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      "source_path": "TeamStation-AI/public/markdown/research/articles/teamstation-ai-publishes-distributed-engineering-os-model-for-agentic-ai-teams.md",
      "evidence_class": "MCP supported public corpus source"
    },
    {
      "id": "TSCF2026",
      "title": "Cognitive Fidelity and the Turing Trap",
      "author": "TeamStation AI R&D Lab Staff",
      "year": "2026",
      "url": "https://teamstation.dev/research/articles/cognitive-fidelity-and-the-turing-trap",
      "role": "Public TeamStation science source for cognitive fidelity, validation quality, and the risk of confusing imitation with real engineering judgment.",
      "source_path": "TeamStation-AI/public/markdown/research/articles/cognitive-fidelity-and-the-turing-trap.md",
      "evidence_class": "Local public corpus source"
    },
    {
      "id": "TSTOPOS2026",
      "title": "Agentic Team Topologies for CTOs and CIOs",
      "author": "TeamStation AI R&D Lab Staff",
      "year": "2026",
      "url": "https://teamstation.dev/research/articles/team-topologies-in-the-agentic-workflow-era-beyond-2026",
      "role": "Public TeamStation research source for team topology changes in agentic SDLC workflows.",
      "source_path": "TeamStation-AI/public/markdown/research/articles/team-topologies-in-the-agentic-workflow-era-beyond-2026.md",
      "evidence_class": "Local public corpus source"
    },
    {
      "id": "TSTEAMTOPO2026",
      "title": "Software Engineering Team Topologies for 2026",
      "author": "TeamStation AI R&D Lab Staff",
      "year": "2026",
      "url": "https://teamstation.dev/research/articles/software-engineering-team-topologies-for-2026",
      "role": "Public TeamStation research source for engineering team topology patterns before and during agentic workflow adoption.",
      "source_path": "TeamStation-AI/public/markdown/research/articles/software-engineering-team-topologies-for-2026.md",
      "evidence_class": "Local public corpus source"
    },
    {
      "id": "TSHIDDENMATH2026",
      "title": "Hidden Math of Distributed Engineering Failure",
      "author": "TeamStation AI R&D Lab Staff",
      "year": "2026",
      "url": "https://teamstation.dev/research/articles/the-hidden-math-behind-distributed-engineering-failure",
      "role": "Public TeamStation research source for coordination cost, distributed execution failure, and hidden capacity loss.",
      "source_path": "TeamStation-AI/public/markdown/research/articles/the-hidden-math-behind-distributed-engineering-failure.md",
      "evidence_class": "Local public corpus source"
    },
    {
      "id": "TSAGENTICLATAM2026",
      "title": "2027 Agentic Team Topologies in LATAM",
      "author": "TeamStation AI R&D Lab Staff",
      "year": "2026",
      "url": "https://teamstation.dev/research/articles/the-2027-blueprint-for-agentic-engineering-team-topologies-in-latin-america",
      "role": "Public TeamStation research source for agentic engineering squad design, LATAM topology, and future capacity planning.",
      "source_path": "TeamStation-AI/public/markdown/research/articles/the-2027-blueprint-for-agentic-engineering-team-topologies-in-latin-america.md",
      "evidence_class": "Local public corpus source"
    },
    {
      "id": "TSDOCTRINE2026",
      "title": "Engineering Capacity Operating System Research",
      "author": "TeamStation Engineering",
      "year": "2026",
      "url": "https://engineering.teamstation.dev/research/engineering-operating-system",
      "role": "Canonical Engineering Doctrine research node for the Engineering Capacity Operating System.",
      "source_path": "Engineering/dist/api/research/engineering-operating-system.json",
      "evidence_class": "MCP supported canonical doctrine node"
    },
    {
      "id": "TSDOCTRINEMD2026",
      "title": "Engineering Capacity Operating System Markdown Source",
      "author": "TeamStation Engineering",
      "year": "2026",
      "url": "https://engineering.teamstation.dev/api/research/engineering-operating-system.md",
      "role": "Machine readable Markdown route for the canonical Engineering Capacity OS source.",
      "source_path": "Engineering/dist/api/research/engineering-operating-system.md",
      "evidence_class": "MCP supported canonical doctrine node"
    },
    {
      "id": "TSCOGFIDELITYDOCTRINE2026",
      "title": "Cognitive Fidelity Doctrine",
      "author": "TeamStation Engineering",
      "year": "2026",
      "url": "https://engineering.teamstation.dev/quality/cognitive-fidelity/",
      "role": "Engineering Doctrine proof layer for cognitive fidelity, quality economics, and validation posture.",
      "source_path": "Engineering/dist/markdown/quality/cognitive-fidelity.md",
      "evidence_class": "Local doctrine source"
    },
    {
      "id": "TSAGENTICWORKFLOWDOCTRINE2026",
      "title": "Agentic Engineering Workflows in Distributed Team Topologies",
      "author": "TeamStation Engineering",
      "year": "2026",
      "url": "https://engineering.teamstation.dev/teams/agentic-development-workflows/",
      "role": "Engineering Doctrine source for agentic workflow, sequential probability, incentives, replacement kinetics, and team topology math.",
      "source_path": "Engineering/dist/markdown/teams/agentic-development-workflows.md",
      "evidence_class": "Local doctrine source"
    }
  ],
  "glossary": [
    {
      "term": "Agentic loop",
      "definition": "A governed cycle where a human, AI agent, tool, source system, and validation gate turn intent into checked output."
