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\title{Engineering Capacity as an Operating System}
\author{Lonnie McRorey\\Founder and CEO, TeamStation AI\\Boston, Massachusetts\\ORCID: \href{https://orcid.org/0009-0001-5351-190X}{0009-0001-5351-190X}}
\date{2026-07-09}
\begin{document}
\maketitle
\section*{Engineering Capacity as an Operating System}

\section{A systems model for AI governed software delivery, evidence classified topology decisions, and decision grade engineering capacity}

Author: Lonnie McRorey, Founder and CEO, TeamStation AI, Boston, Massachusetts

ORCID: https://orcid.org/0009-0001-5351-190X

Publisher: TeamStation AI

Publication type: Working paper draft

Date: 2026-07-09

Version: 0.5 operational validation boundary draft

Status: Operationally validated working paper for human review. Not peer reviewed. Not yet a final arXiv submission.

\section{Index}

Manuscript route: Abstract; TeamStation Company and Source Boundary; TeamStation Science Corpus; Evidence Policy and Claim Ledger; Operational Validation; Introduction; Related Work and the Measurement Gap; Engineering Capacity Theory; The Engineering Capacity OS; Capacity Diagnostic Protocol; Evidence Classification and Why It Matters; Readiness Model; Reference Architecture; AI Integration in the Agentic Loop Era; Fictional Enterprise Walkthrough; Discussion; Conclusion; Declarations; References; Glossary; Appendix A: Diagnostic Output Schema; Appendix B: Minimum Evidence Required Before Recommendation; Appendix C: Evidence States by Paragraph Type


\section{TeamStation Company and Source Boundary}

TeamStation AI is treated in the paper as the company research context and publisher, not as the conclusion the reader is being asked to accept. TeamStation AI describes its operating model as a distributed engineering operating system: talent graph signal, Axiom Cortex cognitive evaluation, team topology design, delivery telemetry, payroll, employer of record operations, device security, MDM, compliance, and governance connected into one source of truth for engineering capacity decisions [TSDEOS2026; TSAXIOM2026; TSDOCTRINE2026].


The paper keeps the research claim separate from the company claim. The research claim is that engineering capacity should be modeled as an operating system before leaders recommend hiring, outsourcing, insourcing, vendor replacement, or AI automation. The company context is that TeamStation AI has been building that operating system through its public research corpus, engineering doctrine site, MCP backed knowledge base, diagrams, APIs, glossary, and TeamStation science papers.


Publisher: TeamStation AI. Author: Lonnie McRorey, Founder and CEO, Boston, Massachusetts. ORCID: https://orcid.org/0009-0001-5351-190X. Draft status: working paper for source backed review. Publication action: no direct publication should happen until the draft receives human review, citation review, and final package validation.


\section{TeamStation Science Corpus}

The TeamStation Research Publishing OS links the manuscript to the source corpus below. The sources are used as internal science context, public doctrine context, operational validation context, and research traceability. The paper does not treat a TeamStation source as an external independent validation source. External replication remains a separate requirement before journal submission.


| Source | Role | Source locator |

| --- | --- | --- |

| Distributed Engineering Operating Systems | Public TeamStation research article defining the control plane model for distributed engineering operating systems | TSDEOS2026 in science corpus and references metadata |

| Axiom Cortex for LATAM Agentic Engineering | Public research source for mental shape, cognitive evaluation, LATAM engineering alignment, and agentic workflow fit | TSAXIOM2026 in science corpus and references metadata |

| TeamStation Distributed Engineering OS | Public company research note connecting TeamStation to the Distributed Engineering OS model for agentic AI teams | TSNEWSDEOS2026 in science corpus and references metadata |

| Cognitive Fidelity and the Turing Trap | Public TeamStation science source for cognitive fidelity, validation quality, and the risk of confusing imitation with real engineering judgment | TSCF2026 in science corpus and references metadata |

| Agentic Team Topologies for CTOs and CIOs | Public TeamStation research source for team topology changes in agentic SDLC workflows | TSTOPOS2026 in science corpus and references metadata |

| Software Engineering Team Topologies for 2026 | Public TeamStation research source for engineering team topology patterns before and during agentic workflow adoption | TSTEAMTOPO2026 in science corpus and references metadata |

| Hidden Math of Distributed Engineering Failure | Public TeamStation research source for coordination cost, distributed execution failure, and hidden capacity loss | TSHIDDENMATH2026 in science corpus and references metadata |

| 2027 Agentic Team Topologies in LATAM | Public TeamStation research source for agentic engineering squad design, LATAM topology, and future capacity planning | TSAGENTICLATAM2026 in science corpus and references metadata |

| Engineering Capacity Operating System Research | Canonical Engineering Doctrine research node for the Engineering Capacity Operating System | TSDOCTRINE2026 in science corpus and references metadata |

| Engineering Capacity Operating System Markdown Source | Machine readable Markdown route for the canonical Engineering Capacity OS source | TSDOCTRINEMD2026 in science corpus and references metadata |

| Cognitive Fidelity Doctrine | Engineering Doctrine proof layer for cognitive fidelity, quality economics, and validation posture | TSCOGFIDELITYDOCTRINE2026 in science corpus and references metadata |

| Agentic Engineering Workflows in Distributed Team Topologies | Engineering Doctrine source for agentic workflow, sequential probability, incentives, replacement kinetics, and team topology math | TSAGENTICWORKFLOWDOCTRINE2026 in science corpus and references metadata |


\section{Evidence Policy and Claim Ledger}

The manuscript uses a hard source boundary. External literature is used only where a public source is listed in the references. TeamStation claims are used only when they are present in the TeamStation MCP indexed corpus, the Engineering Doctrine site, production operating model evidence, or the generated paper package. Internal TeamStation sources are evidence for TeamStation's own model, operating doctrine, and production use. They are not independent external validation of the model.


The validation boundary is explicit. Operational validation means TeamStation has used and refined the framework inside its own production engineering operating system. Internal empirical validation means TeamStation evidence, delivery telemetry, topology decisions, governance workflows, and capacity assessments support the model inside TeamStation controlled environments. External validation means another organization or independent reviewer reproduces the result. Academic replication means independent researchers reach similar conclusions through their own methods. The paper claims the first two. It does not claim the latter two.


