 | 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 | 2,024 | 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 | 2,025 | 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 | 2,026 | 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 |
age-0023 | Frameworks & SDKs | Agent orchestration | Docs | OpenAI Agents SDK: Agent orchestration | https://openai.github.io/openai-agents-python/multi_agent/ | OpenAI Agents SDK Documentation | 2,026 | OpenAI | Explains manager-style orchestration with agents exposed as tools and decentralized orchestration through handoffs. | Makes the centralized-versus-decentralized topology choice explicit in a production SDK. | Official documentation | Topology |
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 | 2,026 | 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 | 2,026 | 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 | 2,026 | 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 | Graph workflows | Docs | Google ADK: Graph-based Agent Workflows | https://adk.dev/graphs/ | Google ADK Documentation | 2,026 | 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 | 2,026 | 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 | 2,025 | 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 | 2,026 | 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 |
age-0031 | Protocols & Handoffs | Tool and context protocol | Standard | Model Context Protocol Specification 2025-11-25 | https://modelcontextprotocol.io/specification/2025-11-25 | MCP Specification 2025-11-25 | 2,025 | Model Context Protocol project; Agentic AI Foundation | Specifies a client-server protocol through which AI applications discover and use tools, resources, prompts, and contextual data. | Standardizes capability and context edges, while remaining distinct from a protocol for delegating work between autonomous agents. | Industry standard | Handoffs |
age-0032 | Protocols & Handoffs | Agent interoperability | Standard | Agent2Agent Protocol Specification v1.0.0 | https://a2a-protocol.org/v1.0.0/specification/ | A2A Specification | 2,026 | A2A Protocol Working Group; Linux Foundation | Defines interoperable agent discovery, task lifecycle, messages, artifacts, streaming, asynchronous updates, version negotiation, and multiple protocol bindings across service boundaries. | Provides a version-pinned wire contract for cross-system handoffs where agents cannot share an in-process runtime. | Industry standard | Handoffs |
age-0033 | State, Memory & Artifacts | Checkpointing | Docs | LangGraph Persistence | https://docs.langchain.com/oss/python/langgraph/persistence | LangGraph Documentation | 2,025 | LangChain | Documents thread-scoped checkpoints, saved state, replay, state inspection, and memory storage for LangGraph runs. | Shows how graph state can become the recoverable system of record instead of living only in model context. | Official documentation | State |
age-0034 | State, Memory & Artifacts | Conversation state | Docs | OpenAI Agents SDK: Sessions | https://openai.github.io/openai-agents-python/sessions/ | OpenAI Agents SDK Documentation | 2,026 | OpenAI | Documents persistent conversation history that can be loaded and updated across repeated agent runs. | Provides a bounded mechanism for carrying state across nodes and turns without manually rebuilding every prompt. | Official documentation | State |
age-0035 | Verification & Evals | Workflow evaluation | Docs | Evaluate agent workflows | https://developers.openai.com/api/docs/guides/agent-evals | OpenAI API Documentation | 2,026 | OpenAI | Explains how to build datasets, graders, trace-based evaluations, and reproducible checks for agent workflows. | Makes gates testable at both the final outcome and the intermediate handoff level. | Official documentation | Gates |
age-0036 | Verification & Evals | Evaluation practice | Blog | Demystifying evals for AI agents | https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents | Anthropic Engineering | 2,026 | Mikaela Grace et al. | Presents practical guidance for defining tasks, outcomes, graders, and evaluation suites for agents with non-deterministic trajectories. | Helps turn vague reviewer judgments into evidence gates that can guide graph changes. | Practitioner analysis | Gates |
age-0037 | Verification & Evals | Evaluation framework | Tool | Inspect AI | https://inspect.aisi.org.uk/ | Inspect AI Documentation | 2,024 | UK AI Security Institute | Provides an open-source evaluation framework with tasks, solvers, scorers, sandboxed tools, and structured logs. | Supports reproducible gate nodes and traceable evidence for both individual agents and composed systems. | Maintained OSS project | Gates |
age-0038 | Reliability & Durable Execution | Durable actors and workflows | Docs | Dapr Agents introduction | https://docs.dapr.io/developing-ai/dapr-agents/dapr-agents-introduction/ | Dapr Documentation | 2,026 | Dapr; CNCF | Introduces an agent framework built on durable actors and workflows, with state, messaging, and recovery supplied by the Dapr runtime. | Shows how graph nodes can inherit distributed-systems durability instead of implementing recovery only in prompts. | Official documentation | Reliability |
age-0039 | Reliability & Durable Execution | Durable execution integration | Tool | Temporal integration for OpenAI Agents SDK | https://github.com/temporalio/sdk-python/tree/main/temporalio/contrib/openai_agents | Temporal Python SDK | 2,025 | Temporal | Integrates OpenAI agent runs, model calls, and tools with Temporal workflows and activities for durable execution. | Provides replay, retry, timeout, and recovery semantics beneath an agent graph without asking the model to manage them. | Maintained OSS project | Reliability |
age-0040 | Reliability & Durable Execution | Database-backed workflows | Docs | DBOS AI Quickstart | https://docs.dbos.dev/ai/ai-quickstart | DBOS Documentation | 2,026 | DBOS | Shows how to place AI application steps inside durable workflows whose progress is recorded and recoverable after interruption. | Offers a compact path from an agent prototype to resumable execution with explicit step boundaries. | Official documentation | Reliability |
