acdl/docs/presentations/nova-no-humans-platform-marp.md

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Nova — The No-Humans Infrastructure Platform

Shifting from Operational Overhead to Strategic Value

v1.18 — Citizen Developer & Production-Grade Guidance

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Slide 1 — Arc Preview

This deck proves Nova is the no-humans infrastructure platform — and shows you the metrics that make the claim defensible.

Today: 18 capabilities verified, 0 consumer estates in production.

The 5-act arc:

1. Problem — why the operator is the bottleneck
2. Vision — Nova's strategic direction (NORTH_STAR)
3. How — the pipeline, Decision Ledger, attestation gates
4. Proof — grounded metrics that make the claim defensible
5. Roadmap — deferred metrics with unblock paths + the ask + scope + RACI

Benefit: you leave knowing which claims are proven today, which are pipeline-ready, and which are deferred with a documented unblock path — no marketing, just grounded evidence.

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Slide 2 — The No-Humans Imperative

Why the operator is the bottleneck — and why removing them from operations (not accountability) is the imperative.

• The cost of humans-in-the-loop: L1/L2 ops hours, escalation latency, the trust gap
• The operator is the bottleneck: provisioning takes days, not minutes
• The attestation model: autonomy in operations, human at stage gates
• Cites docs/NO_HUMANS_THESIS.md

Benefit: you now know the problem framing — autonomy in operations, human at stage gates, is the path forward.

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Slide 3 — Nova's Vision

Infrastructure operations become invisible. Every environment provisioned, every incident healed, every risk remediated — by an autonomous system whose trustworthiness is provable, not promised. Human attestation remains required at stage gates — QA signs off for production, SRE greenlights based on operational readiness — but the operator is never in the loop of normal operations.

• Autonomy in operations, not in accountability
• Cites docs/NO_HUMANS_THESIS.md

Benefit: you now know the destination — invisible operations with provable trust, not promised trust.

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Slide 4 — Strategic Objectives + Anti-Goals

4 Strategic Objectives:

1. Zero-touch operations — autonomy as the default, not the demo
2. Provable trust in AI decisions — Decision Ledger, confidence scoring, circuit breakers
3. Compounding, quantifiable ROI — each quarter must reduce spend, free hours, avoid downtime
4. Default substrate for agentic consumption — the platform AI agents reach for first

5 Anti-Goals (what Nova is NOT):

1. Not a hyperscaler competitor
2. Not a general-purpose AI platform
3. Not removing humans from accountability
4. Not for legacy, untagged, or freeform infrastructure
5. Not sold to operators

Benefit: you now know the scope boundaries — Nova is purpose-built for infrastructure operations, sold to leadership on outcomes.

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Slide 5 — 12–18 Month Targets

Current-milestone targets (grounded/derived):

Domain	Target	Status
MTTR (p95)	< 60s	grounded
Cloud Spend Reduction	≥ 25%	partial (CUR deferred D-096)
L1/L2 Ops Hours Avoided	≥ 70%	derived (N internal runs)
Platform ROI	≥ 250%	derived (formula; N=0 caveat)
Decision Ledger Coverage	100%	grounded
Attestation Coverage	100%	grounded

Post-Pilot targets (pipeline grounded; 0 consumers today):

Domain	Target	Status
Touchless Resolution Rate	≥ 99%	partial
Human Escalation Frequency	< 0.1%	partial
AI Decision Accuracy	≥ 99.5%	partial

Deferred: Predictive vs Reactive ≥3:1 Planned · Drift Auto-Reversal ≥95% Planned

Benefit: you now know the destination numbers — and which are measurable today vs deferred honestly.

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Slide 6 — The Platform Pipeline

How intent becomes verified infrastructure without an operator.

Contract → Resolver → Adapter → Terraform Plan → Checkov (Policy) → Confidence Signal → HITL Gate → Apply → Evidence

• Dev: autonomous (no HITL gate)
• qa/prod/dr: attested (human sign-off required)
• Grounded in run_platform.sh + contract_resolver.py + confidence_signal.py

Benefit: you now know the path from intent to evidence — and where the human appears (stage gates only).

