{
  "date": "2026-09-06",
  "lastReviewed": "2026-09-06T10:53:43Z",
  "lastUpdated": "2026-09-06T10:53:43Z",
  "status": "published",
  "summary": "Early-September signals show enterprise AI moving toward governed agent extensions, policy-enforced evidence, cloud-integrated model choice, outcome-linked infrastructure, and measurable workforce change.",
  "domains": [
    "Agentic AI",
    "AI coding agents",
    "Models",
    "Open source",
    "Enterprise platforms",
    "Data and RAG",
    "Knowledge graphs",
    "AI infrastructure",
    "Token economics",
    "Governance and safety",
    "Business model innovation",
    "Industry adoption",
    "AGI & frontier research"
  ],
  "trends": [
    {
      "trendId": "trend-2026-09-06-001",
      "date": "2026-09-06",
      "title": "Content exclusions extend to agentic Copilot workflows",
      "tldr": "GitHub made content-exclusion policies available in the Copilot app and CLI so excluded files are not used as context in agentic workflows.",
      "domain": "Governance and safety",
      "productOrVendor": "GitHub Copilot",
      "industry": "Cross-industry",
      "architectureImpact": "Propagate repository, organization, and enterprise exclusion policy into retrieval, context assembly, and agent execution paths.",
      "businessImpact": "Content controls reduce exposure of sensitive code while enabling safer expansion of agent access.",
      "developmentImpact": "Create tests proving excluded paths never enter prompts, tool calls, caches, or generated artifacts.",
      "whyItMatters": "Agent safety depends on context boundaries, not only model quality. Exclusion policy is becoming a runtime data-control requirement.",
      "actionForLeaders": "Escalate",
      "sourceIds": [
        "src-2026-09-06-001"
      ],
      "sourceType": "official",
      "confidence": "high",
      "score": 95,
      "status": "published",
      "humanDecision": "approved"
    },
    {
      "trendId": "trend-2026-09-06-002",
      "date": "2026-09-06",
      "title": "Copilot adds broader agent sessions and extension controls",
      "tldr": "GitHub's weekly Copilot release expanded agent-session controls and brought MCP servers, plugins, skills, and canvases together in the Copilot app.",
      "domain": "AI coding agents",
      "productOrVendor": "GitHub Copilot",
      "industry": "Cross-industry",
      "architectureImpact": "Treat agent sessions and extensions as governed software supply-chain surfaces with explicit permissions, provenance, and lifecycle controls.",
      "businessImpact": "A unified extension experience can accelerate adoption while increasing review, maintenance, and support obligations.",
      "developmentImpact": "Test default execution and permission modes, session recovery, extension updates, and audit behavior before broad rollout.",
      "whyItMatters": "Coding assistants are becoming extensible agent platforms rather than isolated completion tools. Governance must cover the tools and sessions around the model.",
      "actionForLeaders": "Evaluate",
      "sourceIds": [
        "src-2026-09-06-002"
      ],
      "sourceType": "official",
      "confidence": "high",
      "score": 94,
      "status": "published",
      "humanDecision": "approved"
    },
    {
      "trendId": "trend-2026-09-06-003",
      "date": "2026-09-06",
      "title": "Claude Fable 5.1 enters Copilot with enterprise retention conditions",
      "tldr": "Claude Fable 5.1 became available across Copilot surfaces, with default data retention for safety classifiers and policy controls for enterprise administrators.",
      "domain": "Models",
      "productOrVendor": "Anthropic / GitHub Copilot",
      "industry": "Cross-industry",
      "architectureImpact": "Model routing must include retention, safety-classifier, residency, and zero-data-retention eligibility constraints.",
      "businessImpact": "Capability gains may carry data-handling trade-offs that affect procurement and regulated-workload eligibility.",
      "developmentImpact": "Add model-policy tests for retention defaults, eligible exceptions, and administrator enablement before production use.",
      "whyItMatters": "Model selection is inseparable from data governance. A stronger model can be unsuitable for a workload if its operational retention conditions conflict with policy.",
      "actionForLeaders": "Evaluate",
      "sourceIds": [
        "src-2026-09-06-003"
      ],
      "sourceType": "official",
      "confidence": "high",
      "score": 93,
      "status": "published",
      "humanDecision": "approved"
    },
    {
      "trendId": "trend-2026-09-06-004",
      "date": "2026-09-06",
      "title": "Microsoft reports expanded responsible-AI governance for agents",