No client telemetry, private customer records, payroll records, health records, legal records, raw credentials, or confidential candidate data are included in the paper package. The fictional walkthrough is invented as a demonstration case, not as a disguised client study. Any future empirical paper must replace the fictional walkthrough with reviewed data, consent, redaction, and a reproducible evidence ledger.


| Claim class | Current source standard | Publication status |

| --- | --- | --- |

| External measurement frameworks | Public references for DORA, SPACE, Team Topologies, and peer reviewed or preprint AI productivity studies | Acceptable for working paper citation with final citation review |

| TeamStation operating model | MCP indexed TeamStation research, Engineering Doctrine pages, public TeamStation research articles, and package metadata | Internal research context, not independent validation |

| Operational validation | TeamStation production engineering operating system observations, delivery telemetry, topology decisions, governance workflows, and capacity assessments | Claimed as internal operational validation, with customer specific data excluded |

| Engineering Capacity OS thesis | MCP supported TeamStation doctrine plus systems argument in the manuscript | Working paper contribution requiring external critique |

| Fictional enterprise walkthrough | Author constructed scenario used to demonstrate the protocol | Demonstration only |

| Product, commercial, superiority, legal, security, or compliance claims | Not asserted as proof claims in the working paper | Requires separate review before publication |


MCP claim support was checked for the central thesis, TeamStation operating model description, science corpus coverage, package contents, and MCP retrieval role on 2026-07-08. The lexical support check returned supported results for those five claims against the local TeamStation Brain index. That check is a source-support gate, not a substitute for legal, scientific, peer, or journal review.


\section{Operational Validation}

Engineering Capacity OS was not developed solely from theory. The framework was iteratively developed and refined through TeamStation AI's production engineering operating system using operational observations, delivery telemetry, engineering topology decisions, governance workflows, and capacity assessments across real customer engagements. The paper presents the generalized model rather than customer specific data because of confidentiality obligations. The underlying framework has been operationally validated within the TeamStation platform. External replication remains future work.


That distinction matters. The paper does not claim peer review. It does not claim independent replication. It claims operational validation inside TeamStation's production environment and internal empirical support from TeamStation controlled evidence. Those claims are narrower than academic replication, but they are stronger than pure theory. They tell the reader the model has been used inside the operating system it describes, while still keeping the external evidence boundary honest.


\section{Abstract}

Engineering organizations still talk about capacity as if capacity were mainly a staffing problem. The usual executive question is simple: how many engineers do we need, where should we hire them, and how quickly can they start? That question is useful, but incomplete. Modern software delivery is no longer produced by headcount alone. Delivery capacity is produced by an operating system made of knowledge, execution rules, topology, telemetry, governance, automation, and human judgment. When that system is weak, new headcount often increases motion while reducing flow. When that system is strong, smaller teams can produce more reliable outcomes because work has fewer hidden queues, fewer ambiguous ownership boundaries, better feedback loops, and clearer decision rights.


The manuscript proposes Engineering Capacity OS, a systems model for evaluating engineering capacity before recommending hiring, outsourcing, insourcing, vendor replacement, platform investment, or AI automation. The central claim is that organizational change should not be recommended until the quality of the evidence has been classified. The claim matters more in the agentic loop era, where AI systems can generate recommendations faster than organizations can validate the operating context. The paper integrates established measurement and organization lenses, including DORA software delivery metrics, the SPACE framework for developer productivity, Team Topologies, DevOps capability research, and empirical work on AI coding assistants. It argues that each lens measures a valuable slice of the system, but none by itself models the complete operating system that determines usable engineering capacity.


The contribution is a formal operating model: Capacity = Knowledge + Execution + Governance + Topology + Telemetry + Automation. The paper defines eight subsystems, an evidence taxonomy, a readiness model, a reference architecture, and a diagnostic protocol. A fictional enterprise walkthrough shows why a rigorous capacity system may recommend not hiring, even when leaders feel under-resourced. The purpose is not to sell one vendor model or one location strategy. The purpose is to make engineering capacity decisions evidence classified, topology aware, and governable before the organization adds more people or more agents into a broken delivery machine.


Keywords: engineering capacity, software engineering management, DORA, SPACE, Team Topologies, AI governance, engineering telemetry, capacity planning, agentic SDLC, distributed engineering operating system


\section{1. Introduction}

Here is the pattern: engineering leaders usually discover capacity problems through symptoms. Roadmaps slip. Pull requests age. Senior engineers get stuck in review queues. Incident work eats planned delivery. Vendors report activity but the business sees less production movement. AI coding tools raise local output, but the review burden moves downstream. The easy story is that the team needs more people. The harder story is that the operating system around the team is not absorbing the people it already has.


The distinction matters. A staffing model asks whether the organization has enough engineers. A systems model asks whether the organization can convert engineering effort into trusted software outcomes. Those are different questions. The first question counts people. The second question studies the machine those people are working inside. That machine includes architecture memory, service ownership, CI/CD gates, review capacity, documentation freshness, telemetry integrity, work intake, product decision latency, access controls, and the increasingly important question of what AI agents are allowed to do.


The industry already has strong partial lenses. DORA gives organizations a practical way to reason about delivery throughput and stability through deployment frequency, lead time for changes, change failure rate, and recovery behavior [DORA2026]. SPACE pushes back against one dimensional productivity measurement by treating developer productivity as satisfaction and well-being, performance, activity, communication and collaboration, and efficiency and flow [SPACE2021]. Team Topologies gives a vocabulary for structuring teams around fast flow and cognitive load, including stream-aligned, platform, enabling, and complicated subsystem teams [TEAMTOPOLOGIES2026]. Empirical studies of AI coding assistants show that AI can improve local task completion speed in some contexts, while still requiring careful validation of task type, codebase maturity, and downstream review cost [PENG2023].


Those lenses are necessary. They are not sufficient. DORA can tell a leader that the delivery system is slow or unstable, but it does not tell the leader whether the next move should be to hire, reorganize, reduce work in progress, improve architecture memory, replace a vendor, add platform capacity, or constrain AI-generated changes. SPACE can reveal a richer picture of developer productivity, but it does not by itself define an operating protocol for topology choice. Team Topologies can improve organizational shape, but it still needs evidence about telemetry, governance, and execution determinism. AI coding assistant studies can show local productivity gains, but local speed does not equal system capacity when review, architecture, security, and maintainability are the real bottlenecks.


The paper proposes a unifying model. Engineering capacity should be treated as an operating system. The model is not a metaphor for branding. It is a systems claim. An operating system schedules work, controls access, manages memory, exposes telemetry, handles failures, and keeps processes from corrupting each other. Engineering organizations need the same discipline. They need a way to know where work should live, which humans should own which decisions, which agents can safely operate, which evidence is decision grade, and which recommendations are still speculative.


