Below is a cohesive set of **7 product advisories** that together define an “AI-native” Stella Ops with defensible moats. Each advisory follows the same structure:

* **Problem** (what hurts today)
* **Why** (why Stella should solve it)
* **What we ship** (capabilities, boundaries)
* **How we achieve** (proposed `AdvisoryAI` backend modules + key UI components)
* **Guardrails** (safety / trust / determinism)
* **KPIs** (how you prove it works)

I’m assuming your canonical object model already includes **Runs** (incident/escalation/change investigation runs) and a system-of-record in **PostgreSQL** with **Valkey** as a non-authoritative accelerator.

---

# ADVISORY-AI-000 — AdvisoryAI Foundation: Chat + Workbench + Runs (the “AI OS surface”)

## Problem

Most “AI in ops” fails because it’s only a chat box. Chat is not:

* auditable
* repeatable
* actionable with guardrails
* collaborative (handoffs, approvals, artifacts)

Operators need a place where AI output becomes **objects** (runs, decisions, patches, evidence packs), not ephemeral text.

## Why we do it

This advisory is the substrate for all other moats. Without it, your other features remain demos.

## What we ship

1. **AdvisoryAI Orchestrator** that can:

* read Stella objects (runs, services, policies, evidence)
* propose plans
* call tools/actions (within policy)
* produce structured artifacts (patches, decision records, evidence packs)

2. **AI Workbench UI**:

* Chat panel for intent
* Artifact cards (Run, Playbook Patch, Decision, Evidence Pack)
* Run Timeline view (what happened, tool calls, approvals, outputs)

## How we achieve (modules + UI)

### Backend modules (suggested)

* `StellaOps.AdvisoryAI.WebService`

  * Conversation/session orchestration
  * Tool routing + action execution requests
  * Artifact creation (Run notes, patches, decisions)
* `StellaOps.AdvisoryAI.Prompting`

  * Prompt templates versioned + hashed
  * Guarded system prompts per “mode”
* `StellaOps.AdvisoryAI.Tools`

  * Tool contracts (read-only queries, action requests)
* `StellaOps.AdvisoryAI.Eval`

  * Regression tests for tool correctness + safety

### UI components

* `AiChatPanelComponent`
* `AiArtifactCardComponent` (Run/Decision/Patch/Evidence Pack)
* `RunTimelineComponent` (with “AI steps” and “human steps”)
* `ModeSelectorComponent` (Analyst / Operator / Autopilot)

### Canonical flow

```
User intent (chat) 
  -> AdvisoryAI proposes plan (steps)
  -> executes read-only tools
  -> generates artifact(s)
  -> requests approvals for risky actions
  -> records everything on Run timeline
```

## Guardrails

* Every AI interaction writes to a **Run** (or attaches to an existing Run).
* Prompt templates are **versioned + hashed**.
* Tool calls and outputs are **persisted** (for audit and replay).

## KPIs

* % AI sessions attached to Runs
* “Time to first useful artifact”
* Operator adoption (weekly active users of Workbench)

---

# ADVISORY-AI-001 — Evidence-First Outputs (trust-by-construction)

## Problem

In ops, an answer without evidence is a liability. LLMs are persuasive even when wrong. Operators waste time verifying or, worse, act on incorrect claims.

## Why we do it

Evidence-first output is the trust prerequisite for:

* automation
* playbook learning
* org memory
* executive reporting

## What we ship

* A **Claim → Evidence** constraint:

  * Each material claim must be backed by an `EvidenceRef` (query snapshot, ticket, pipeline run, commit, config state).
* An **Evidence Pack** artifact:

  * A shareable bundle of evidence for an incident/change/review.

## How we achieve (modules + UI)

### Backend modules

* `StellaOps.AdvisoryAI.Evidence`

  * Claim extraction from model output
  * Evidence retrieval + snapshotting
  * Citation enforcement (or downgrade claim confidence)
* `StellaOps.EvidenceStore`

  * Immutable (or content-addressed) snapshots
  * Hashes, timestamps, query parameters

### UI components

* `EvidenceSidePanelComponent` (opens from inline citations)
* `EvidencePackViewerComponent`
* `ConfidenceBadgeComponent` (Verified / Inferred / Unknown)

### Implementation pattern

* For each answer:

  1. Draft response
  2. Extract claims
  3. Attach evidence refs
  4. If evidence missing: label as uncertain + propose verification steps

## Guardrails

* If evidence is missing, Stella must **not** assert certainty.
* Evidence snapshots must capture:

  * query inputs
  * time range
  * raw result (or hash + storage pointer)

## KPIs

* Citation coverage (% of answers with evidence refs)
* Reduced back-and-forth (“how do you know?” rate)
* Adoption of automation after evidence-first rollout

---

# ADVISORY-AI-002 — Policy-Aware Automation (safe actions, not just suggestions)

## Problem

The main blocker to “AI that acts” is governance:

* wrong environment
* insufficient permission
* missing approvals
* non-idempotent actions
* unclear accountability

## Why we do it

If Stella can’t safely execute actions, it will remain a read-only assistant. Policy-aware automation is a hard moat because it requires real engineering discipline and operational maturity.

