--- marp: true theme: nova-sp paginate: true size: 16x9 footer: 'Nova — The Autonomous Cloud Delivery Platform' --- # Nova — The Autonomous Cloud Delivery Platform **Shifting from Operational Overhead to Strategic Value** Product Development & Citizen Developer Overview --- ## Slide 1 — The Problem **Product teams now own their cloud infrastructure — but ownership without discipline is destroying value.** - **No lifecycle planning.** Resources are authored for creation, not for patching, decommissioning, or rollback — so changes are destructive. - **Proactive scanning is not part of authoring.** AI-frontier models exploit zero-days at a rapid pace; teams cannot keep up by reacting. Modules must be scanned as code and at runtime — and remediated at the pace the threat moves. - **Bandwidth gaps in infrastructure operations.** Time spent on remediation + the push for innovation leaves operations chronically under-resourced; detections are missed, incidents grow. - **Tribal knowledge and the rockstar-operator problem.** Operations depend on a handful of administrators; when they leave, the knowledge leaves with them. The platform should encode the discipline, not the person. Every hour a developer spends writing, deploying, fixing, or remediating infrastructure is an hour not spent releasing features to production. **Benefit:** the answer is an autonomous cloud delivery platform that encodes discipline as policy, scans proactively, remediates rapidly, and makes operations visible to leadership rather than hidden in tribal knowledge. --- ## Slide 2 — Nova's Vision > **Infrastructure operations become visible. 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; the operator is never in the loop of normal operations.** - **Visibility is the recurring theme** — security posture, remediation velocity, reliability, and lead time as queryable signals - **Provable, not promised** — trust established by deterministic scripts that calculate a score; the platform functions without AI - **Autonomy in operations, human at stage gates** — QA signs off for production; SRE greenlights operational readiness **Benefit:** the destination is autonomous operations with provable trust — security, remediation velocity, reliability, and lead time made visible to leadership, not promised to them. --- ## Slide 3 — Strategic Objectives **4 Strategic Objectives:** 1. **Zero-touch operations** — autonomy as the default, not the demo; stage-gate attestation (QA, SRE) remains human by design 2. **Provable trust in automated decisions** — deterministic scripts calculate a score; the platform functions without AI; Decision Ledger, confidence scoring, circuit breakers, blast-radius controls 3. **Compounding, quantifiable ROI** — four CTO-grade metrics, all flowing into PowerBI: - **Lead Time** (PR → Production) · **Infrastructure Vulnerability Count** (trend) · **MTTR** · **Cloud Spend Reduction** 4. **Integrate with externally owned development platforms — regardless of source** — PDLC, SDLC, Agentic, or Citizen Developer; Nova provides skills + MCP endpoints; all prod intents go through the same controls and quality gates **Benefit:** the scope is explicit — Nova governs infrastructure and delivery, integrates with any upstream source through one validated contract, and measures success on four metrics a CTO can repeat back. --- ## Slide 4 — Anti-Goals (What Nova Is NOT) 1. Not a general-purpose AI agent platform 2. Not a system that removes humans from accountability — only from normal operations 3. Not an upstream development platform (no product backlogs, IDE, code authorship) 4. Not a replacement for the Product Development Lifecycle (PDLC) **Benefit:** the boundaries are explicit — Nova is purpose-built for infrastructure operations and delivery, not a general-purpose AI agent or an upstream development platform. --- ## Slide 5 — Scope: Downstream of PDLC **Nova governs infrastructure and delivery. The PDLC is upstream — Nova never penetrates it. Integration is through one validated contract.