      "tldr": "Microsoft's 2026 Responsible AI Transparency Report describes adaptive governance, technical risk management, evaluation, and controls for agentic AI.",
      "domain": "Governance and safety",
      "productOrVendor": "Microsoft",
      "industry": "Cross-industry",
      "architectureImpact": "Operationalize risk management, evaluation, misuse monitoring, and agent governance as lifecycle services rather than policy documents.",
      "businessImpact": "Transparency evidence can become a procurement differentiator as customers assess provider controls and accountability.",
      "developmentImpact": "Maintain evaluation suites, incident paths, and traceable control changes across model and agent releases.",
      "whyItMatters": "Agentic systems increase the pace and autonomy of change. Governance must therefore be adaptive, measurable, and connected to engineering practice.",
      "actionForLeaders": "Plan",
      "sourceIds": [
        "src-2026-09-06-004"
      ],
      "sourceType": "official",
      "confidence": "high",
      "score": 93,
      "status": "published",
      "humanDecision": "approved"
    },
    {
      "trendId": "trend-2026-09-06-005",
      "date": "2026-09-06",
      "title": "OpenAI reports governed AI adoption in a professional-services firm",
      "tldr": "Gilbert + Tobin describes scaled ChatGPT adoption supported by approved-task guidance, data controls, role-based access, and human accountability.",
      "domain": "Industry adoption",
      "productOrVendor": "OpenAI / Gilbert + Tobin",
      "industry": "Cross-industry",
      "architectureImpact": "Pair enterprise AI platforms with approved-use patterns, access controls, residency choices, and review workflows.",
      "businessImpact": "Adoption evidence is strongest when enablement and governance are designed together rather than treated as separate programs.",
      "developmentImpact": "Capture task-level outcomes and review requirements, then expand from demonstrated workflows instead of mandating undifferentiated usage.",
      "whyItMatters": "Enterprise adoption is an operating-model change, not just a license rollout. Human accountability and data handling remain part of the architecture.",
      "actionForLeaders": "Plan",
      "sourceIds": [
        "src-2026-09-06-005"
      ],
      "sourceType": "official",
      "confidence": "high",
      "score": 92,
      "status": "published",
      "humanDecision": "approved"
    },
    {
      "trendId": "trend-2026-09-06-006",
      "date": "2026-09-06",
      "title": "Copilot billing changes make AI usage controls more operational",
      "tldr": "GitHub announced upcoming Copilot billing and policy changes, including per-seat payment requirements, usage tracking, and continued availability of spend controls.",
      "domain": "Token economics",
      "productOrVendor": "GitHub Copilot",
      "industry": "Cross-industry",
      "architectureImpact": "Connect model and agent usage telemetry to identity, seat assignment, budget, and policy systems.",
      "businessImpact": "AI procurement now needs explicit cost allocation and controls as included usage and additional consumption interact.",
      "developmentImpact": "Instrument usage by workflow and team, and test overage behavior before the October billing changes take effect.",
      "whyItMatters": "Agent adoption turns software licensing into an operating-model concern. Leaders need unit economics and guardrails before usage scales.",
      "actionForLeaders": "Plan",
      "sourceIds": [
        "src-2026-09-06-006"
      ],
      "sourceType": "official",
      "confidence": "high",
      "score": 91,
      "status": "published",
      "humanDecision": "approved"
    },
    {
      "trendId": "trend-2026-09-06-007",
      "date": "2026-09-06",
      "title": "Policy-executed analysis outperforms unconstrained runtime planning in a benchmark",
      "tldr": "The MasterControl benchmark reports that policy-executed analysis met its full answer-and-evidence contract where a runtime-planning configuration did not.",
      "domain": "Data and RAG",
      "productOrVendor": "arXiv / MasterControl Seventeen Every Time",
      "industry": "Cross-industry",
      "architectureImpact": "Use explicit policies, evidence contracts, and deterministic control paths for high-consequence analytical workflows.",
      "businessImpact": "Reliability may depend more on control architecture than on adding model intelligence to unconstrained planning.",
      "developmentImpact": "Require answer-plus-evidence contracts and test policy enforcement separately from model reasoning quality.",
      "whyItMatters": "Agentic systems can fail at the interface between planning and execution. Explicit policy layers provide a practical way to bound that failure mode.",
      "actionForLeaders": "Evaluate",