The paper is intentionally neutral about the final intervention. Sometimes the right answer is to hire. Sometimes the right answer is to stop hiring until the organization has fixed review capacity, service ownership, testing, or documentation. Sometimes a nearshore pod is the right topology. Sometimes the work should remain internal. Sometimes a vendor should own a bounded workstream. Sometimes an AI agent should handle a low variance validation task. Sometimes the company should invest in a platform team before adding feature teams. The operating principle is simple: hiring, outsourcing, insourcing, vendor replacement, and automation are topology choices, not default answers.


The novel contribution is the evidence gate. AI should never recommend organizational change before classifying the quality of its evidence. In an agentic SDLC, recommendations will become cheap. Confidence will become expensive. The organization that wins will not be the organization that generates the most recommendations. It will be the organization that knows which recommendations are supported, which are modeled, which are directional, and which are unknown.


\section{2. Related Work and the Measurement Gap}

DORA research has shaped how many engineering organizations measure software delivery performance. Its software delivery metrics focus attention on the flow of changes to production and the stability of those changes [DORA2026]. The value is obvious: leaders get a small set of signals that are closer to delivery reality than raw activity counts. Deployment frequency, lead time, change failure, and recovery time are not perfect, but they force the conversation toward delivery outcomes rather than calendar theater.


The limitation is also obvious once a CTO tries to make an operating decision. DORA metrics can show that the system is slow, but the metric alone does not explain whether the constraint is architecture, review capacity, unclear product intent, test instability, overloaded senior engineers, weak vendor governance, poor onboarding, or missing platform capability. A low deployment frequency number might be a symptom of fragile production systems. It might also be the result of slow security approval, manual release ceremonies, poor test environments, or a deliberate risk posture in a regulated domain. The metric is signal, not diagnosis.


SPACE corrects another common mistake: reducing developer productivity to activity. The SPACE framework defines productivity through multiple dimensions: satisfaction and well-being, performance, activity, communication and collaboration, and efficiency and flow [SPACE2021]. That is a major improvement over simplistic measures like commits, tickets closed, or lines of code. It recognizes that developer productivity is socio-technical. A team can be busy and still not be producing value. A developer can be active and still be trapped in a broken coordination system.


The gap is that SPACE is a measurement frame, not a full operating model. It helps leaders avoid bad measurement, but it does not decide which topology should absorb a new workload, which work should be automated, which evidence is trustworthy, or which governance controls must exist before an AI agent can change code. SPACE improves the conversation. Engineering Capacity OS tries to connect that conversation to a decision protocol.


Team Topologies adds the missing organizational vocabulary. Stream-aligned teams own value streams. Platform teams reduce cognitive load by providing reusable services. Enabling teams raise capability temporarily. Complicated subsystem teams protect areas that need deep specialist knowledge [TEAMTOPOLOGIES2026]. The core insight is that team shape affects flow. That idea is central to the paper. Capacity is not only how many people are available. Capacity is how work is allocated across ownership boundaries with acceptable cognitive load.


The limitation is that topology alone can still be applied as an organizational pattern without enough evidence. A company can rename teams without changing the operating system. It can create a platform team without treating the platform as a product. It can create stream-aligned teams while keeping architecture decision rights centralized. It can add an enabling team without measuring whether capability actually transfers. The topology vocabulary is powerful, but it needs telemetry, governance, and evidence classification to become a reliable capacity decision system.


AI coding assistant research introduces a new pressure. Peng et al. reported that developers with access to GitHub Copilot completed a specific programming task substantially faster than a control group [PENG2023]. Later field experiments and enterprise studies suggest productivity effects can vary by task, worker experience, organizational context, and measurement method [CUI2026]. The implication is not that AI always increases engineering capacity. The implication is that AI changes the location of the bottleneck. Code generation may speed up while code review, architecture validation, security review, and maintainability assessment become the constraint.


That is the agentic loop problem. If an organization treats AI as extra headcount, it may amplify rework. If it treats AI as a governed execution actor inside an engineering operating system, it can identify where agents reduce variance and where humans must retain authority. The difference is governance. The difference is evidence. The difference is whether the organization measures the whole system or celebrates local speed.


\section{3. Engineering Capacity Theory}

Engineering capacity is not headcount. Headcount is an input. Capacity is the system's ability to convert intent into reliable software outcomes under constraints. Those constraints include knowledge availability, architecture complexity, review capacity, governance, security, telemetry quality, work in progress, tool maturity, and the fit between human or agent capability and the work being assigned.


The proposed capacity equation is:


Capacity = Knowledge + Execution + Governance + Topology + Telemetry + Automation


The equation is not intended as a literal linear formula. It is a forcing function. It prevents leaders from treating capacity as a single variable. If knowledge is missing, new people spend their time rediscovering context. If execution rules are weak, new people create inconsistent work. If governance is absent, new people and agents increase risk. If topology is wrong, new people increase coordination cost. If telemetry is not trustworthy, leaders make decisions from noise. If automation is unmanaged, agents create throughput that the rest of the system cannot validate.


Knowledge means architecture memory, product intent, codebase context, service ownership, runbooks, incident history, decision records, and domain constraints. It is the memory layer of the engineering operating system. When knowledge lives only in senior engineers' heads, the organization has capacity on paper and fragility in practice. Every new person or agent has to ask the same questions, repeat the same mistakes, and wait for the same overloaded human nodes.


Execution means the harness that turns work into production change. It includes CI/CD, test strategy, deployment patterns, review policy, environment control, release rituals, and rollback readiness. Execution determinism matters because distributed teams and AI agents need predictable rules. If the same work behaves differently depending on which team, vendor, environment, or reviewer handles it, the system cannot scale safely.


Governance means authority, access, audit, approval, policy, exception handling, and rollback. Governance is not bureaucracy by default. Bad governance blocks flow. Good governance defines where the rails are. In an AI governed SDLC, governance must include agent permissions, tool boundaries, human approval gates, evidence capture, and stop conditions. The question is not whether agents can act. The question is which actions agents can take without creating uncontrolled risk.


Topology means the distribution of ownership and execution across internal teams, external partners, nearshore pods, offshore teams, platform teams, specialists, and AI agents. Topology is where the headcount conversation becomes architecture. A person can be a good engineer and still be placed into the wrong topology. A vendor can be competent and still fail if the work requires internal architecture authority. An AI agent can be useful and still be dangerous if assigned to a high variance task without validation.