** - **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 enforcement → cloud resource lifecycle → environment progression (dev → qa → prod → dr) → immutable audit + attestation - **The integration point is one contract** — any upstream source (AI agent, agentic SDLC, dev platform) produces submissions subject to the same compliance standards - **Nova validates the submission, not the author** — the audit trail, the policy envelope, and the evidence stream are the same regardless of source **Benefit:** a clean scope boundary — Nova is purpose-built for infrastructure operations and integrates with any upstream source through one validated contract, so the platform team's surface area stays bounded. --- ## Slide 6 — RACI: Who Owns What **Four roles, one matrix — citizen developer owns FRs + UAT, platform owns NFRs + infra, quality engineering owns the gate evidence, SRE owns operational readiness.** | Work Category | Citizen Dev | Platform | Quality Eng | SRE | |---|---|---|---|---| | Functional Requirements | **R/A** | C | I | I | | User Acceptance Testing | **R/A** | C | I | I | | Non-Functional Requirements | I | **R/A** | C | C | | Infrastructure (cloud, state, IAM) | I | **R/A** | I | C | | QA (policy, confidence, schema) | C | R | **R/A** | I | | Production deployment to cloud | I | **R/A** | C | C | | Quality attestation (QA sign-off) | **A** | R | **R** | I | | Production readiness (SRE sign-off) | **A** | R | C | **R** | **R**=Responsible · **A**=Accountable (sign-off) · **C**=Consulted · **I**=Informed. Production readiness is co-owned: the platform runs attestations agentically; the citizen developer authorizes the promotion at the stage gate. **Benefit:** every party knows what they bring, what the platform provides, what quality engineering guards, and where SRE signs off — accountability is explicit, never diffuse. --- ## Slide 7 — The Platform Pipeline **How intent becomes verified infrastructure — fail-fast policy scanning before the plan, runtime scanning after it.** ![h:480 class:tall](assets/png/platform-pipeline.png) - **Contract → resolver → adapter → Checkov on static code (before plan) → terraform plan → Wiz on the plan → confidence signal → stage gate → apply → evidence + ledger** - **Fail-fast, quick feedback** — Checkov runs on the authored Terraform code before `terraform plan` so developers get immediate policy feedback - **Wiz on the plan when configured; Checkov as a drop-in otherwise** — Wiz scans the plan output; when Wiz credentials are absent, Checkov runs against the plan. **Wiz and Checkov are never both run on the plan.** **Benefit:** two layers of scanning, zero operator involvement in normal operations — fast deterministic feedback at authoring time and a runtime scan on the resolved plan. --- ## Slide 8 — The Decision Ledger **Every automated decision is captured, immutable, queryable — and accountable.** - **What is captured:** the chosen action, the confidence score, the alternatives considered, whether a human overrode it, and the outcome (backfilled once the apply completes). Every stage-gate attestation (QA, SRE) is captured with approver identity and the evidence presented. - **"AI decisions" are really automated decisions** — made by deterministic scripts that calculate a score and a band; the platform functions without AI. When an LLM planner is added later, it will emit richer alternatives without breaking the schema. - **The value is accountability, not the storage engine** — the ledger is append-only and tamper-evident; every decision is queryable for auditing, traceable to an outcome, and impossible to rewrite after the fact. **Benefit:** "autonomous" is defensible because every decision is immutable, queryable, and accountable — and the audience knows exactly what "automated" means here: deterministic scoring, not a black-box LLM. --- ## Slide 9 — Attestation Matrix: QA **The designed controls that keep humans at stage gates — QA concerns, freshness-validated.** | Concern | Env | Freshness | Description | |---------|-----|-----------|-------------| | Functional correctness | qa | 24h | The application behaves as specified; evidence accepted from the consumer's UAT. | | Performance baseline | qa | 7d | The deployment meets its performance envelope vs. the agreed baseline. | | Security posture | qa | 24h | The deployment's security findings have been reviewed and accepted. | **Benefit:** QA signs off on quality before any promotion — the gate is explicit, not implicit. --- ## Slide 10 — Attestation Matrix: Prod/DR **Production and DR controls — operational readiness, resilience, and disaster recovery.