      "sourceIds": [
        "src-2026-09-06-007"
      ],
      "sourceType": "research",
      "confidence": "high",
      "score": 91,
      "status": "published",
      "humanDecision": "approved"
    },
    {
      "trendId": "trend-2026-09-06-008",
      "date": "2026-09-06",
      "title": "Agentic legal-work platforms quantify model value against domain benchmarks",
      "tldr": "Legora reports using GPT-6 Astra for document review and benchmarking the workflow against financial-statement tasks while retaining professional judgment.",
      "domain": "Business model innovation",
      "productOrVendor": "OpenAI / Legora",
      "industry": "Cross-industry",
      "architectureImpact": "Integrate domain benchmarks, human checkpoints, and evidence capture into agentic product architectures.",
      "businessImpact": "Workflow-specific quality and throughput measures provide a stronger value proposition than generic model capability claims.",
      "developmentImpact": "Test planted-error detection, completeness, and escalation behavior on representative document sets.",
      "whyItMatters": "Vertical AI products increasingly compete on measurable workflow outcomes. The defensible layer is the benchmarked process, not model access alone.",
      "actionForLeaders": "Evaluate",
      "sourceIds": [
        "src-2026-09-06-008"
      ],
      "sourceType": "official",
      "confidence": "high",
      "score": 90,
      "status": "published",
      "humanDecision": "approved"
    },
    {
      "trendId": "trend-2026-09-06-009",
      "date": "2026-09-06",
      "title": "Research agents expose reliability limits on structured artifact editing",
      "tldr": "SLIDEFORGE presents an agent architecture that preserves native slide structure and evaluates component recovery, visual quality, and editability rather than judging screenshots alone.",
      "domain": "Agentic AI",
      "productOrVendor": "arXiv / SLIDEFORGE",
      "industry": "Cross-industry",
      "architectureImpact": "Represent complex artifacts as executable state graphs and validate structure, semantics, visual quality, and editability independently.",
      "businessImpact": "Artifact-preserving agents can create more durable value than systems that only generate plausible previews.",
      "developmentImpact": "Test native editability, layout preservation, component recovery, and regression behavior as separate acceptance criteria.",
      "whyItMatters": "Agent output quality is multidimensional. Production systems need artifact-level validation, not only language or image similarity scores.",
      "actionForLeaders": "Evaluate",
      "sourceIds": [
        "src-2026-09-06-009"
      ],
      "sourceType": "research",
      "confidence": "high",
      "score": 90,
      "status": "published",
      "humanDecision": "approved"
    },
    {
      "trendId": "trend-2026-09-06-010",
      "date": "2026-09-06",
      "title": "Amazon Quick adds natural-language custom app construction",
      "tldr": "AWS announced natural-language construction of custom apps in Amazon Quick for Plus, Professional, and Enterprise customers.",
      "domain": "Enterprise platforms",
      "productOrVendor": "AWS Amazon Quick",
      "industry": "Cross-industry",
      "architectureImpact": "Treat natural-language app generation as a governed application lifecycle with identity, data access, testing, and release controls.",
      "businessImpact": "Low-code generative app creation can shorten experimentation cycles while shifting value toward governance and process ownership.",
      "developmentImpact": "Require generated-app review, regression tests, permission checks, and reproducible configuration before release.",
      "whyItMatters": "Natural-language app builders move AI from assistant to software production surface. Enterprises need platform controls that match that expanded blast radius.",
      "actionForLeaders": "Experiment",
      "sourceIds": [
        "src-2026-09-06-010"
      ],
      "sourceType": "official",
      "confidence": "high",
      "score": 89,
      "status": "published",
      "humanDecision": "approved"
    },
    {
      "trendId": "trend-2026-09-06-011",
      "date": "2026-09-06",
      "title": "Dallas Fed finds early labor-market effects concentrated in exposed roles",
      "tldr": "Dallas Fed analysis reports early signs of AI automation effects in job postings while examining productivity and labor-market disruption from GenAI adoption.",
      "domain": "Human-AI collaboration",
      "productOrVendor": "Federal Reserve Bank of Dallas",
      "industry": "Cross-industry",
      "architectureImpact": "Include workforce telemetry, role redesign, human review, and skills transition in AI operating-model architecture.",