age-0041 | Reliability & Durable Execution | Durable agent patterns | Docs | Restate Durable Agents | https://docs.restate.dev/ai/patterns/durable-agents | Restate Documentation | 2,026 | Restate | Documents durable agent patterns using persisted execution, reliable calls, retries, timers, and stateful services. | Maps common mid-graph failures to runtime guarantees rather than fragile application-level retry code. | Official documentation | Reliability |
age-0042 | Observability & Cost | Tracing | Docs | OpenAI Agents SDK: Tracing | https://openai.github.io/openai-agents-python/tracing/ | OpenAI Agents SDK Documentation | 2,026 | OpenAI | Documents traces and spans for agent runs, model generations, tool calls, handoffs, and guardrails. | Makes node and edge behavior inspectable so latency, failures, and expensive paths can be attributed correctly. | Official documentation | Observability & cost |
age-0043 | Observability & Cost | Telemetry standard | Standard | OpenTelemetry GenAI Semantic Conventions | https://github.com/open-telemetry/semantic-conventions-genai | OpenTelemetry GenAI repository | 2,026 | OpenTelemetry GenAI SIG | Develops shared telemetry names and attributes for generative-AI model, tool, and agent operations in traces, metrics, and events. | Helps graph telemetry remain portable across runtimes and observability vendors. | Maintained OSS project | Observability & cost |
age-0044 | Observability & Cost | Cost attribution | Docs | Arize Phoenix: Cost Tracking | https://arize.com/docs/phoenix/tracing/how-to-tracing/cost-tracking | Arize Phoenix Documentation | 2,026 | Arize AI | Explains how Phoenix derives and displays token usage and model cost from traced generative-AI calls. | Supports per-node and per-path cost accounting instead of treating a multi-agent run as one opaque bill. | Official documentation | Observability & cost |
age-0045 | Observability & Cost | Observability platform | Docs | LangSmith Observability | https://docs.langchain.com/langsmith/observability | LangSmith Documentation | 2,026 | 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 | 2,025 | 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 | 2,025 | 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://openreview.net/forum?id=IkmD3fKBPQ | ICLR | 2,024 | 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 | 2,025 | 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 | 2,026 | 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 | 2,026 | 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 | 1,975 | 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 | 1,980 | 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 | 2,026 | 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://openreview.net/forum?id=K3n5jPkrU6 | ICLR | 2,025 | Chen Qian; Zihao Xie; YiFei Wang; Wei Liu; Kunlun Zhu; Hanchen Xia; Yufan Dang; Zhuoyun Du; Weize Chen; Cheng Yang; Zhiyuan Liu; Maosong Sun | 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 | 2,026 | 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 | 2,026 | 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 | 2,026 | 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 | 2,026 | 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 | 2,025 | 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 | 2,026 | 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 | 2,026 | 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 | 2,025 | 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 | 2,026 | 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 | 2,026 | 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 | 2,026 | 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 | 2,026 | 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 | 2,026 | 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 | 2,026 | 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 | 2,026 | 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 | 2,026 | 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 | 2,026 | 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 | 2,026 | Ruijie Shi; Houbin Zhang; Yuecheng Han; Yuheng Wang; Jingru Fan; Runde Yang; Yufan Dang; Huatao Li; Dewen Liu; Yuan Cheng; Chen Qian | 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 | 2,026 | 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 | 2,026 | 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 | 2,026 | 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 | 2,026 | 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 | 2,025 | 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 | 2,026 | 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 | 2,026 | 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 | 2,026 | 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 | 2,026 | 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 | 2,026 | 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 | 2,025 | 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 | 2,025 | 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 | 2,025 | 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 | 2,025 | 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 | 2,026 | 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 | 2,024 | 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 | 2,025 | 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 | 2,025 | 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 | 2,025 | 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 | 2,026 | 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 | 2,026 | 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 | 2,026 | 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 | 2,026 | 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 | 2,026 | 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 | 2,025 | 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 | 2,025 | 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 | 2,024 | 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 |
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