** | Concern | Env | Freshness | Description | |---------|-----|-----------|-------------| | Operational readiness | prod | 30d | SRE confirms the deployment is operable: runbooks, dashboards, on-call. | | Incident response | prod | 90d | The on-call path has been exercised; a working incident-response plan exists. | | Capacity & cost | prod | 30d | Capacity headroom and monthly cost are within the agreed envelope. | | Resilience: DR drill | prod | 180d | A DR drill has been run and recovery met the RTO. | | Resilience: chaos | prod | 90d | A chaos exercise has been run and the deployment absorbed the failure. | | Resilience: backup | prod | 30d | Backups are restorable and tested within the freshness window. | | DR region deploy | dr | 180d | The DR region can be deployed and is reachable. | Separation-of-duties on prod: the approver cannot be the same person who built the deployment. **Benefit:** the gate model is explicit — autonomy in operations, human in accountability, by design. The matrix is what makes autonomous operations safe enough to trust in production. --- ## Slide 11 — Telemetry & Live Ops **Every metric in this deck is traceable to a real emitted signal — the live-ops dashboard makes operations visible in PowerBI.** ![h:480 class:tall](assets/png/telemetry-live-ops.png) - **Platform components → CloudEvents envelope → event log + decision ledger + run records → collector → cold store → PowerBI views → live ops dashboard** - **The live ops dashboard (PowerBI)** surfaces the four CTO-grade metrics (Lead Time, Vulnerability Count, MTTR, Cloud Spend) alongside trust metrics (Decision Ledger coverage, Attestation coverage) and efficiency metrics (touchless resolution, escalation frequency) - **Every number is traceable to a signal** — when a CFO asks "where does this number come from?", the answer is a query against the cold store, not a Slack thread **Benefit:** the architecture is the trust substrate — leadership sees the same numbers the platform produces, in PowerBI, with full traceability. Operations become visible. --- ## Slide 12 — Decision Ledger + Attestation Coverage **By design, no change reaches production without a ledger entry and a human attestation — both queryable for auditing, with full traceability.** - **Decision Ledger coverage: 100%** — every platform run emits a decision record with outcome backfill; no automated decision is ever lost - **Attestation coverage: 100%** — every prod/dr promotion is attested by a human (QA for quality, SRE for production readiness), recorded with approver identity, separation-of-duties check, and the evidence matrix - **No change to production without both** — the ledger entry and the human attestation are mandatory, enforced by the pipeline, not by policy - **Full traceability** — a production change is traceable from the contract that declared intent, through the policy scan, the confidence score, the attestation, to the applied outcome **Benefit:** trust is provable — not a marketing claim, a queryable record. An auditor answers "who approved this, when, on what evidence?" in one query; a CTO answers "how many of last quarter's prod changes were touchless?" in one query. --- ## Slide 13 — Cost & ROI **The ROI formula and the cost estimates — grounded, with the production denominator honestly flagged.** - **Cost estimates are pre-apply and offline** — the platform reads the terraform plan and estimates cost before anything is applied; a cost regression is caught before the spend happens - **The ROI formula:** `Platform ROI = (FTE hours saved × blended rate + cloud savings + avoided downtime) ÷ platform op cost` - **The four CTO-grade metrics are the ROI proof:** Lead Time (PR → Prod), Infrastructure Vulnerability Count (trend), MTTR, Cloud Spend Reduction — all flow into PowerBI - **Honest caveat:** derived metrics are computed on internal runs today; the production-denominator activates when a pilot estate runs. The formula is grounded; the production numbers are not yet. **Benefit:** the ROI is not a black box — the formula is shown, the four metrics are committed, and the production-denominator caveat is stated up front. The CFO sees exactly what is real today and what activates with a pilot. --- ## Slide 14 — What's Deferred — and Why **Honesty about what is not measured yet — and the blocking work for each.