Sync 80-resource JSONL atlas

resources.jsonl

@@ -24,7 +24,7 @@
 {"id":"age-0024","section":"Frameworks & SDKs","subcategory":"Pattern catalog","rtype":"Docs","title":"Strands Agents: Multi-Agent Patterns","url":"https://strandsagents.com/docs/user-guide/concepts/multi-agent/multi-agent-patterns/","venue":"Strands Agents Documentation","year":2026,"authors":"Strands Agents; AWS","description":"Documents several coordination shapes for composing agents, including supervisor, swarm, workflow, and graph-oriented patterns.","why":"Lets builders compare topology choices within one SDK instead of treating one pattern as universal.","evidence":"Official documentation","layer":"Topology"}
 {"id":"age-0025","section":"Frameworks & SDKs","subcategory":"Typed agents","rtype":"Docs","title":"Pydantic AI: Multi-Agent Applications","url":"https://pydantic.dev/docs/ai/guides/multi-agent-applications/","venue":"Pydantic AI Documentation","year":2026,"authors":"Pydantic","description":"Shows delegation, programmatic control flow, and graph-based state machines for composing typed Python agents.","why":"Useful for expressing node inputs, outputs, dependencies, and orchestration boundaries in ordinary application code.","evidence":"Official documentation","layer":"Topology"}
 {"id":"age-0026","section":"Frameworks & SDKs","subcategory":"Multi-agent orchestration","rtype":"Docs","title":"LlamaIndex: Multi-Agent Patterns","url":"https://developers.llamaindex.ai/python/framework/understanding/agent/multi_agent/","venue":"LlamaIndex Documentation","year":2026,"authors":"LlamaIndex","description":"Covers agent workflow, orchestrator, and planner-oriented approaches for coordinating specialized agents.","why":"Provides implementation patterns for choosing who owns delegation and how results return to the coordinating node.","evidence":"Official documentation","layer":"Handoffs"}
-{"id":"age-0027","section":"Frameworks & SDKs","subcategory":"Deterministic workflows","rtype":"Docs","title":"Google Agent Development Kit: Workflows","url":"https://adk.dev/workflows/","venue":"Google ADK Documentation","year":2025,"authors":"Google","description":"Documents sequential, parallel, and loop workflow agents that provide deterministic control around model-driven agents.","why":"Supplies composable work-graph operators while keeping deterministic orchestration distinct from model judgment.","evidence":"Official documentation","layer":"Work graphs"}
+{"id":"age-0027","section":"Frameworks & SDKs","subcategory":"Graph workflows","rtype":"Docs","title":"Google ADK: Graph-based Agent Workflows","url":"https://adk.dev/graphs/","venue":"Google ADK Documentation","year":2026,"authors":"Google","description":"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.","why":"Makes the graph load-bearing and inspectable while separating deterministic process control from model reasoning.","evidence":"Official documentation","layer":"Work graphs"}
 {"id":"age-0028","section":"Frameworks & SDKs","subcategory":"Graph workflows","rtype":"Docs","title":"Microsoft Agent Framework: Workflows","url":"https://learn.microsoft.com/en-us/agent-framework/workflows/","venue":"Microsoft Learn","year":2026,"authors":"Microsoft","description":"Describes workflows built from executors and explicit edges, with support for branching, aggregation, state, and checkpointing.","why":"Exposes the work graph as an inspectable program rather than hiding coordination inside prompts.","evidence":"Official documentation","layer":"Work graphs"}
 {"id":"age-0029","section":"Frameworks & SDKs","subcategory":"Graph runtime","rtype":"Docs","title":"LangGraph overview","url":"https://docs.langchain.com/oss/python/langgraph/overview","venue":"LangGraph Documentation","year":2025,"authors":"LangChain","description":"Introduces a low-level runtime for stateful agent graphs with durable execution, streaming, memory, and human intervention.","why":"A widely used substrate for implementing explicit nodes, edges, state transitions, and resumable work graphs.","evidence":"Official documentation","layer":"Work graphs"}