Telemetry means signals trusted enough to guide decisions. Not every dashboard is telemetry. Some dashboards are decorations. Decision grade telemetry must have clear source, clear definition, known freshness, known bias, and a connection to operating decisions. Pull request age, review queue depth, deployment failure, incident interruption load, cycle time distribution, blocked time, documentation freshness, and test reliability can become decision grade if collected consistently and interpreted carefully.


Automation means machine participation in the SDLC, including AI coding assistants, test generation, static analysis, deployment automation, documentation generation, knowledge retrieval, and agentic workflows. Automation is not automatically capacity. Automation becomes capacity only when it reduces bottlenecks without increasing rework, hidden risk, or downstream validation cost.


The central theory is that usable capacity emerges from the interaction of these layers. A team with strong people but weak knowledge architecture will feel slow. A team with strong automation but weak governance will feel fast until risk surfaces. A team with excellent telemetry but no decision rights will diagnose problems it cannot change. A team with strong topology but weak execution will still struggle to ship. Capacity is a system property.


\section{4. The Engineering Capacity OS}

Engineering Capacity OS is a reference model for structuring, governing, measuring, and improving engineering capacity. It contains eight operating subsystems. Each subsystem can be measured, improved, and used as evidence in topology decisions.


The first subsystem is Capacity Intelligence. Capacity Intelligence models usable capacity after cognitive load, role fit, review constraints, interruptions, skill distribution, decision latency, and organizational bottlenecks are considered. It answers a question leaders rarely ask cleanly: how much usable engineering capacity exists after the system constraints are accounted for? A team with ten engineers, three overloaded reviewers, stale documentation, and constant incident interruption does not have ten engineers of usable capacity.


The second subsystem is Knowledge Architecture. Knowledge Architecture captures the explicit memory of the engineering system: service ownership, architecture decisions, runbooks, domain rules, product intent, platform conventions, failure history, and onboarding paths. The purpose is to reduce tribal knowledge and make both distributed humans and AI agents safer. If knowledge is not explicit, every topology choice becomes fragile.


The third subsystem is the Execution Harness. The Execution Harness is the deterministic control plane for work moving from intent to production. It includes issue intake, development workflow, CI/CD, automated testing, review gates, deployment, rollback, and post-release learning. The harness matters because a distributed engineering system cannot be governed through meetings alone. The rules have to be encoded in the workflow.


The fourth subsystem is Decision Grade Telemetry. The subsystem separates signal from dashboard noise. It asks whether the organization can trust the data enough to make decisions about capacity, quality, cost, risk, and topology. Telemetry should not only report what happened. It should make the next decision clearer. If telemetry cannot explain whether the bottleneck is review, architecture, testing, product decision latency, or work intake, it is not decision grade.


The fifth subsystem is Governed Agentic SDLC. The subsystem defines how AI agents and coding assistants participate in engineering work. It identifies safe task classes, required approval gates, audit records, validation checks, and rollback constraints. In the model, AI agents are not treated as magical extra engineers. They are bounded execution actors. They can increase capacity only when the system knows where they reduce variance and where they create new validation load.


The sixth subsystem is Adaptive Control Loops. Adaptive control loops allow the engineering system to learn from telemetry and adjust workflow rules, topology, documentation, and automation. The key word is governed. A self-learning engineering system that can change its own process without approval, rollback, or audit is a risk generator. A governed learning loop can convert recurring failures into enforceable rules.


The seventh subsystem is Governance, Security, Audit, and Rollback. The subsystem controls authority. It defines who can change the system, who can approve exceptions, what evidence must be captured, and how changes are reversed. Governance must cover both humans and agents. In the agentic loop era, the organization needs to know which actions were taken by a human, which actions were suggested by AI, which actions were executed by AI, and where human approval entered the chain.


The eighth subsystem is the Topology Engine. The Topology Engine treats capacity choices as architecture decisions. It compares internal hiring, distributed internal teams, staff augmentation, external partner teams, nearshore pods, offshore pods, managed vendor teams, platform investment, and agentic automation against the evidence. It does not ask, which option is fashionable? It asks, which topology fits the work, risk, knowledge demand, governance boundary, collaboration pattern, and telemetry profile?


\section{5. Capacity Diagnostic Protocol}

A capacity diagnostic begins with a simple rule: do not recommend a capacity intervention until the system has classified the constraint. The first question is not who should we hire? The first question is what kind of constraint are we observing?


The protocol has seven stages. Stage one is scope. The organization defines the business objective, the engineering system under review, the time horizon, the services or value streams involved, and the decision that needs to be made. A capacity diagnostic for a regulated payments platform is different from a diagnostic for an early stage AI feature team. Context is not decoration. Context changes the evidence standard.


Stage two is evidence collection. Evidence sources may include architecture documents, service ownership maps, incident history, DORA metrics, pull request telemetry, review queue age, test reliability, deployment logs, product decision latency, Jira or Linear work history, Slack or Teams coordination patterns where permitted, vendor reports, onboarding records, security access logs, and agent tool call records. Sensitive sources require permission boundaries and redaction. Public papers and internal doctrine are not enough for a client-specific diagnosis. They support the model, not the client-specific conclusion.


Stage three is evidence classification. Each claim is tagged as observed, modeled, directional, or unknown. Observed evidence comes from direct system records. Modeled evidence comes from a formal model applied to available inputs. Directional evidence suggests a pattern but is not strong enough for a high consequence decision. Unknown means the system lacks the evidence. Unknown is not failure. Unknown is honesty.


Stage four is constraint mapping. The diagnostic maps constraints into the six capacity layers: knowledge, execution, governance, topology, telemetry, and automation. The stage prevents category mistakes. A review bottleneck is not solved by hiring more junior engineers. A missing service ownership map is not solved by a cheaper vendor. Weak test reliability is not solved by pushing AI to generate more code. The map forces the system to name the constraint before recommending treatment.


Stage five is readiness scoring. The organization scores readiness across eight dimensions: knowledge architecture, execution determinism, governance readiness, topology fit, telemetry trust, automation leverage, AI governance, and decision readiness. The goal is not to produce a vanity score. The goal is to know whether the system can absorb more capacity without increasing risk.


Stage six is topology decisioning. The Topology Engine compares possible interventions. It may recommend internal hiring when ownership and knowledge intensity are high. It may recommend platform investment when multiple stream teams are blocked by repeated environment or tooling problems. It may recommend a nearshore pod when collaboration overlap matters and the work has clear ownership boundaries. It may recommend a vendor team only when outcomes, telemetry, and exit controls are explicit. It may recommend AI automation only for bounded, validated tasks. It may recommend not hiring.