** To be clear: these deferrals are *measurement infrastructure*, not the autonomy itself. The platform runs without an operator in the loop of normal operations. What is deferred is the evidence pipeline for certain metrics — not the autonomy. | # | Deferred metric | Blocking work | |---|-----------------|---------------| | 1 | Live infra health, outbox write rate, SLA | Live AWS re-provisioning (currently torn down to zero-cost steady state) | | 2 | Tamper-evident ledger checkpoints | Audit-ledger build-out (Object Lock + signed checkpoints) | | 3 | Onboarding funnel (requested → granted) | Auto-grant implementation | | 4 | Drift auto-reversal | Drift-detection scheduler (not yet built) | | 5 | Live cost reconciliation | Live AWS re-provisioning + actual-spend feed | | 6 | Predictive vs reactive ratio | ML anomaly-forecasting service (not yet built) | **Benefit:** the boundaries are explicit — what Nova measures today, and exactly what blocks the rest. The autonomy is real; the measurement gaps are documented with the work that unblocks each one. --- ## Slide 15 — Roadmap to the North Star **The path from the grounded metrics to the 12–18 month targets — each deferred metric has an unblock path and a timeframe.** | Timeframe | Work | Unblocks | |-----------|------|----------| | Near-term | Live AWS re-provisioning | Live infra health, outbox write rate, live cost reconciliation, SLA | | Near-term | Auto-grant implementation | Onboarding funnel (requested → granted) | | Mid-term | Drift-detection scheduler | Drift auto-reversal | | Mid-term | Audit-ledger build-out (Object Lock + signed checkpoints) | Tamper-evident ledger checkpoints | | Mid-term | Hot-path activation (batch → near-real-time) | Live-ops dashboard freshness | | Longer-term | ML anomaly-forecasting service | Predictive vs reactive ratio | Re-evaluation triggers: each blocking piece of work lifts on its own schedule; the metrics layer evolves as each one lands. **Benefit:** every deferred metric has an unblock path — nothing is hand-waved; everything has a plan and a timeframe. --- ## Slide 16 — 12-Month Product Roadmap **The product arc from pilot activation to integration — four quarters, four outcomes.** | Quarter | Theme | Board-level outcome | |---------|-------|---------------------| | **Q1** | Pilot Activation | Nova runs a real customer estate end-to-end, autonomously, with a measurable zero-touch rate. | | **Q2** | Provable Trust | Every automated decision lands in a tamper-evident ledger; the CFO sees real cloud-spend reconciliation. | | **Q3** | Compounding ROI | Quarter-over-quarter cloud spend drops; drift is detected and reversed without a human. | | **Q4** | Integration & Predictive | AI agents deploy through Nova by default; the ML anomaly-forecasting service goes live. | Grounded in the four strategic objectives (autonomy, provable trust, ROI, integration) and the deferred-metric unblock paths. **Benefit:** the 12-month product arc — each quarter activates a strategic objective and its corresponding board-level metric, from pilot activation through integration leadership. --- ## Slide 17 — Quarter-by-Quarter Outcomes | Quarter | Product theme | Key deliverable | Target metric | |---------|---------------|-----------------|---------------| | **Q1** | Pilot Activation | Re-provision live AWS; activate first pilot estate; onboarding auto-grant | Touchless ≥ 99% · Escalation < 0.1% · Accuracy ≥ 99.5% | | **Q2** | Provable Trust | Tamper-evident ledger (Object Lock + signed checkpoints); daily checkpoints; live cost reconciliation | Decision Ledger Coverage 100% · Cost Savings ≥ 25% | | **Q3** | Compounding ROI + Drift | Drift-detection scheduler; auto-reversal; pre-apply → actual-spend reconciliation on the pilot estate | Drift Auto-Reversal ≥ 95% · Spend Reduction ≥ 25% | | **Q4** | Integration + Predictive | ML anomaly-forecasting; AI-agent intent surface; multi-cloud (Azure/GCP) preview | Predictive:Reactive ≥ 3:1 · AI-Agent Intent Share (first measurement) | **Month-18 destination:** *"Nova is the layer enterprise leadership points to when they say 'we don't have an infrastructure ops team anymore, and the audit trail is stronger than it ever was.'"