 {"id":"age-0030","section":"Protocols & Handoffs","subcategory":"In-process transfer","rtype":"Docs","title":"OpenAI Agents SDK: Handoffs","url":"https://openai.github.io/openai-agents-python/handoffs/","venue":"OpenAI Agents SDK Documentation","year":2026,"authors":"OpenAI","description":"Documents transfers from one agent to another, including tool-shaped handoff schemas, input filters, and callbacks.","why":"Turns an edge into an explicit contract controlling when ownership moves and what context crosses with it.","evidence":"Official documentation","layer":"Handoffs"}
@@ -49,3 +49,32 @@
 {"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"}
+{"id":"age-0052","section":"Research Foundations","subcategory":"Blackboard coordination","rtype":"Paper","title":"A Multi-Level Organization for Problem Solving Using Many, Diverse, Cooperating Sources of Knowledge","url":"https://www.ijcai.org/Proceedings/75/Papers/072.pdf","venue":"IJCAI","year":1975,"authors":"Lee D. Erman; Victor R. Lesser","description":"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.","why":"Provides an early architecture for loosely coupled specialist nodes coordinating through inspectable shared state instead of direct all-to-all calls.","evidence":"Peer-reviewed research","layer":"State"}
+{"id":"age-0053","section":"Research Foundations","subcategory":"Negotiated delegation","rtype":"Paper","title":"The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver","url":"https://doi.org/10.1109/TC.1980.1675516","venue":"IEEE Transactions on Computers","year":1980,"authors":"Reid G. Smith","description":"Defines a negotiation protocol in which managers announce tasks, potential contractors bid, and awards establish temporary problem-solving relationships.","why":"Supplies the classic contract for capability-aware delegation and auditable assignment edges between autonomous nodes.","evidence":"Peer-reviewed research","layer":"Handoffs"}
+{"id":"age-0054","section":"Start Here","subcategory":"Communication survey","rtype":"Paper","title":"The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why – A Survey from MARL to Emergent Language and LLMs","url":"https://openreview.net/pdf?id=LGsed0QQVq","venue":"Transactions on Machine Learning Research","year":2026,"authors":"Jingdi Chen; Hanqing Yang; Zongjun Liu; Carlee Joe-Wong","description":"Synthesizes multi-agent communication across reinforcement learning, emergent language, and LLM systems through sender, recipient, timing, content, and purpose decisions.","why":"Provides a design-oriented map for engineering edge selection, message timing, payloads, grounding, scalability, and interpretability.","evidence":"Peer-reviewed research","layer":"Handoffs"}
+{"id":"age-0055","section":"Research Foundations","subcategory":"Collaboration scaling","rtype":"Paper","title":"Scaling Large Language Model-based Multi-Agent Collaboration","url":"https://arxiv.org/abs/2406.07155","venue":"ICLR","year":2025,"authors":"Chen Qian et al.","description":"Introduces MacNet, a DAG-based collaboration architecture executed in topological order, and studies communication structure while scaling experiments beyond 1,000 agents.","why":"Makes topology a causal scaling variable rather than assuming that larger teams or denser communication automatically help.","evidence":"Peer-reviewed research","layer":"Topology"}
+{"id":"age-0056","section":"Research Foundations","subcategory":"Holistic orchestration","rtype":"Paper","title":"MAS-Orchestra: Understanding and Improving Multi-Agent Reasoning Through Holistic Orchestration and Controlled Benchmarks","url":"https://openreview.net/forum?id=3fGXBm4c3S","venue":"ICML","year":2026,"authors":"Zixuan Ke et al.","description":"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.","why":"Tests when whole-system graph structure helps instead of attributing gains to coordination without controlled task evidence.","evidence":"Peer-reviewed research","layer":"Work graphs"}
+{"id":"age-0057","section":"Research Foundations","subcategory":"Conditional topology","rtype":"Paper","title":"CARD: Towards Conditional Design of Multi-agent Topological Structures","url":"https://openreview.net/forum?id=JgvJdICc6P","venue":"ICLR","year":2026,"authors":"Tongtong Wu et al.","description":"Generates communication graphs conditioned on agent roles, models, tools, and data sources, and adapts topology as task resources change.","why":"Treats the available capabilities and directed edges as a versionable organizational artifact rather than a fixed team template.","evidence":"Peer-reviewed research","layer":"Evolution"}