## What we ship

* A typed **Action Registry**:

  * schemas, risk levels, idempotency, rollback/compensation
* A **Policy decision point** (PDP) before any action:

  * allow / allow-with-approvals / deny
* An **Approval workflow** linked to Runs

## How we achieve (modules + UI)

### Backend modules

* `StellaOps.ActionRegistry`

  * Action definitions + schemas + risk metadata
* `StellaOps.PolicyEngine`

  * Rules: environment protections, freeze windows, role constraints
* `StellaOps.AdvisoryAI.Automation`

  * Converts intent → action proposals
  * Submits action requests after approvals
* `StellaOps.RunLedger`

  * Every action request + result is a ledger entry

### UI components

* `ActionProposalCardComponent`
* `ApprovalModalComponent` (scoped approval: this action/this run/this window)
* `PolicyExplanationComponent` (human-readable “why allowed/denied”)
* `RollbackPanelComponent`

## Guardrails

* Default: propose actions; only auto-execute in explicitly configured “Autopilot scopes.”
* Every action must support:

  * idempotency key
  * audit fields (why, ticket/run linkage)
  * reversible/compensating action where feasible

## KPIs

* % actions proposed vs executed
* “Policy prevented incident” count
* Approval latency and action success rate

---

# ADVISORY-AI-003 — Ops Memory (structured, durable, queryable)

## Problem

Teams repeat incidents because knowledge lives in:

* chat logs
* tribal memory
* scattered tickets
* unwritten heuristics

Chat history is not an operational knowledge base: it’s unstructured and hard to reuse safely.

## Why we do it

Ops memory reduces repeat work and accelerates diagnosis. It also becomes a defensible dataset because it’s tied to your Runs, artifacts, and outcomes.

## What we ship

A set of typed memory objects (not messages):

* `DecisionRecord`
* `KnownIssue`
* `Tactic`
* `Constraint`
* `PostmortemSummary`

Memory is written on:

* Run closure
* approvals (policy events)
* explicit “save as org memory” actions

## How we achieve (modules + UI)

### Backend modules

* `StellaOps.AdvisoryAI.Memory`

  * Write: extract structured memory from run artifacts
  * Read: retrieve memory relevant to current context (service/env/symptoms)
  * Conflict handling: “superseded by”, timestamps, confidence
* `StellaOps.MemoryStore` (Postgres tables + full-text index as needed)

### UI components

* `MemoryPanelComponent` (contextual suggestions during a run)
* `MemoryBrowserComponent` (search + filters)
* `MemoryDiffComponent` (when superseding prior memory)

## Guardrails

* Memory entries have:

  * scope (service/env/team)
  * confidence (verified vs anecdotal)
  * review/expiry policies for tactics/constraints
* Never “learn” from unresolved or low-confidence runs by default.

## KPIs

* Repeat incident rate reduction
* Time-to-diagnosis delta when memory exists
* Memory reuse rate inside Runs

---

# ADVISORY-AI-004 — Playbook Learning (Run → Patch → Approved Playbook)

## Problem

Runbooks/playbooks drift. Operators improvise. The playbook never improves, and the organization pays the same “tuition” repeatedly.

## Why we do it

Playbook learning is the compounding loop that turns daily operations into a proprietary advantage. Competitors can generate playbooks; they struggle to continuously improve them from real run traces with review + governance.

## What we ship

* Versioned playbooks as structured objects
* **Playbook Patch** proposals generated from Run traces:

  * coverage patches, repair patches, optimization patches, safety patches, detection patches
* Owner review + approval workflow

## How we achieve (modules + UI)

### Backend modules

* `StellaOps.Playbooks`

  * Playbook schema + versioning
* `StellaOps.AdvisoryAI.PlaybookLearning`

  * Extract “what we did” from Run timeline
  * Compare to playbook steps
  * Propose a patch with evidence links
* `StellaOps.DiffService`

  * Human-friendly diff output for UI

### UI components

* `PlaybookPatchCardComponent`
* `DiffViewerComponent` (Monaco diff or equivalent)
* `PlaybookApprovalFlowComponent`
* `PlaybookCoverageHeatmapComponent` (optional, later)

## Guardrails

* Never auto-edit canonical playbooks; only patches + review.
* Require evidence links for each proposed step.
* Prevent one-off contamination by marking patches as:

  * “generalizable” vs “context-specific”

## KPIs

* % incidents with a playbook
* Patch acceptance rate
* MTTR improvement for playbook-backed incidents

---

# ADVISORY-AI-005 — Integration Concierge (setup + health + “how-to” that is actually correct)

## Problem

Integrations are where tools die:

* users ask “how do I integrate X”
* assistant answers generically
* setup fails because of environment constraints, permissions, webhooks, scopes, retries, or missing prerequisites
* no one can debug it later

## Why we do it

Integration handling becomes a moat when it is:

* deterministic (wizard truth)
* auditable (events + actions traced)
* self-healing (retries, backfills, health checks)
* explainable (precise steps, not generic docs)

## What we ship

1. **Integration Setup Wizard** per provider (GitLab, Jira, Slack, etc.)
2. **Integration Health** dashboard:

* last event received
* last action executed
* failure reasons + next steps
* token expiry warnings

3. **Chat-driven guidance** that drives the same wizard backend:

* when user asks “how to integrate GitLab,” Stella replies with the exact steps for the instance type, auth mode, and required permissions, and can pre-fill a setup plan.