* **Benefit:** each quarter has a concrete deliverable, a target metric grounded in a strategic objective, and a path from "honestly deferred" to "shipped and measured." --- ## Slide 18 — Production-Grade Guidance via Atelier (1/2) **Nova instructs the citizen developer's AI agent on production-grade engineering — a set of skills and an MCP server.** - **Skills** — markdown files keyed to production-grade engineering domains (API, security, data, testing, observability, errors, DevOps, infrastructure-as-code, compliance); the skills extend the baseline catalog with Nova-specific production-grade principles - **MCP server** — a plugin-registry, stdio server exposing four tools: `lookup_principle`, `list_domains`, `matrix_lookup`, `validate_against_principles`. The developer's AI agent (or any agentic SDLC platform) calls these tools to look up the principles that apply to its submission - **The integration point is the same regardless of source** — whether the submission comes from an AI coding agent, an agentic SDLC platform, or a traditional IDE, the same skills and MCP server apply. This is how Nova makes the citizen developer production-grade without owning the PDLC **Benefit:** the citizen developer's AI agent is not unguided — Nova provides production-grade engineering principles as skills and as an MCP surface, so submissions arrive at the contract boundary already aligned with the platform's standards. --- ## Slide 19 — Production-Grade Guidance via Atelier (2/2) **Agentic validation catches engineering-discipline gaps that deterministic scanners miss — and the validation is reproducible.** - **Beyond deterministic scanners** — Wiz, Checkmarx, and Mend check policy and secrets; they do not check engineering discipline. The Atelier MCP server catches correctness, clarity, and observability gaps that deterministic tools cannot: "is this service observable?", "is this error path handled?", "is this API contract clear?" - **Agentic validation, not a second policy engine** — the MCP server gives the AI agent the principles to validate against; the agent does the validation. The agent reasons about the submission against the principles, not a second static scan - **Vendored for audit reproducibility** — Atelier is vendored at a pinned tag. A validation result is replayable against the exact principles that produced it, so an audit can reproduce a validation months later, not just trust a log line **Benefit:** the citizen developer's submission is checked for engineering discipline, not just policy compliance — and the check is reproducible for audit. That is what makes the submission production-grade, regardless of which upstream platform produced it. --- ## Slide 20 — Recap + Ask **The 4-beat recap + the business decision.** **Recap:** - **Problem:** product teams own infrastructure without the discipline and lifecycle planning it requires; bandwidth gaps and tribal knowledge leave operations exposed - **Solution:** autonomous cloud delivery — operations become visible, trust is provable (deterministic scoring), humans at stage gates - **Proof:** 100% ledger coverage, 100% attestation coverage, grounded ROI formula, four CTO-grade metrics flowing into PowerBI - **Roadmap:** deferred metrics have unblock paths; the 12-month product arc activates one strategic objective per quarter **The ask:** "Approve a pilot estate to activate the production-denominator metrics (Lead Time, Vulnerability Count, MTTR, Cloud Spend), and approve the tamper-evident ledger build-out to move from the local hash-chain to S3 Object Lock + signed checkpoints. These two decisions move Nova from 'pipeline-ready' to 'production-proven.'" **Benefit:** a clear business decision — approve a pilot and the ledger build-out — with the confidence that every claim in this deck is grounded, derived, or honestly deferred. --- ## Appendix A1 — Metrics Glossary | KPI | Definition | Status | |-----|-----------|--------| | Touchless Resolution Rate | runs without operational stage-gate block ÷ total | partial (Post-Pilot) | | Human Escalation Frequency | operational stage-gate blocks ÷ total | partial (Post-Pilot) | | Automated 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 (pre-apply) | sum(delta_usd where delta < 0) | partial (live reconciliation 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 | **Benefit:** a reference for every metric mentioned in the deck.