+{"id":"age-0058","section":"Reliability & Durable Execution","subcategory":"Resilient topology","rtype":"Paper","title":"ResMAS: Resilience Optimization in LLM-based Multi-agent Systems","url":"https://ojs.aaai.org/index.php/AAAI/article/view/40824","venue":"AAAI","year":2026,"authors":"Zhilun Zhou et al.","description":"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.","why":"Moves resilience from reactive recovery into the design of the graph itself and evaluates transfer to new tasks and models.","evidence":"Peer-reviewed research","layer":"Reliability"}
+{"id":"age-0059","section":"Research Foundations","subcategory":"System evolution","rtype":"Paper","title":"EvoMAS: Evolutionary Generation of Multi-Agent Systems","url":"https://openreview.net/forum?id=ic0AGRIkmY","venue":"ICML","year":2026,"authors":"Yuntong Hu et al.","description":"Evolves structured multi-agent configurations through trace-guided mutation, crossover, selection, and an experience memory across reasoning, coding, and tool-use tasks.","why":"Shows how an inspectable team specification can evolve from execution evidence while retaining executability and runtime robustness.","evidence":"Peer-reviewed research","layer":"Evolution"}
+{"id":"age-0060","section":"Critiques & Limits","subcategory":"Error propagation","rtype":"Paper","title":"Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems","url":"https://aclanthology.org/2025.emnlp-main.623/","venue":"EMNLP","year":2025,"authors":"Xu Shen et al.","description":"Causally studies correct and erroneous information propagation across communication densities and finds that moderately sparse structures can preserve useful diffusion while suppressing errors.","why":"Provides evidence against defaulting to dense graphs and links topology decisions to measured error amplification.","evidence":"Peer-reviewed research","layer":"Topology"}
+{"id":"age-0061","section":"Frameworks & SDKs","subcategory":"Graph-centric orchestration","rtype":"Paper","title":"MASFactory: A Graph-centric Framework for Orchestrating LLM-Based Multi-Agent Systems with Vibe Graphing","url":"https://aclanthology.org/2026.acl-demo.35/","venue":"ACL System Demonstrations","year":2026,"authors":"Yang Liu et al.","description":"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.","why":"Provides a direct implementation path from an inspectable organizational graph to execution and evaluates it on seven public benchmarks.","evidence":"Peer-reviewed research","layer":"Work graphs"}
+{"id":"age-0062","section":"Frameworks & SDKs","subcategory":"Directed agent graphs","rtype":"Docs","title":"AutoGen GraphFlow (Workflows)","url":"https://microsoft.github.io/autogen/dev/user-guide/agentchat-user-guide/graph-flow.html","venue":"Microsoft AutoGen Documentation","year":2026,"authors":"Microsoft","description":"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.","why":"Distinguishes the execution graph from the message graph, exposing both who acts next and what context each agent receives.","evidence":"Official documentation","layer":"Work graphs"}
+{"id":"age-0063","section":"Protocols & Handoffs","subcategory":"Durable remote tasks","rtype":"Standard","title":"Model Context Protocol: Tasks","url":"https://modelcontextprotocol.io/specification/2025-11-25/basic/utilities/tasks","venue":"MCP Specification 2025-11-25","year":2025,"authors":"Model Context Protocol project; Agentic AI Foundation","description":"Specifies experimental durable asynchronous request state machines with capability negotiation, polling, deferred results, progress, input-required states, cancellation, TTLs, and task-message correlation.","why":"Turns a remote tool or context edge into a recoverable task contract that can outlive one synchronous request.","evidence":"Official documentation","layer":"Reliability"}
+{"id":"age-0064","section":"Protocols & Handoffs","subcategory":"Secure agent messaging","rtype":"Docs","title":"Secure Low-Latency Interactive Messaging (SLIM)","url":"https://datatracker.ietf.org/doc/draft-mpsb-agntcy-slim/","venue":"IETF Datatracker; individual Internet-Draft","year":2026,"authors":"Luca Muscariello; Michele Papalini; Mauro Sardara; Sam Betts","description":"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.","why":"Adds a concrete secure transport substrate for high-volume graph edges that must cross process and organizational boundaries.","evidence":"Official documentation","layer":"Handoffs"}