## How we achieve (modules + UI)

### Backend modules

* `StellaOps.Integrations`

  * Provider contracts: inbound events + outbound actions
  * Normalization into Stella `Signals` and `Actions`
* `StellaOps.Integrations.Reliability`

  * Webhook dedupe, replay, dead-letter, backfill polling
* `StellaOps.AdvisoryAI.Integrations`

  * Retrieves provider-specific setup templates
  * Asks only for missing parameters
  * Produces a “setup checklist” artifact attached to a Run or Integration record

### UI components

* `IntegrationWizardComponent`
* `IntegrationHealthComponent`
* `IntegrationEventLogComponent` (raw payload headers + body stored securely)
* `SetupChecklistArtifactComponent` (generated by AdvisoryAI)

## Guardrails

* Store inbound webhook payloads for replay/debug, with redaction where required.
* Always support reconciliation/backfill (webhooks are never perfectly lossless).
* Use least-privilege token scopes by default, with clear permission error guidance.

## KPIs

* Time-to-first-successful-event
* Integration “healthy” uptime
* Setup completion rate without human support

---

# ADVISORY-AI-006 — Outcome Analytics (prove ROI with credible attribution)

## Problem

AI features are easy to cut in budgeting because value is vague. “It feels faster” doesn’t survive scrutiny.

## Why we do it

Outcome analytics makes Stella defensible to leadership and helps prioritize what to automate next. It also becomes a dataset for continuous improvement.

## What we ship

* Baseline metrics (before Stella influence):

  * MTTA, MTTR, escalation count, repeat incidents, deploy failure rate (as relevant)
* Attribution model (only count impact when Stella materially contributed):

  * playbook patch accepted
  * evidence pack used
  * policy-gated action executed
  * memory entry reused
* Monthly/weekly impact reports

## How we achieve (modules + UI)

### Backend modules

* `StellaOps.Analytics`

  * Metric computation + cohorts (by service/team/severity)
* `StellaOps.AdvisoryAI.Attribution`

  * Joins outcomes to AI artifacts and actions in the Run ledger
* `StellaOps.Reporting`

  * Scheduled report generation (exportable)

### UI components

* `OutcomeDashboardComponent`
* `AttributionBreakdownComponent`
* `ExecutiveReportExportComponent`

## Guardrails

* Avoid vanity metrics (“number of chats”).
* Always show confidence/limitations in attribution (correlation vs causation).

## KPIs

* MTTR delta (with Stella artifacts vs without)
* Repeat incident reduction
* Escalation reduction
* Automation coverage growth

---

## One unifying implementation note: “AdvisoryAI” should output objects, not prose

To make all seven advisories work together, standardize on a small set of **AI-produced artifacts**:

* `Plan` (step list with tools/actions)
* `EvidencePack`
* `DecisionRecord`
* `PlaybookPatch`
* `IntegrationSetupChecklist`
* `RunSummary` (postmortem-ready)

Every artifact is:

* versioned
* evidence-linked
* attached to a Run
* subject to policy gates when it triggers actions

This gives you:

* auditability
* deterministic replay of the *inputs and tool outputs*
* compounding “Ops memory” and “Playbook learning” data

---

## Example: how this maps to “AdvisoryAI module + Chat UI component”

Minimum viable architecture that is coherent:

**Backend**

* `StellaOps.AdvisoryAI.WebService` (orchestrator)
* `StellaOps.AdvisoryAI.Evidence` (citations)
* `StellaOps.AdvisoryAI.Memory` (structured memory)
* `StellaOps.AdvisoryAI.PlaybookLearning` (patch proposals)
* `StellaOps.AdvisoryAI.Integrations` (setup guidance + checklists)
* `StellaOps.PolicyEngine` + `StellaOps.ActionRegistry` (gated actions)
* `StellaOps.RunLedger` + `StellaOps.EvidenceStore` (audit + snapshots)

**UI**

* AI chat panel that emits/accepts **artifact cards**
* Evidence side panel
* Approval modal
* Diff viewer for patches
* Integration wizard + health

---

If you want, I can translate these advisories into a single internal spec pack:

* database tables (Postgres)
* event contracts (signals/actions)
* JSON schemas for artifacts/actions
* UI navigation and component tree
* the first 10 “golden workflows” you should ship with the Workbench