      "businessImpact": "AI investment decisions need workforce and capability planning alongside productivity expectations.",
      "developmentImpact": "Track task-level automation, exception rates, and skill requirements rather than treating job categories as static.",
      "whyItMatters": "The workforce impact is showing up unevenly across exposed roles. Leaders need evidence-led transition plans rather than aggregate assumptions about jobs.",
      "actionForLeaders": "Plan",
      "sourceIds": [
        "src-2026-09-06-011"
      ],
      "sourceType": "research",
      "confidence": "high",
      "score": 89,
      "status": "published",
      "humanDecision": "approved"
    },
    {
      "trendId": "trend-2026-09-06-012",
      "date": "2026-09-06",
      "title": "AWS makes Claude Fable 5.1 available through its model platform",
      "tldr": "AWS announced availability of Claude Fable 5.1 for coding, scientific research, and enterprise workflows through AWS infrastructure.",
      "domain": "Models",
      "productOrVendor": "AWS / Anthropic",
      "industry": "Cross-industry",
      "architectureImpact": "Evaluate model availability across cloud control planes, including networking, identity, observability, residency, and failover implications.",
      "businessImpact": "Multi-cloud model access can improve procurement leverage and workload portability but increases evaluation and governance surface area.",
      "developmentImpact": "Benchmark long-horizon reasoning and error rates through the intended cloud serving path, not only direct APIs.",
      "whyItMatters": "Frontier models are becoming infrastructure choices as much as model choices. Availability through a cloud platform changes deployment and operating assumptions.",
      "actionForLeaders": "Evaluate",
      "sourceIds": [
        "src-2026-09-06-012"
      ],
      "sourceType": "official",
      "confidence": "high",
      "score": 88,
      "status": "published",
      "humanDecision": "approved"
    },
    {
      "trendId": "trend-2026-09-06-013",
      "date": "2026-09-06",
      "title": "Microsoft frames infrastructure yield as the next AI operating constraint",
      "tldr": "Microsoft argues that AI infrastructure investment must translate into useful intelligence through better utilization, efficiency, and operational execution.",
      "domain": "AI infrastructure",
      "productOrVendor": "Microsoft",
      "industry": "Cross-industry",
      "architectureImpact": "Measure accelerator utilization, inference latency, workload efficiency, and outcome yield together rather than tracking capacity alone.",
      "businessImpact": "AI capital expenditure requires operating metrics that connect infrastructure spend to delivered business value.",
      "developmentImpact": "Add cost, latency, utilization, and quality telemetry to deployment acceptance criteria.",
      "whyItMatters": "Compute availability is not the same as useful AI capacity. Infrastructure planning now needs outcome-linked efficiency measures.",
      "actionForLeaders": "Plan",
      "sourceIds": [
        "src-2026-09-06-013"
      ],
      "sourceType": "official",
      "confidence": "medium",
      "score": 87,
      "status": "published",
      "humanDecision": "approved"
    }
  ],
  "sources": [
    {
      "sourceId": "src-2026-09-06-001",
      "organization": "GitHub Copilot",
      "title": "Content exclusions generally available in Copilot app and CLI",
      "date": "2026-09-02",
      "url": "https://github.blog/changelog/2026-09-02-content-exclusions-generally-available-in-copilot-app-and-cli/",
      "sourceType": "official",
      "whatItSupports": "GitHub made content-exclusion policies available in the Copilot app and CLI so excluded files are not used as context in agentic workflows."
    },
    {
      "sourceId": "src-2026-09-06-002",
      "organization": "GitHub Copilot",
      "title": "GitHub Copilot weekly releases — August 24",
      "date": "2026-08-28",
      "url": "https://github.blog/changelog/2026-08-28-github-copilot-weekly-releases-august-24/",
      "sourceType": "official",
      "whatItSupports": "GitHub's weekly Copilot release expanded agent-session controls and brought MCP servers, plugins, skills, and canvases together in the Copilot app."
    },
    {
      "sourceId": "src-2026-09-06-003",
      "organization": "Anthropic / GitHub Copilot",
      "title": "Claude Fable 5.1 is generally available in GitHub Copilot",
      "date": "2026-09-01",
      "url": "https://github.blog/changelog/2026-09-01-claude-fable-5-1-generally-available-in-github-copilot/",
      "sourceType": "official",
      "whatItSupports": "Claude Fable 5.1 became available across Copilot surfaces, with default data retention for safety classifiers and policy controls for enterprise administrators."