+{"id":"age-0065","section":"Protocols & Handoffs","subcategory":"Agent identity and authorization","rtype":"Docs","title":"Accelerating the Adoption of Software and Artificial Intelligence Agent Identity and Authorization","url":"https://csrc.nist.gov/pubs/other/2026/02/05/accelerating-the-adoption-of-software-and-ai-agent/ipd","venue":"NIST NCCoE Initial Public Draft","year":2026,"authors":"Harold Booth; William Fisher; Ryan Galluzzo; Joshua Roberts","description":"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.","why":"Grounds graph roles and permissions in identity practice so delegation does not silently transfer more authority than an edge contract allows.","evidence":"Official documentation","layer":"Roles"}
+{"id":"age-0066","section":"State, Memory & Artifacts","subcategory":"Versioned work products","rtype":"Docs","title":"Google ADK: Artifacts","url":"https://adk.dev/artifacts/","venue":"Google ADK Documentation","year":2026,"authors":"Google","description":"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.","why":"Provides explicit, inspectable edge artifacts instead of forcing large or structured outputs through conversational context.","evidence":"Official documentation","layer":"State"}
+{"id":"age-0067","section":"State, Memory & Artifacts","subcategory":"Procedural memory","rtype":"Paper","title":"LEGOMem: Modular Procedural Memory for Multi-agent LLM Systems for Workflow Automation","url":"https://www.microsoft.com/en-us/research/publication/legomem-modular-procedural-memory-for-multi-agent-llm-systems-for-workflow-automation/","venue":"AAMAS","year":2026,"authors":"Dongge Han; Camille Couturier; Daniel Madrigal; Xuchao Zhang; Victor Ruehle; Saravan Rajmohan","description":"Decomposes execution trajectories into reusable procedural memories and allocates them to an orchestrator and specialist agents for workflow automation.","why":"Shows how durable experience can improve decomposition and delegation without collapsing every node's memory into one undifferentiated store.","evidence":"Peer-reviewed research","layer":"State"}
+{"id":"age-0068","section":"Verification & Evals","subcategory":"Adversarial agent detection","rtype":"Paper","title":"When Agents Go Rogue: Activation-Based Detection of Malicious Behaviors in Multi-Agent Systems","url":"https://openreview.net/forum?id=BnduUW8izq","venue":"ICML","year":2026,"authors":"Haowen Xu et al.","description":"Evaluates activation-space detection and restorative steering of compromised agents across five attack scenarios, multiple models, and synchronous and asynchronous multi-agent interactions.","why":"Tests a topology-agnostic gate for locating and repairing a malicious node when its messages appear superficially benign.","evidence":"Peer-reviewed research","layer":"Reliability"}
+{"id":"age-0069","section":"Reliability & Durable Execution","subcategory":"Intervention-driven debugging","rtype":"Paper","title":"DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems","url":"https://iclr.cc/virtual/2026/poster/10007537","venue":"ICLR","year":2026,"authors":"Ming Ma et al.","description":"Tests failure hypotheses by editing messages or plans and measuring whether each intervention repairs the outcome or advances execution in branching multi-agent traces.","why":"Turns graph debugging into causal repair experiments instead of relying on plausible but unverified post-hoc explanations.","evidence":"Peer-reviewed research","layer":"Reliability"}
+{"id":"age-0070","section":"Reliability & Durable Execution","subcategory":"Durable graph workflows","rtype":"Docs","title":"Microsoft Agent Framework: Durable Extension","url":"https://learn.microsoft.com/en-us/agent-framework/integrations/durable-extension","venue":"Microsoft Learn","year":2026,"authors":"Microsoft","description":"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.","why":"Shows how an agent graph can resume without losing context or repeating completed work after interruption.","evidence":"Official documentation","layer":"Reliability"}
