# Work Orchestration Best Practices — External Research

> Research artefact — 2026-07-19. Phase 2 of the work-orchestration
> reconciliation effort. Surveys current (2025/2026) practice for organizing
> human, agent, and classic-automation workflows, filtered for relevance to
> a file-first, repo-anchored, solo-founder-plus-agent-fleet factory.
> Companion: `2026-07-19-work-orchestration-infrastructure-survey.md`.

## 1. GitOps / everything-as-code

The industry consensus for declarative operations maps directly onto ADR-001
and generalizes it:

- **Four GitOps principles**: declarative desired state; versioned &
  immutable storage; state pulled automatically by agents; continuous
  reconciliation between desired and actual state
  ([GitLab](https://about.gitlab.com/topics/gitops/),
  [Spacelift](https://spacelift.io/blog/gitops-best-practices)).
  Our `fix-consistency` is exactly a reconciliation loop; the gap is that it
  runs on demand rather than continuously, and only covers workplans/tasks —
  not queues, suggestions, or decisions.
- **Validate before apply**: schema validation, policy compliance, dry-run
  in CI before a change reaches the runtime system
  ([Pulumi](https://www.pulumi.com/blog/gitops-best-practices-i-wish-i-had-known-before/)).
  Applied to work items: queue/workplan YAML should be schema-validated in
  Forgejo CI, not only at hub-ingest time.
- **Separate code and state repos** where lifecycles differ
  ([GitHub blog](https://github.blog/enterprise-software/devops/applying-gitops-principles-to-your-operations/))
  — consistent with ADR-005 project repos and control-plane repos like
  binky-control.
- **Ops-as-issues / IssueOps**: operations triggered and audited through
  issue lifecycles. Validation of our issue-core direction; the key lesson
  is that the issue is the *interface*, not the source of truth.

## 2. Human-in-the-loop tiering for agentic work

2026 agentic-governance literature converges on patterns our AutonomyPolicy
already anticipates — with refinements worth adopting:

- **Risk-tiered approval, not blanket oversight**: pre-approval
  (human-in-the-loop) for irreversible actions (payments, contracts,
  deletion, production changes); async monitoring (human-on-the-loop) for
  high-volume recoverable work
  ([Strata](https://www.strata.io/blog/agentic-identity/practicing-the-human-in-the-loop/),
  [StackAI](https://www.stackai.com/insights/human-in-the-loop-ai-agents-how-to-design-approval-workflows-for-safe-and-scalable-automation)).
  Maps to Green/Blue (on-the-loop) vs. Yellow/Orange/Red (in-the-loop).
- **Approval gates before side effects — propose-then-commit**: block the
  side-effecting call until approval is recorded
  ([Velt](https://velt.dev/blog/why-ai-agents-need-approval-layer)). Our
  approval-package pattern satisfies this at the process level; the harness
  tool-profile mechanism is where it becomes technically enforced.
- **Identity-aware orchestration**: pause execution, route to authorized
  humans, time-boxed decision windows, log every intervention
  ([Strata](https://www.strata.io/blog/agentic-identity/practicing-the-human-in-the-loop/)).
  `needed_by` / `fallback_if_no_response` in DecisionQueue are exactly
  time-boxed windows; the hub should own the clock, not the file reader.
- **Every agent has an owner, risk profile, and documented decision
  boundary**; outputs traceable to model versions and configs
  ([CMR "Governing the Agentic Enterprise"](https://cmr.berkeley.edu/2026/03/governing-the-agentic-enterprise-a-new-operating-model-for-autonomous-ai-at-scale/),
  [McKinsey](https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/trust-in-the-age-of-agents)).
  Matches `agt-…` hub identities + manifests; argues for making `lane` and
  `owner` mandatory fields on every work item, not just on agents.
- **Durable pause/resume**: graph-based orchestrators persist state so a
  workflow can wait days for human input and resume losslessly
  ([Virtido](https://virtido.com/blog/agentic-workflows-patterns-best-practices-enterprise)).
  Our equivalent is the file+hub pair: a Yellow item *is* a paused workflow;
  no in-memory orchestrator state should be required to resume it.
- **Reviewer quality matters**: "a poorly trained reviewer approving flawed
  agent outputs is worse than no checkpoint"
  ([nhimg.org](https://nhimg.org/faq/how-should-security-teams-implement-human-in-the-loop-controls-for-ai-agents/))
  — supports the approval-*package* discipline (evidence, recommendation,
  fallback) over bare questions, and the attention-budget cap.

## 3. Flow management: WIP limits, metrics, prioritization

Kanban/lean portfolio practice supplies the quantitative layer the fleet
lacks:

- **Four flow metrics**: WIP, cycle time, work-item age, throughput
  ([ProKanban](https://www.prokanban.org/blog/https-prokanban-org-blog-the-kanban-pocket-guide-chapter-6-the-basic-metrics-of-flow)).
  All four are computable from hub data (timestamped UUIDs + status
  transitions) — none is surfaced today. Work-item age is the direct,
  data-driven form of the "stalled" tag (aspect 6).
- **WIP limits at every state**, including portfolio level; WSJF
  (cost-of-delay / job size) chooses the next item, WIP limits decide
  whether the system may accept it
  ([Agility at Scale — WIP](https://agility-at-scale.com/safe/lpm/work-in-progress/),
  [WSJF](https://agility-at-scale.com/safe/lpm/wsjf-weighted-shortest-job-first/)).
  The founder attention budget (max 7 decisions/day) is a WIP limit on the
  human workstation; the same construct should apply to agent lanes
  (e.g. max active workplans per repo). A WSJF calibration experiment
  already exists (`docs/daily-statehub-wsjf-calibration-2026-06-04.md`).
- **Epics as economic bets** with testable benefit hypotheses, decomposed
  only for the near horizon; close the measurement loop on predicted
  outcomes ([Agility at Scale — Epics](https://agility-at-scale.com/safe/lpm/epics/)).
  Maps to domain/repo goals → workplans; argues for a lightweight
  `hypothesis`/`expected_outcome` field on goals, checked at closure review.
- **Lean budget guardrails**: fund value streams within policy boundaries
  instead of approving each piece of work; segment budgets per area of risk
  ([Atomic Object](https://spin.atomicobject.com/budget-guardrails-software-project/),
  [Agility at Scale — Guardrails](https://agility-at-scale.com/safe/lpm/portfolio-guardrails/)).
  Direct template for token/cost budgets: per-lane and per-workplan budget
  envelopes with escalation on breach, instead of per-request permission.