    },
    {
      "sourceId": "src-2026-09-06-004",
      "organization": "Microsoft",
      "title": "Responsible AI in 2026: How we are adapting for what's ahead",
      "date": "2026-09-01",
      "url": "https://blogs.microsoft.com/on-the-issues/2026/09/01/responsible-ai-in-2026-how-we-are-adapting-for-whats-ahead/",
      "sourceType": "official",
      "whatItSupports": "Microsoft's 2026 Responsible AI Transparency Report describes adaptive governance, technical risk management, evaluation, and controls for agentic AI."
    },
    {
      "sourceId": "src-2026-09-06-005",
      "organization": "OpenAI / Gilbert + Tobin",
      "title": "How law firm Gilbert + Tobin governs and scales AI with OpenAI",
      "date": "2026-09-01",
      "url": "https://openai.com/index/gilbert-tobin",
      "sourceType": "official",
      "whatItSupports": "Gilbert + Tobin describes scaled ChatGPT adoption supported by approved-task guidance, data controls, role-based access, and human accountability."
    },
    {
      "sourceId": "src-2026-09-06-006",
      "organization": "GitHub Copilot",
      "title": "Upcoming changes to GitHub Copilot policies and billing",
      "date": "2026-08-28",
      "url": "https://github.blog/changelog/2026-08-28-upcoming-changes-to-github-copilot-policies-and-billing/",
      "sourceType": "official",
      "whatItSupports": "GitHub announced upcoming Copilot billing and policy changes, including per-seat payment requirements, usage tracking, and continued availability of spend controls."
    },
    {
      "sourceId": "src-2026-09-06-007",
      "organization": "arXiv / MasterControl Seventeen Every Time",
      "title": "MasterControl Seventeen Every Time",
      "date": "2026-09-02",
      "url": "https://arxiv.org/abs/2609.03209",
      "sourceType": "research",
      "whatItSupports": "The MasterControl benchmark reports that policy-executed analysis met its full answer-and-evidence contract where a runtime-planning configuration did not."
    },
    {
      "sourceId": "src-2026-09-06-008",
      "organization": "OpenAI / Legora",
      "title": "Legora reviewed 41 documents in minutes with GPT-6 Astra",
      "date": "2026-09-03",
      "url": "https://openai.com/index/legora-financial-statement-review-with-astra",
      "sourceType": "official",
      "whatItSupports": "Legora reports using GPT-6 Astra for document review and benchmarking the workflow against financial-statement tasks while retaining professional judgment."
    },
    {
      "sourceId": "src-2026-09-06-009",
      "organization": "arXiv / SLIDEFORGE",
      "title": "SLIDEFORGE: An LLM Agent for Controllable Editing of Slides as Structured Artifacts",
      "date": "2026-09-02",
      "url": "https://arxiv.org/abs/2609.03109",
      "sourceType": "research",
      "whatItSupports": "SLIDEFORGE presents an agent architecture that preserves native slide structure and evaluates component recovery, visual quality, and editability rather than judging screenshots alone."
    },
    {
      "sourceId": "src-2026-09-06-010",
      "organization": "AWS Amazon Quick",
      "title": "Amazon Quick now lets you build custom apps with natural language",
      "date": "2026-09-01",
      "url": "https://aws.amazon.com/about-aws/whats-new/2026/09/amazon-quick-custom-apps-natural-language/",
      "sourceType": "official",
      "whatItSupports": "AWS announced natural-language construction of custom apps in Amazon Quick for Plus, Professional, and Enterprise customers."
    },
    {
      "sourceId": "src-2026-09-06-011",
      "organization": "Federal Reserve Bank of Dallas",
      "title": "Job postings show early signs of AI automation impact",
      "date": "2026-09-01",
      "url": "https://www.dallasfed.org/research/economics/2026/0901",
      "sourceType": "research",
      "whatItSupports": "Dallas Fed analysis reports early signs of AI automation effects in job postings while examining productivity and labor-market disruption from GenAI adoption."
    },
    {
      "sourceId": "src-2026-09-06-012",
      "organization": "AWS / Anthropic",
      "title": "Claude Fable 5.1, Anthropic's new frontier model is now available on AWS",
      "date": "2026-09-01",
      "url": "https://aws.amazon.com/about-aws/whats-new/2026/09/claude-fable-5-1-aws/",
      "sourceType": "official",
      "whatItSupports": "AWS announced availability of Claude Fable 5.1 for coding, scientific research, and enterprise workflows through AWS infrastructure."