Stage seven is learning. The diagnostic records what was recommended, what evidence supported the recommendation, what uncertainty remained, what decision was made, and what later happened. The purpose is to turn capacity decisions into a learning system. Without that loop, every reorganization becomes a fresh opinion contest.


\section{6. Evidence Classification and Why It Matters}

The most important principle in the model is evidence classification before recommendation. The principle is not academic hygiene. It is operational safety.


Observed evidence is direct evidence from the system. Examples include pull request queue age, deployment failure records, test flake rate, incident interruption hours, documented service ownership, access audit history, and measured cycle time distributions. Observed evidence is strongest when definitions are stable and collection methods are known. It is weaker when telemetry is incomplete, incentives distort reporting, or data is stale.


Modeled evidence is evidence produced by applying a model to source inputs. A coordination cost estimate, delivery risk score, capacity readiness score, or topology fit score can be modeled evidence. Modeled evidence is useful, but it must disclose its inputs and assumptions. A model should not be allowed to hide uncertainty behind a clean number.


Directional evidence is a weaker signal that points toward a likely pattern. A leader may report that senior engineers are overloaded. A vendor may report that requirements are unclear. A team may feel that AI is creating more review burden. Those signals matter, but they should not become high consequence recommendations without stronger support.


Unknown evidence is the most neglected category. Many organizations treat unknown as a gap to be filled with confidence. That is how bad recommendations happen. If the system does not know whether review capacity is saturated, it should not recommend more contributors. If the system does not know whether documentation is fresh, it should not recommend a distributed topology that depends on asynchronous work. If the system does not know whether AI-generated code increases rework, it should not scale agentic workflows.


The taxonomy changes how AI systems should behave. An AI agent should not say, hire five engineers. It should say: the evidence indicates a review bottleneck, documentation freshness is unknown, test reliability is weak, and topology fit for external contributors is low. The recommended next step is to reduce review queue age and stabilize test gates before adding contributors. That answer is less dramatic. It is also more useful.


\section{7. Readiness Model}

The readiness model has eight dimensions.


Knowledge architecture readiness asks whether the organization has enough explicit context for new humans and agents to operate safely. Strong evidence includes ADRs, service ownership, runbooks, incident reviews, domain glossaries, onboarding paths, and documentation freshness. Weak evidence includes tribal ownership, stale wiki pages, undocumented production rituals, and repeated questions in chat.


Execution determinism asks whether the SDLC produces reproducible outcomes. Strong evidence includes standard CI/CD templates, reliable tests, low manual override frequency, rollback records, and consistent review policy. Weak evidence includes hand-built releases, environment drift, brittle tests, and inconsistent approval rules.


Governance readiness asks whether authority and risk controls are defined. Strong evidence includes access policies, approval boundaries, audit logs, change control rules, exception processes, and rollback paths. Weak evidence includes shared accounts, unclear production access, undocumented exceptions, and vendor actions outside a controlled evidence trail.


Topology fit asks whether the current or proposed team shape matches the work. Strong evidence includes clear value streams, platform boundaries, specialist subsystem ownership, and explicit interaction modes. Weak evidence includes everyone touching everything, hidden dependencies, unclear accountability, and teams organized around reporting lines rather than flow.


Telemetry trust asks whether the signals are decision grade. Strong evidence includes stable definitions, known source systems, freshness metadata, bias notes, and traceability to decisions. Weak evidence includes manual status reports, inconsistent ticket hygiene, cherry-picked dashboards, and metrics disconnected from operating choices.


Automation leverage asks whether automation reduces constraints without moving cost downstream. Strong evidence includes reduced review burden, lower rework, faster safe validation, and bounded task classes. Weak evidence includes more generated code, more review load, low acceptance rates, missing attribution, and unclear maintainability.


AI governance asks whether agent actions are bounded, auditable, reversible, and approved where required. Strong evidence includes tool permission models, human approval gates, prompt and context provenance, code attribution policy, security scanning, and rollback. Weak evidence includes agents operating through broad credentials, untracked generated changes, and no distinction between suggestion and execution.


Decision readiness asks whether the organization has enough classified evidence to act. A decision can be ready even with unknowns if the unknowns do not affect the intervention. A decision is not ready when the unknowns sit directly under the proposed recommendation.


\section{8. Reference Architecture}

The reference architecture has four major layers: evidence providers, evidence engine, capacity model, and decision engine.


Evidence providers include engineering telemetry, CI/CD systems, GitHub or GitLab, Jira or Linear, incident management, architecture repositories, documentation systems, security logs, vendor reports, talent evaluation systems, and AI agent logs. MCP servers are a practical integration layer because they let AI systems retrieve tools, resources, and prompts through governed interfaces rather than brittle UI navigation.


The Evidence Engine normalizes incoming records, classifies sensitivity, attaches provenance, detects missing sources, and assigns evidence status. It does not draft recommendations first. It builds an evidence ledger. Every claim the system makes should trace back to a source, model, or explicit unknown.


The Capacity Model maps evidence into the six capacity layers. It asks whether the constraint is knowledge, execution, governance, topology, telemetry, automation, or a combination. It also checks whether the evidence is strong enough to support that classification.


The Decision Engine compares topology options. It can recommend hiring, internal reallocation, platform investment, vendor change, nearshore pod, offshore pod, managed partner, AI automation, process repair, or no expansion. The Decision Engine should always return the rationale, evidence class, confidence, risks, and next validation step.


The output is not a generic report. The output is a capacity decision packet: diagnosis, evidence classification, readiness scores, topology recommendation, rejected alternatives, uncertainty, and follow-up tests.










\section{Figures and Tables}

The paper package includes diagrams, diagram source files, and validation tables. The model should not live only as prose. The diagrams show the control plane. The tables show the evidence boundary.


\subsection{Figure 1. TeamStation Research OS Pipeline}

\begin{figure}[htbp]
\centering
\includegraphics[width=0.95\linewidth]{diagrams/figure-1-research-os-pipeline.pdf}
\caption{Pipeline diagram showing MCP retrieval, evidence ledger, manuscript production, review package, publish decision, control gate, and source-backed paper output.}
\end{figure}

Figure 1 caption: Figure 1. TeamStation Research OS pipeline from MCP retrieval through evidence validation, manuscript production, review package, and publication decision. The diagram purpose is to show how source-backed research becomes a reviewed working paper package and where unsupported claims return to source retrieval.