    },
    {
      "term": "Axiom Cortex",
      "definition": "TeamStation's cognitive evaluation layer for mental shape, engineering judgment, role fit, and agentic workflow alignment."
    },
    {
      "term": "Capacity Intelligence",
      "definition": "The subsystem that estimates usable capacity after cognitive load, review limits, interruptions, topology, and decision latency are accounted for."
    },
    {
      "term": "Decision grade telemetry",
      "definition": "Operational signal with known source, definition, freshness, bias, and direct connection to a capacity or governance decision."
    },
    {
      "term": "Distributed Engineering Operating System",
      "definition": "The TeamStation operating model connecting talent signal, team topology, governance, telemetry, compliance, payroll, devices, MDM, and delivery controls into one source of truth."
    },
    {
      "term": "Engineering Capacity OS",
      "definition": "The research model proposed in the paper for treating capacity as knowledge, execution, governance, topology, telemetry, and automation, not as headcount alone."
    },
    {
      "term": "Evidence classification",
      "definition": "The rule that every claim is tagged as observed, modeled, directional, unknown, opinion, hypothesis, future work, internal research, or external validation before recommendation."
    },
    {
      "term": "Governed Agentic SDLC",
      "definition": "A software delivery system where AI assistants and agents operate inside explicit permissions, evidence capture, approval gates, rollback paths, and human ownership."
    },
    {
      "term": "Knowledge Architecture",
      "definition": "The memory layer of the engineering system: architecture decisions, ownership, runbooks, domain rules, glossary, onboarding path, and incident learning."
    },
    {
      "term": "MCP",
      "definition": "Model Context Protocol, used here as the governed retrieval and tool layer connecting agents to TeamStation research, engineering doctrine, APIs, diagrams, and knowledge sources."
    },
    {
      "term": "Nebula Talent Graph",
      "definition": "TeamStation's talent intelligence layer for representing engineering capability, signal, role alignment, and topology fit."
    },
    {
      "term": "Topology choice",
      "definition": "A decision about where work should live: internal team, platform team, enabling team, nearshore pod, vendor, agent, automation lane, or no expansion."
    }
  ],
  "package_status": "Operationally validated working paper draft. Not peer reviewed. Not independently replicated. Do not publish without human review, citation review, and final package validation.",
  "evidence_policy": {
    "teamstation_sources": "Internal research context and traceability, not independent external validation.",
    "external_sources": "Public references only where listed in BibTeX, RIS, CSL JSON, schema metadata, and the manuscript reference list.",
    "fictional_walkthrough": "Demonstration scenario only. Not client data.",
    "excluded_data": "No client telemetry, private customer records, payroll records, health records, legal records, raw credentials, or confidential candidate data.",
    "mcp_claim_support_checked_at": "2026-07-08",
    "mcp_claim_support_status": "central thesis, TeamStation operating model, science corpus coverage, package contents, and MCP retrieval role returned supported in TeamStation Brain lexical source-support checks"
  },
  "validation_boundary": {
    "operational_validation": "Completed inside TeamStation production engineering operating system using operational observations, delivery telemetry, topology decisions, governance workflows, and capacity assessments.",
    "internal_empirical_validation": "Supported by TeamStation controlled evidence and production operating model records.",
    "external_validation": "Not claimed. Independent external validation remains future work.",
    "academic_replication": "Not claimed. Independent academic replication remains future work.",
    "peer_review": "Not claimed."
  },
  "date_published": "2026-07-09",
  "date_modified": "2026-07-10",
  "canonical_url": "https://engineering.teamstation.dev/research/engineering-operating-system/paper/",
  "pdf_url": "https://engineering.teamstation.dev/api/research/engineering-capacity-paper/paper.pdf"
}