    },
    {
      "sourceId": "src-2026-09-06-013",
      "organization": "Microsoft",
      "title": "The yield imperative: Turning AI infrastructure into useful intelligence",
      "date": "2026-09-01",
      "url": "https://blogs.microsoft.com/blog/2026/09/01/the-yield-imperative-turning-ai-infrastructure-into-useful-intelligence/",
      "sourceType": "official",
      "whatItSupports": "Microsoft argues that AI infrastructure investment must translate into useful intelligence through better utilization, efficiency, and operational execution."
    }
  ],
  "rejectedCandidates": [
    {
      "trendId": "reject-2026-09-06-001",
      "title": "Undated or weakly sourced model and startup leads",
      "reason": "Rejected because canonical publisher metadata, in-range publication date, or claim-specific support could not be verified."
    },
    {
      "trendId": "reject-2026-09-06-002",
      "title": "EU AI Act implementation summaries",
      "reason": "Rejected from this snapshot because the strongest surfaced sources described obligations beginning 2026-08-02, outside the active range."
    }
  ],
  "reviewNotes": [
    "Approved update range: 2026-08-28 through 2026-09-06 inclusive, based on the latest earlier archive snapshot dated 2026-08-27.",
    "Twelve qualified candidates were selected from official publisher, institutional research, and arXiv sources after URL, title, date, claim-support, enterprise-relevance, and deduplication checks.",
    "Required research coverage was 13/13 lanes; qualified coverage was AI coding agents (1), Token economics (1), Models (2), Governance and safety (2), Industry adoption (1), Business model innovation (1), AI infrastructure (1), Enterprise platforms (1), Human-AI collaboration (1), Agentic AI (1), and Data and RAG (1).",
    "No qualified in-range item was retained for Open source, Knowledge graphs, or AGI & frontier research; no stale or weak items were added to pad those lanes.",
    "This approved dataset update does not deploy to production, launch What's Due, or create a compliance obligation."
  ],
  "sourceRegistry": {
    "categories": [
      {
        "name": "Official AI provider blogs",
        "sources": [
          "OpenAI",
          "Anthropic",
          "Google DeepMind",
          "Microsoft",
          "AWS",
          "Meta AI",
          "Mistral",
          "Cohere",
          "NVIDIA",
          "Databricks",
          "Snowflake",
          "Hugging Face"
        ]
      },
      {
        "name": "Model and benchmark sources",
        "sources": [
          "Artificial Analysis",
          "LMSYS/Chatbot Arena",
          "Stanford AI Index",
          "Papers with Code",
          "arXiv"
        ]
      },
      {
        "name": "Enterprise and architecture sources",
        "sources": [
          "Thoughtworks Technology Radar",
          "InfoQ",
          "CNCF",
          "Microsoft Architecture Center",
          "AWS Architecture Blog",
          "Google Cloud Blog",
          "Databricks Blog"
        ]
      },
      {
        "name": "Developer tooling sources",
        "sources": [
          "GitHub",
          "GitLab",
          "JetBrains",
          "Cursor",
          "Replit",
          "Sourcegraph",
          "Docker",
          "Kubernetes"
        ]
      },
      {
        "name": "Analyst and business sources",
        "sources": [
          "McKinsey",
          "BCG",
          "Bain",
          "Deloitte",
          "PwC",
          "Accenture",
          "Gartner",
          "Forrester",
          "IDC",
          "MIT Sloan",
          "Harvard Business Review"
        ]
      },
      {
        "name": "Trusted AI news and analysis",
        "sources": [
          "The Batch",
          "Ben's Bites",
          "Import AI",
          "The Decoder",
          "VentureBeat AI",
          "TechCrunch AI",
          "The Information"
        ]
      },
      {
        "name": "Academic and research sources",
        "sources": [
          "arXiv",
          "ACM",
          "IEEE",
          "Nature",
          "Science",
          "Research lab blogs"
        ]
      }
    ],
    "fields": [
      "Source name",
      "URL or feed",
      "Source type",
      "Domain coverage",
      "Trust level",
      "Update frequency",
      "Fetch allowed or manual only",
      "Notes on what to extract",
      "Whether paywalled",
      "Whether official, analyst, research, news, or opinion"
    ]
  }
}