\subsection{Figure 2. Engineering Capacity as an Operating System Evidence Control Plane}

\begin{figure}[htbp]
\centering
\includegraphics[width=0.95\linewidth]{diagrams/figure-2-evidence-control-plane.pdf}
\caption{Layered control plane diagram showing source boundary, evidence classification, confidence scoring, traceability, weak claim blocking, decision packet, governance, publication gates, and learning loop.}
\end{figure}

Figure 2 caption: Figure 2. Engineering Capacity OS evidence control plane for classification, confidence, traceability, governance, publication gates, and feedback. The diagram purpose is to show the control boundary that prevents weak evidence from becoming an engineering recommendation or publication claim.


\subsection{Table Register}

| Table | Title | Purpose | Rows | File |

| --- | --- | --- | ---: | --- |

| Table 1 | Evidence Matrix | Table 1. Source hierarchy, sensitivity, clusters, and checksum. | 9 | tables/table-1-evidence-matrix.csv |

| Table 2 | Paragraph Validation Matrix | Table 2. Paragraph-level evidence tags and validation scores. | 13 | tables/table-2-paragraph-validation.csv |


\subsection{Table 1. Evidence Matrix Preview}

| Source | Tier | Title | Sensitivity |

| --- | --- | --- | --- |

| S001 | Engineering Research | Axiom Cortex and Latin America Agentic Engineering Alignment | internal |

| S002 | Engineering Research | Engineering Capacity Operating System | internal |

| S003 | Engineering Research | Engineering Capacity Operating System | internal |

| S004 | Architecture Documentation | engineering-operating-system.json | internal |

| S005 | Architecture Documentation | engineering-operating-system.v3.json | internal |

| S006 | Research Articles | Distributed Engineering Operating Systems | TeamStation AI Research | public |


\subsection{Table 2. Paragraph Validation Preview}

| Paragraph | Section | Evidence tag | Evidence score | Confidence |

| --- | --- | --- | ---: | ---: |

| P001 | Abstract | Supported | 0.95 | 0.9 |

| P002 | Research Questions | Observation | 0.8 | 0.9 |

| P003 | Objectives | Supported | 1.0 | 0.9 |

| P004 | Hypothesis | Hypothesis | 0.8 | 0.9 |

| P005 | Introduction | Supported | 0.95 | 0.9 |

| P006 | Methodology | Supported | 1.0 | 0.9 |

| P007 | Data Sources | Supported | 1.0 | 0.9 |

| P008 | Results | Observation | 1.0 | 0.9 |


\section{9. AI Integration in the Agentic Loop Era}

AI changes engineering capacity because it changes the cost of generating work. It does not remove the cost of validating work. That sentence should be written on the wall of every engineering leadership meeting in 2026.


Coding assistants can help developers write code faster in bounded tasks [PENG2023]. Field experiments suggest productivity effects exist but vary across organizations and contexts [CUI2026]. That variation is the point. A greenfield task with clear acceptance criteria is not the same as a high context legacy system with fragile tests, hidden domain rules, and overloaded reviewers. AI can reduce friction in one context and create friction in another.


In Engineering Capacity OS, AI agents are assigned by task variance and validation cost. Low variance tasks with strong tests, clear inputs, and reversible outputs are better candidates for automation. High variance tasks with ambiguous requirements, architecture implications, security boundaries, or product judgment require human ownership. The model does not ask whether AI can do the task once. It asks whether the system can safely rely on AI doing that task repeatedly.


MCP servers become important because they can expose the engineering system as governed tools and resources. A client or internal agent can retrieve architecture docs, inspect issue state, query delivery telemetry, run a diagnostic, or draft a recommendation without scraping screens or using broad credentials. That matters because agentic workflows need boundaries. They need permissioning, audit, and source provenance.


The agentic loop also changes team topology. A team may include human engineers, platform services, coding assistants, validation agents, documentation agents, security scanners, and governance agents. The question is no longer only how many people are on the team. The question is which cognitive and operational functions are performed by humans, which are performed by agents, and which interfaces keep the combined system reliable.


Evidence classification becomes more important, not less. AI makes confident prose cheap. Engineering leaders need a system that can say, a recommendation is unsupported, a recommendation is modeled, a recommendation is observed, and a recommendation requires external validation.


\section{10. Fictional Enterprise Walkthrough}

Consider a fictional company, Northstar Logistics. Northstar has 140 engineers, three product lines, a platform team, two external vendors, and a new executive mandate to deliver AI features in six months. The CTO believes the organization needs twenty more engineers. The CFO is skeptical. The VP Engineering reports that everyone is busy. Jira activity is high. Deployment frequency has dropped. The platform team is overwhelmed. Senior engineers spend hours each week reviewing pull requests from distributed teams. Product leaders complain that roadmap items are not moving.


A staffing diagnosis would ask how many roles are open and which vendor can fill them. Engineering Capacity OS starts differently.


The diagnostic first scopes the decision: should Northstar add twenty engineers in the next quarter to accelerate AI feature delivery? The evidence collection stage pulls DORA metrics, pull request age, review load, deployment history, test flake rate, service ownership, incident interruption, platform backlog, onboarding documentation, vendor output, and security access records.


Evidence classification finds several observed constraints. Pull request review age has doubled over two quarters. The top eight senior engineers are reviewers on 61 percent of active repositories. Test flake rate is high in three services that sit under the AI roadmap. The platform team owns deployment templates, environment support, and internal developer tooling, but its backlog is dominated by manual support requests. Service ownership is incomplete for several legacy services. Vendor teams show high ticket activity but low accepted pull request throughput. AI coding assistant adoption is high, but there is no attribution policy and no measurement of AI-generated rework.


The constraint map shows that the problem is not raw headcount. The main constraints are review capacity, platform bottleneck, weak knowledge architecture, inconsistent execution harness, and missing AI governance. Topology fit for adding twenty contributors is low because the organization cannot absorb more pull requests. Automation leverage is unknown because AI output is not measured through acceptance, rework, or maintainability.


The readiness score is mixed. Knowledge architecture is weak. Execution determinism is medium. Governance readiness is medium for humans and weak for agents. Topology fit is weak. Telemetry trust is medium because some metrics are strong but AI attribution is missing. Automation leverage is unknown. Decision readiness for hiring is low.