## 4. Dependency and risk practice

- **Dependency mapping to see cascade risk**; identify risks early, attach
  mitigation to the item
  ([Asana](https://asana.com/resources/project-risks),
  [Atlassian](https://www.atlassian.com/work-management/project-management/project-risk-management)).
  The hub has dependency edges (task- and workplan-level); the gap is
  surfacing them (blocked-cascade views) and the absence of edges for
  queue-item species.
- **Risk classification determines autonomy level, data access, and
  approval requirements per agent**
  ([TM Forum](https://inform.tmforum.org/features-and-opinion/ai-autonomy-governance-a-governance-framework-for-agentic-ai-enabling-safe-accountable-and-scalable-autonomous-intelligence),
  [Trend Micro](https://www.trendmicro.com/vinfo/us/security/news/cybercrime-and-digital-threats/from-anarchy-to-authority-closing-the-governance-gap-in-agentic-ai)).
  Confirms lanes-as-risk-classes; suggests lane should also gate credential
  catalog access (ops-warden already keyed per-lane).

## 5. Patterns worth *not* importing

- **Heavy portfolio ceremony** (SAFe boards, quarterly PI planning): the
  attention budget is 90 min/day; anything requiring synchronous ceremonies
  fails the constraint. Take the *metrics and limits*, skip the meetings.
- **Ticket-system-as-source-of-truth** (Jira-style): contradicts ADR-001;
  issues/tickets remain interface projections.
- **Generic workflow engines as the primary model** (BPMN, Airflow-style
  DAGs for knowledge work): our work is document-anchored and
  interruption-driven; the task-flow-engine's assertion model (state =
  provable properties, not engine position) is the better fit and is already
  specced.
- **Per-item human approval as default**: the literature is unanimous that
  blanket approval queues collapse under volume; risk-tiering is the
  scalable form — the AutonomyPolicy default ("ask Bernd is never the
  default") is the correct opinion.

## 6. Condensed implications for the draft

1. Treat *work intake* and *work execution* as separate concerns with
   different friction budgets — intake must cost one YAML block or less
   (the queues proved this), execution structure can cost a workplan file.
2. Every work-item species must live in the same reconciliation loop
   (file → CI validation → hub index → derived views) or it becomes a
   sidetrack by construction.
3. Human attention is a WIP-limited workstation; model it exactly like an
   agent lane with a budget, a queue, and flow metrics.
4. States should be few and abstract (canon lifecycle); everything
   situational (stalled, unclear, automatable, needs-human) should be a
   **derived or policy-assigned tag**, not a state — states migrate badly,
   tags migrate trivially.
5. Timestamped UUIDs internally + stable canonical names externally is the
   correct layering and is industry-consistent (UUIDv7 for time-sortable
   internal keys).
6. Budgets become guardrail envelopes (per lane / workplan / run) enforced
   at the runtime layer (harness), metered to the hub, escalated on breach.

## Sources

- https://about.gitlab.com/topics/gitops/
- https://www.pulumi.com/blog/gitops-best-practices-i-wish-i-had-known-before/
- https://spacelift.io/blog/gitops-best-practices
- https://github.blog/enterprise-software/devops/applying-gitops-principles-to-your-operations/
- https://virtido.com/blog/agentic-workflows-patterns-best-practices-enterprise
- https://www.strata.io/blog/agentic-identity/practicing-the-human-in-the-loop/
- https://velt.dev/blog/why-ai-agents-need-approval-layer
- https://www.stackai.com/insights/human-in-the-loop-ai-agents-how-to-design-approval-workflows-for-safe-and-scalable-automation
- https://nhimg.org/faq/how-should-security-teams-implement-human-in-the-loop-controls-for-ai-agents/
- https://cmr.berkeley.edu/2026/03/governing-the-agentic-enterprise-a-new-operating-model-for-autonomous-ai-at-scale/
- https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/trust-in-the-age-of-agents
- https://inform.tmforum.org/features-and-opinion/ai-autonomy-governance-a-governance-framework-for-agentic-ai-enabling-safe-accountable-and-scalable-autonomous-intelligence
- https://www.trendmicro.com/vinfo/us/security/news/cybercrime-and-digital-threats/from-anarchy-to-authority-closing-the-governance-gap-in-agentic-ai
- https://agility-at-scale.com/safe/lpm/work-in-progress/
- https://agility-at-scale.com/safe/lpm/wsjf-weighted-shortest-job-first/
- https://agility-at-scale.com/safe/lpm/epics/
- https://agility-at-scale.com/safe/lpm/portfolio-guardrails/
- https://spin.atomicobject.com/budget-guardrails-software-project/
- https://www.prokanban.org/blog/https-prokanban-org-blog-the-kanban-pocket-guide-chapter-6-the-basic-metrics-of-flow
- https://asana.com/resources/project-risks
- https://www.atlassian.com/work-management/project-management/project-risk-management