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Slide 7 — The Decision Ledger

Every AI decision captured with confidence, alternatives, and outcome.

• outbox_writer.py → SQLite append-only hash-chain table
• ai.decision.made: decision_id=run_id, chosen_action=band, confidence=score, alternatives=perInput, human_override=HITL block
• attestation.recorded: qa/prod/dr sign-offs
• D-121, D-122, D-132. Honors D-083 (no S3 Object Lock/JWS — local hash-chain)

D-122 honesty: Nova's "AI" is the confidence-gated policy engine (confidence_signal + HITL gate), not an LLM planner. The Decision Ledger captures this real decision path — not a fabricated "AI agent."

Benefit: you now know why 'autonomous' is defensible — every decision is immutable, queryable, and accountable. And you know exactly what 'AI' means here: a confidence-gated policy engine, not a black-box LLM.

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Slide 8 — The 8-Concern Attestation Matrix

Designed controls that keep humans at stage gates.

Concern	Env	Freshness	Type
functional_correctness	qa	24h	operator-supplied
performance_baseline	qa	7d	operator-supplied
security_posture	qa	24h	operator-supplied
operational_readiness	prod	30d	operator-supplied
incident_response	prod	90d	operator-supplied
capacity_cost	prod	30d	operator-supplied
resilience_dr_drill	prod	180d	operator-supplied
dr_region_deploy	dr	180d	operator-supplied

• Offline-testable concerns run for real; operator-supplied concerns accept signed evidence
• Separation-of-duties on prod
• Grounded in attestation_matrix.py + hitl_gates.py

Benefit: you now know the gate model — autonomy in operations, human in accountability, by design.

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Slide 9 — Telemetry Architecture

How Nova instruments itself — CloudEvents envelope, cold store, PowerBI export.

Platform → CloudEvents 1.0 → metrics/events.jsonl + metrics/decision_ledger.db + metrics/runs/ → Collector → metrics/nova_metrics.db (SQLite cold store) → metrics/powerbi/ (CSV/JSON) → PowerBI

• D-120 (Nova-native), D-125 (hybrid), D-126 (cold-only)
• Planned: Hot-path (live ops dashboard) — D-126

Benefit: you now know that every metric in this deck is traceable to a real emitted event — the architecture IS the trust substrate. When a CFO asks 'where does this number come from?', the answer is a file path, not a Slack thread.

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Slide 10 — Capability Health + Confidence Distribution

Grounded proof: capability health and confidence distribution from real runs.

Status	Count
Verified	18
Skipped	4
Broken	0
Decayed	0

• 4 Skipped = live-AWS caps (CAP-013..016), honestly skipped (D-096 teardown), not a failure
• Source: .ciagent/REGRESSION_REPORT.json

Benefit: you now know the platform is verified — 18 capabilities pass, 4 are honestly skipped, 0 broken.

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Slide 11 — Decision Ledger + Attestation Coverage

Trust metrics — both 100%.

• Decision Ledger Coverage: 100% of platform runs emit ai.decision.made with outcome backfill
• Attestation Coverage: 100% of prod/dr promotions attested by a human
• AI Decision Accuracy: decisions not followed by apply.failed/incident within 5min
• Trust snapshot: metrics/TRUST_SNAPSHOT.md with chain-integrity verdict
• Planned: Tamper-Evident Ledger Checkpoints (D-083)

Benefit: you now know the trust is provable — not a marketing claim, a queryable record.

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Slide 12 — Zero-Touch Efficiency

Touchless resolution, human escalation, and MTTR.

• Touchless Resolution Rate: runs without operational HITL block ÷ total (attestation gates excluded)
• Human Escalation Frequency: operational HITL blocks only (confidence-driven; attestation sign-offs excluded)
• MTTR (platform-run): apply.failed → successful retry (D-131)

Post-Pilot caveat: computed on N internal runs today; production-denominator activates when a pilot estate runs.