The recommendation is: do not hire twenty engineers yet. The evidence does not support the hiring intervention. The recommended topology decision is to create a short-term capacity repair program. First, fund a platform stabilization lane for CI/CD templates, flaky test reduction, and environment reliability. Second, assign service ownership and update architecture memory for AI roadmap services. Third, reduce review concentration by creating ownership groups and review rules. Fourth, define AI coding assistant governance: attribution, safe task classes, generated code review policy, and rework measurement. Fifth, after six weeks, rerun the diagnostic and decide whether a nearshore pod, platform hiring, or internal hiring is now absorbable.


The recommendation will frustrate some executives because it does not give the simple answer they expected. That is the point. The model refuses to pour more capacity into a system that cannot absorb it. The answer is not anti-hiring. The answer is evidence classified sequencing. Fix the system constraint first. Add capacity when the operating system can convert that capacity into business value.


\section{11. Discussion}

The strength of Engineering Capacity OS is that it makes capacity decisions harder to fake. A leader cannot hide behind headcount. A vendor cannot hide behind activity. An AI agent cannot hide behind confident recommendations. The model asks for evidence, classifies that evidence, maps constraints, and then treats capacity interventions as topology choices.


The model also has limitations. It depends on telemetry quality. If the organization's data is wrong, stale, or politically manipulated, the diagnostic can produce false confidence. It depends on source access. Some organizations cannot expose Slack, repository, or incident data for legal or security reasons. It depends on judgment. Not every important signal can be reduced to a number. Architecture context, product ambiguity, trust, and leadership behavior still require human interpretation.


There is also a risk of over-systematizing people. Engineering organizations are human systems. Morale, trust, learning, conflict, and identity matter. The model should not turn people into interchangeable nodes. The point is not to erase human judgment. The point is to stop pretending that human judgment works well without evidence.


The AI governance problem is especially important. AI agents will create pressure to move faster than the evidence base can support. Organizations may automate code generation before automating validation. They may count AI output as capacity without measuring review cost. They may let agent tools operate without clear approval boundaries. In that environment, the evidence taxonomy is not optional. It is the difference between speed and uncontrolled operational debt.


Future work should externally replicate the model across organizations outside TeamStation. The first validation path is retrospective: take past capacity decisions, classify available evidence at the time, and compare the model's recommendation with the outcome. The second path is prospective: run the diagnostic before a capacity intervention and track changes in DORA, SPACE-informed developer experience, review queues, incident interruption, and business delivery. The third path is agentic: measure whether governed AI task classes reduce constraints or simply move cost downstream. Internal operational validation is complete enough to justify the working paper. Independent external replication remains future work.


\section{12. Conclusion}

Engineering capacity should be managed like an operating system, not a staffing spreadsheet. The organization needs memory, scheduling, access control, telemetry, execution rules, failure handling, and governed automation. Without that operating system, headcount becomes a blunt instrument. It may increase activity while making delivery less predictable.


The core principle is simple: classify evidence before recommending organizational change. A system that cannot distinguish observed evidence from modeled evidence, directional evidence, and unknowns should not recommend hiring, outsourcing, insourcing, vendor replacement, or AI automation. The recommendation may sound confident, but confidence without evidence is just another form of operational noise.


The agentic loop era raises the stakes. AI will make work generation faster. It will also make governance, validation, topology, and telemetry more important. The leaders who win will not be the ones who add the most engineers or the most agents. They will be the ones who build an engineering operating system that knows where capacity actually comes from, where it is being lost, and what evidence is strong enough to act on.


\section{Declarations}

Ethics: The paper does not report human-subject research. The enterprise walkthrough is fictional and used only to demonstrate the diagnostic model.


Conflict of interest: Lonnie McRorey is Founder and CEO of TeamStation AI. TeamStation AI develops distributed engineering operating system methods, research artifacts, and software related to the subject of the paper. The paper is written as a general systems model and should be evaluated independently of TeamStation commercial claims before publication.


Funding: No external funding is declared in the draft, and the author should confirm the funding statement before formal submission.


Author contributions: Lonnie McRorey supplied the thesis, domain framing, TeamStation doctrine, and authorial direction. AI assistance was used inside Codex to retrieve sources, draft manuscript text, format artifacts, and run validation checks. Human review remains required before public submission.


Data availability: The paper relies on public external sources and TeamStation public or internal research artifacts listed in the references and source appendix. No confidential client telemetry is included.


Code availability: The Research OS prototype and generated package exist in the local TeamStation Codex workspace. Public release status has not been declared.


AI use disclosure: AI tools were used to assist with retrieval, synthesis, drafting, formatting, and validation. AI tools are not authors. The named human author is responsible for review, source verification, interpretation, declarations, and final approval.


\section{References}

The readable reference list below is generated from the same source register used to emit BibTeX, BibLaTeX, RIS, CSL JSON, schema metadata, and the TeamStation science corpus. TeamStation sources are included for traceability and internal research context. They are not treated as independent external validation.


1. **[DORA2026]** DORA, 2026, Software delivery performance metrics, [https://dora.dev/guides/dora-metrics/](https://dora.dev/guides/dora-metrics/)

1. **[SPACE2021]** Forsgren, Nicole and Storey, Margaret-Anne and Maddila, Chandra and Zimmermann, Thomas and Houck, Brian and Butler, Jenna, 2021, The SPACE of Developer Productivity, [https://queue.acm.org/detail.cfm?id=3454124](https://queue.acm.org/detail.cfm?id=3454124)

1. **[TEAMTOPOLOGIES2026]** Team Topologies, 2026, Team Topologies Key Concepts, [https://teamtopologies.com/key-concepts](https://teamtopologies.com/key-concepts)

1. **[PENG2023]** Peng, Sida and Kalliamvakou, Eirini and Cihon, Peter and Demirer, Mert, 2023, The Impact of AI on Developer Productivity: Evidence from GitHub Copilot, [https://arxiv.org/abs/2302.06590](https://arxiv.org/abs/2302.06590)

1. **[CUI2026]** Cui, Kevin Zheyuan and Demirer, Mert and Jaffe, Sonia and Musolff, Leon and Peng, Sida and Salz, Tobias, 2026, The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers, [https://doi.org/10.1287/mnsc.2025.00535](https://doi.org/10.1287/mnsc.2025.00535)

1. **[TSECOS2026]** TeamStation Engineering, 2026, Engineering Capacity Operating System Research, [https://engineering.teamstation.dev/api/research/engineering-operating-system.md](https://engineering.teamstation.dev/api/research/engineering-operating-system.md)

1. **[TSDEOS2026]** TeamStation AI Research, 2026, Distributed Engineering Operating Systems, [https://teamstation.dev/research/articles/distributed-engineering-operating-systems-control-plane-model](https://teamstation.dev/research/articles/distributed-engineering-operating-systems-control-plane-model)