+{"id":"age-0071","section":"Critiques & Limits","subcategory":"Control-flow security","rtype":"Paper","title":"Breaking and Fixing Defenses Against Control Flow Hijacking in Multi-Agent Systems","url":"https://openreview.net/forum?id=PNU9Rj5RDQ","venue":"ICLR","year":2026,"authors":"Rishi Dev Jha; Harold Triedman; Justin Wagle; Vitaly Shmatikov","description":"Demonstrates attacks against alignment-check defenses and introduces ControlValve, which generates permitted control-flow graphs and enforces least privilege for each agent invocation.","why":"Makes invocation authority and contextual permissions explicit on every edge instead of trusting a separate checker that can also be hijacked.","evidence":"Peer-reviewed research","layer":"Reliability"}
+{"id":"age-0072","section":"Observability & Cost","subcategory":"Budget-aware topology","rtype":"Paper","title":"BAMAS: Structuring Budget-Aware Multi-Agent Systems","url":"https://ojs.aaai.org/index.php/AAAI/article/view/40226","venue":"AAAI","year":2026,"authors":"Liming Yang; Junyu Luo; Xuanzhe Liu; Yiling Lou; Zhenpeng Chen","description":"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.","why":"Jointly engineers node selection, edge structure, and spend instead of optimizing quality while treating inference cost as an afterthought.","evidence":"Peer-reviewed research","layer":"Observability & cost"}
+{"id":"age-0073","section":"Observability & Cost","subcategory":"Workflow reconstruction","rtype":"Paper","title":"AgentXRay: White-Boxing Agentic Systems via Workflow Reconstruction","url":"https://arxiv.org/abs/2602.05353","venue":"ICML","year":2026,"authors":"Ruijie Shi et al.","description":"Reconstructs an editable explicit stand-in workflow for a black-box agentic system from input-output behavior using iterative search and evaluation.","why":"Offers a path to inspect, compare, and modify systems whose load-bearing workflow is hidden behind an API.","evidence":"Peer-reviewed research","layer":"Observability & cost"}
+{"id":"age-0074","section":"Benchmarks & Datasets","subcategory":"Distributed coordination","rtype":"Benchmark","title":"SILO-BENCH: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems","url":"https://aclanthology.org/2026.acl-long.1354/","venue":"ACL","year":2026,"authors":"Yuzhe Zhang et al.","description":"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.","why":"Tests whether more nodes and denser edges overcome distributed information constraints under explicit coordination and cost measures.","evidence":"Benchmark/dataset","layer":"Topology"}
+{"id":"age-0075","section":"Benchmarks & Datasets","subcategory":"Dynamic asynchronous evaluation","rtype":"Benchmark","title":"Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments","url":"https://openreview.net/forum?id=9gw03JpKK4","venue":"ICLR","year":2026,"authors":"Romain Froger et al.","description":"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.","why":"Makes delays, environmental change, peer communication, and externally checked writes first-class work-graph conditions.","evidence":"Benchmark/dataset","layer":"Work graphs"}
+{"id":"age-0076","section":"Benchmarks & Datasets","subcategory":"Adversarial multi-agent safety","rtype":"Benchmark","title":"TAMAS: Benchmarking Adversarial Risks in Multi-Agent LLM Systems","url":"https://aclanthology.org/2026.acl-long.1442/","venue":"ACL","year":2026,"authors":"Ishan Kavathekar; Hemang Jain; Ameya Rathod; Ponnurangam Kumaraguru; Tanuja Ganu","description":"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.","why":"Evaluates whether safety controls preserve useful work while attacks exploit the distinctive trust and communication surfaces of a multi-agent graph.","evidence":"Benchmark/dataset","layer":"Reliability"}
+{"id":"age-0077","section":"Benchmarks & Datasets","subcategory":"Failure attribution","rtype":"Benchmark","title":"Seeing the Whole Elephant: A Benchmark for Failure Attribution in LLM-based Multi-Agent Systems","url":"https://aclanthology.org/2026.acl-long.912/","venue":"ACL","year":2026,"authors":"Mengzhuo Chen et al.","description":"Introduces TraceElephant with full execution traces and reproducible environments for attributing failures to responsible agents and decisive steps.","why":"Measures causal diagnosis over nodes, messages, plans, and dependencies instead of evaluating only the final team output.","evidence":"Benchmark/dataset","layer":"Observability & cost"}
+{"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"}