Benefit: you now know the zero-touch efficiency is measurable — the pipeline works today on internal runs, and the denominator expands to production estates when a pilot activates.

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Slide 13 — Cost & ROI

Cost estimates and the ROI formula — with honest caveats.

• Cost Estimates via Infracost: pre-apply, grounded (reads plan JSON, offline)
• ROI formula: Platform ROI = (FTE hours saved × blended rate + cloud savings + avoided downtime) ÷ platform op cost
• N=0 caveat: "Computed on N internal runs today; production-denominator activates post-pilot. The formula is grounded; the production numbers are not yet."
• Planned: Live CUR Reconciliation (D-096)

Benefit: you now know the ROI formula — and you know it's computed on internal runs today, not fabricated production numbers.

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Slide 14 — What's Deferred — and Why

Honesty about what isn't measured yet.

To be clear: these deferrals are measurement infrastructure, not whether the platform runs without humans. The platform IS autonomous in operations. What's deferred is the evidence pipeline for certain metrics — not the autonomy itself.

#	Deferred Metric	Blocking Decision
1	Live Infrastructure Health	D-096
2	Live Outbox Write Rate	D-096
3	Tamper-Evident Ledger Checkpoints	D-083
4	Onboarding Funnel (granted)	D-113/D-114/D-119
5	Drift Auto-Reversal	D-096 + no scheduler
6	Live CUR Reconciliation	D-096
7	SLA / Unplanned Downtime	D-096
8	Predictive vs Reactive	future emitter

Benefit: you now know the boundaries — what Nova measures today, and exactly what blocks the rest. The autonomy is real; the measurement gaps are documented.

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Slide 15 — Roadmap to the North Star

The path from v1.17's grounded metrics to the 12–18 month targets.

• Each deferred metric → blocking decision → unblock requirement → candidate milestone
• Hot-path activation (post-D-096, Nova-native only, D-120)
• Re-evaluation triggers: D-096 lift, D-083 lift, onboarding-grant lift

From docs/METRICS_DEFERRED_ROADMAP.md.

Benefit: you now know the path — every deferred metric has an unblock requirement and a candidate milestone. Nothing is hand-waved; everything has a plan.

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Slide 16 — Recap + Ask

The 5-act recap + the business decision.

Recap:

• Problem: operator is the bottleneck; autonomy in operations, human at stage gates
• Vision: invisible operations with provable trust (NORTH_STAR)
• How: pipeline + Decision Ledger + 8-concern attestation matrix
• Proof: 18V+4S, 100% ledger coverage, 100% attestation, grounded ROI formula
• Roadmap: deferred metrics have unblock paths

The ask: "Approve a pilot estate to activate the production-denominator metrics (Touchless Resolution, Human Escalation, AI Decision Accuracy), and approve the tamper-evident ledger build-out (D-083 lift) to move from local hash-chain to S3 Object Lock + JWS. These two decisions move Nova from 'pipeline-ready' to 'production-proven.'"

Benefit: you leave with a clear business decision to make — approve a pilot + the ledger build-out — and the confidence that every claim in this deck is grounded, derived, or honestly deferred.

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Slide 17 — Scope: Downstream of PDLC

Nova governs infrastructure + delivery. The PDLC (product backlog, code authorship, IDE) is upstream — Nova never penetrates it.

• The PDLC is upstream: product backlog, code authorship (AI agent / IDE / agentic SDLC), sprint planning, application business logic
• Nova is downstream: contract ingestion → submission-readiness gate → policy → cloud lifecycle → environment progression → audit + attestation
• Integration is only through the contract boundary: the citizen developer's AI coding agent, an upstream agentic SDLC, or any dev platform may all produce submissions — the source does not matter as all are subject to the same compliance standards
• Nova validates the submission, not the author
• Cites docs/scope.md + PROJECT.md § Scope

Benefit: you now know the scope boundary — Nova is purpose-built for infrastructure operations, not product development; integration is through one validated contract.