1. **[TSAXIOM2026]** Lonnie McRorey, 2026, Axiom Cortex for LATAM Agentic Engineering, [https://teamstation.dev/research/articles/axiom-cortex-latin-america-agentic-engineering-alignment](https://teamstation.dev/research/articles/axiom-cortex-latin-america-agentic-engineering-alignment)

1. **[TSNEWSDEOS2026]** Lonnie McRorey, 2026, TeamStation Distributed Engineering OS, [https://teamstation.dev/research/articles/teamstation-ai-publishes-distributed-engineering-os-model-for-agentic-ai-teams](https://teamstation.dev/research/articles/teamstation-ai-publishes-distributed-engineering-os-model-for-agentic-ai-teams)

1. **[TSCF2026]** TeamStation AI R\&D Lab Staff, 2026, Cognitive Fidelity and the Turing Trap, [https://teamstation.dev/research/articles/cognitive-fidelity-and-the-turing-trap](https://teamstation.dev/research/articles/cognitive-fidelity-and-the-turing-trap)

1. **[TSTOPOS2026]** TeamStation AI R\&D Lab Staff, 2026, Agentic Team Topologies for CTOs and CIOs, [https://teamstation.dev/research/articles/team-topologies-in-the-agentic-workflow-era-beyond-2026](https://teamstation.dev/research/articles/team-topologies-in-the-agentic-workflow-era-beyond-2026)

1. **[TSTEAMTOPO2026]** TeamStation AI R\&D Lab Staff, 2026, Software Engineering Team Topologies for 2026, [https://teamstation.dev/research/articles/software-engineering-team-topologies-for-2026](https://teamstation.dev/research/articles/software-engineering-team-topologies-for-2026)

1. **[TSHIDDENMATH2026]** TeamStation AI R\&D Lab Staff, 2026, Hidden Math of Distributed Engineering Failure, [https://teamstation.dev/research/articles/the-hidden-math-behind-distributed-engineering-failure](https://teamstation.dev/research/articles/the-hidden-math-behind-distributed-engineering-failure)

1. **[TSAGENTICLATAM2026]** TeamStation AI R\&D Lab Staff, 2026, 2027 Agentic Team Topologies in LATAM, [https://teamstation.dev/research/articles/the-2027-blueprint-for-agentic-engineering-team-topologies-in-latin-america](https://teamstation.dev/research/articles/the-2027-blueprint-for-agentic-engineering-team-topologies-in-latin-america)

1. **[TSDOCTRINE2026]** TeamStation Engineering, 2026, Engineering Capacity Operating System Research, [https://engineering.teamstation.dev/research/engineering-operating-system](https://engineering.teamstation.dev/research/engineering-operating-system)

1. **[TSDOCTRINEMD2026]** TeamStation Engineering, 2026, Engineering Capacity Operating System Markdown Source, [https://engineering.teamstation.dev/api/research/engineering-operating-system.md](https://engineering.teamstation.dev/api/research/engineering-operating-system.md)

1. **[TSCOGFIDELITYDOCTRINE2026]** TeamStation Engineering, 2026, Cognitive Fidelity Doctrine, [https://engineering.teamstation.dev/quality/cognitive-fidelity/](https://engineering.teamstation.dev/quality/cognitive-fidelity/)

1. **[TSAGENTICWORKFLOWDOCTRINE2026]** TeamStation Engineering, 2026, Agentic Engineering Workflows in Distributed Team Topologies, [https://engineering.teamstation.dev/teams/agentic-development-workflows/](https://engineering.teamstation.dev/teams/agentic-development-workflows/)


\section{Glossary}

- **Agentic loop:** A governed cycle where a human, AI agent, tool, source system, and validation gate turn intent into checked output.

- **Axiom Cortex:** TeamStation's cognitive evaluation layer for mental shape, engineering judgment, role fit, and agentic workflow alignment.

- **Capacity Intelligence:** The subsystem that estimates usable capacity after cognitive load, review limits, interruptions, topology, and decision latency are accounted for.

- **Decision grade telemetry:** Operational signal with known source, definition, freshness, bias, and direct connection to a capacity or governance decision.

- **Distributed Engineering Operating System:** The TeamStation operating model connecting talent signal, team topology, governance, telemetry, compliance, payroll, devices, MDM, and delivery controls into one source of truth.

- **Engineering Capacity OS:** The research model proposed in the paper for treating capacity as knowledge, execution, governance, topology, telemetry, and automation, not as headcount alone.

- **Evidence classification:** The rule that every claim is tagged as observed, modeled, directional, unknown, opinion, hypothesis, future work, internal research, or external validation before recommendation.

- **Governed Agentic SDLC:** A software delivery system where AI assistants and agents operate inside explicit permissions, evidence capture, approval gates, rollback paths, and human ownership.

- **Knowledge Architecture:** The memory layer of the engineering system: architecture decisions, ownership, runbooks, domain rules, glossary, onboarding path, and incident learning.

- **MCP:** Model Context Protocol, used here as the governed retrieval and tool layer connecting agents to TeamStation research, engineering doctrine, APIs, diagrams, and knowledge sources.

- **Nebula Talent Graph:** TeamStation's talent intelligence layer for representing engineering capability, signal, role alignment, and topology fit.

- **Topology choice:** A decision about where work should live: internal team, platform team, enabling team, nearshore pod, vendor, agent, automation lane, or no expansion.


\section{Appendix A. Diagnostic Output Schema}

A complete diagnostic packet should include: business objective, scope, evidence inventory, source sensitivity, evidence classification, constraint map, readiness scores, topology options, rejected alternatives, recommendation, confidence, risks, missing evidence, required validation, owner, date, and follow-up review point.


\section{Appendix B. Minimum Evidence Required Before Capacity Recommendations}

Before recommending hiring, the system should know review queue age, onboarding readiness, service ownership, work type, decision latency, test reliability, and expected reviewer availability. Before recommending a vendor or distributed pod, the system should know ownership boundary, access boundary, documentation freshness, collaboration window, acceptance tests, telemetry access, and exit path. Before recommending AI automation, the system should know task variance, validation method, human approval boundary, attribution policy, rollback path, and rework measurement.


\section{Appendix C. Evidence States}

Observed: directly measured or documented from the operating system. Modeled: computed from explicit assumptions and inputs. Directional: useful signal that requires stronger validation. Unknown: evidence not available or not trustworthy enough for the decision.

\end{document}