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Slide 18 — RACI: Who Owns What

Three roles, one matrix — the citizen developer owns FRs + UAT, the platform owns NFRs + infra + QA + prod deploy, release management is co-owned.

Work Category	Citizen Dev	Platform	Release Mgmt
Functional Requirements (FRs)	R/A	C	I
User Acceptance Testing (UAT)	R/A	C	I
Non-Functional Requirements (NFRs)	I	R/A	C
Infrastructure (cloud, state, IAM)	I	R/A	C
QA (policy, confidence, schema)	C	R/A	I
Production deployment to cloud	I	R/A	C
Release attestation (QA + SRE)	A	R	R

• Compliance-standard equivalence: FRs + UAT may come from any upstream source (AI agent, agentic SDLC, dev platform) — all pass the same submission-readiness gate
• Release co-ownership: the platform runs the attestations agentically; the citizen developer oversees and triggers the actual release (human at the stage gate)
• Cites docs/raci.md + PROJECT.md § RACI Matrix

Benefit: you now know exactly what you bring (FRs + UAT), what Nova provides (NFRs + infra + QA + prod deploy), and what you co-own (the release attestation).

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Slide 19 — Production-Grade Guidance via Atelier

Nova instructs the citizen developer's AI agent on production-grade engineering — skills + an MCP server with agentic validation beyond deterministic scanners.

• Skills (9): markdown files under skills/ keyed to Atelier domain paths (api, security, data, testing, observability, errors, devops, infrastructure-as-code, compliance) — extending the BA.A 5-skill catalog
• MCP server: mcp/atelier/server.py (plugin-registry, stdio) — 4 tools: lookup_principle, list_domains, matrix_lookup, validate_against_principles
• Agentic validation: catches C1 correctness + C2 clarity + C7 observability gaps that Wiz/Checkmarx/Mend cannot — deterministic tools check policy/secrets; the MCP server checks engineering discipline
• Vendored Atelier (pinned tag v0.3.6): audit reproducibility — a validation result is replayable against the exact principles that produced it
• Cites docs/skills.md + mcp/atelier/README.md

Benefit: you now know the citizen developer is not unguided — Nova provides production-grade engineering principles via skills + an MCP server, so the AI agent's submissions meet the same standards regardless of upstream source.

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Appendix A1 — Metrics Glossary

KPI	Definition	Status
Touchless Resolution Rate	runs without operational HITL block ÷ total	partial (Post-Pilot)
Human Escalation Frequency	operational HITL blocks ÷ total	partial (Post-Pilot)
AI Decision Accuracy	decisions not followed by failure within 5min	partial (Post-Pilot)
MTTR (p95)	apply.failed → successful retry	grounded
Confidence-Gate Halt Rate	runs with band=block ÷ total	grounded
Provisioning Lead Time	run.completed − run.started	grounded
Deployment Frequency	count(run.completed) per day	grounded
Cost Savings (Infracost)	sum(delta_usd where delta < 0)	partial (CUR deferred)
FTE Hours Saved	run count × manual baseline × rate	derived (N=0 caveat)
Platform ROI	(labor + cloud + avoided downtime) ÷ op cost	derived (N=0 caveat)
Decision Ledger Coverage	decisions with outcome ÷ total	grounded
Attestation Coverage	prod/dr attested ÷ total prod/dr	grounded
Policy Compliance Rate	1 − failed_assets ÷ total	grounded

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Appendix A2 — Operating Model & Cost

• Cost figures from COST.md: $0.001883 over 8 days, ~$0.007/month, S3-dominated, zero BAU compute
• Zero-cost steady state: all resources torn down post-v1.11 (D-096); the platform runs offline
• References the pre-mortem (PRE_MORTEM.md: v1.10 decay root cause + structural mitigations)

Benefit: you now know the operating cost is negligible — and the structural mitigation that prevents decay.