diff --git a/aws-managed-definitions/README.md b/aws-managed-definitions/README.md index 6b0eb47..dbfbad0 100644 --- a/aws-managed-definitions/README.md +++ b/aws-managed-definitions/README.md @@ -16,8 +16,8 @@ Out-of-box transformation definitions provided by AWS for common migration and u | General | [Codebase Analysis](comprehensive-codebase-analysis/codebase-analysis.md) | Static analysis and documentation for migration planning | | Readiness | [Modernization Readiness Analysis](readiness-analysis/modernization-readiness-analysis.md) | Scans portfolios for cloud-native maturity gaps and maps findings to AWS modernization pathways | | Readiness | [Agentic Readiness Analysis](readiness-analysis/agentic-readiness-analysis.md) | Evaluates whether systems are ready to be safely called by AI agents — covering APIs, identity, state management, human-in-the-loop, and observability | -| Readiness | [Portfolio Modernization Readiness](readiness-analysis/portfolio-modernization-readiness.md) | Aggregates per-repo MOD reports into portfolio-level roadmap and cross-cutting analysis | -| Readiness | [Portfolio Agentic Readiness](readiness-analysis/portfolio-agentic-readiness.md) | Aggregates per-repo ARA reports into portfolio-level cross-cutting analysis | +| Readiness | [Portfolio Modernization Readiness](readiness-analysis/portfolio-modernization-readiness-analysis.md) | Aggregates per-repo MOD reports into portfolio-level roadmap and cross-cutting analysis | +| Readiness | [Portfolio Agentic Readiness](readiness-analysis/portfolio-agentic-readiness-analysis.md) | Aggregates per-repo ARA reports into portfolio-level cross-cutting analysis | ## Directory Structure @@ -44,6 +44,8 @@ aws-managed-definitions/ └── readiness-analysis/ ├── modernization-readiness-analysis.md ├── agentic-readiness-analysis.md - ├── portfolio-modernization-readiness.md - └── portfolio-agentic-readiness.md + ├── portfolio-modernization-readiness-analysis.md + ├── portfolio-agentic-readiness-analysis.md + └── references/ + └── program-library.md # AWS Program & GTM catalog; loaded by the two portfolio TDs ``` diff --git a/aws-managed-definitions/readiness-analysis/agentic-readiness-analysis.md b/aws-managed-definitions/readiness-analysis/agentic-readiness-analysis.md index b7ceba3..015806a 100644 --- a/aws-managed-definitions/readiness-analysis/agentic-readiness-analysis.md +++ b/aws-managed-definitions/readiness-analysis/agentic-readiness-analysis.md @@ -1889,6 +1889,15 @@ The complete report structure, for reference: ``` +### Next Steps — Engagement Programs (Portfolio-Level) + +This per-repo report intentionally does NOT recommend AWS engagement programs (MAP, EBA, AppMod, AI Assessment, SHIP, ACP, etc.). Program eligibility depends on cross-repo scope, customer segment, ARR, and partner context that a single-repo analysis does not have. + +Engagement-level program recommendations are produced by the **Portfolio Agentic Readiness Analysis**, which aggregates all per-repo ARA reports and evaluates them against the shared AWS Program & GTM Library. Run the portfolio TD after all per-repo reports are generated. + +Code-level remediation for this repo is captured in the BLOCKERs, RISKs, and INFO sections above. + + ## Constraints and Guardrails Strictly follow these rules at all times: @@ -2200,8 +2209,8 @@ Ordered by severity (High → Medium → Low) then by section order (AUTH → AP Source: `remediation_roadmap.items[]` **Recommended Actions:** -- Custom Transformation row (always present) -- AWS Programs subsection: EBA, MAP +- AWS Transform Custom row (always present) — expand analysis to additional repositories +- Engagement-program recommendations (MAP, EBA, AppMod, etc.) are NOT produced per-repo; they come from the Portfolio Agentic Readiness Analysis. If no portfolio analysis is available, this subsection shows only the AWS Transform Custom row plus a pointer to run the portfolio TD. **Agent Deployment Recommendation** (footer block): diff --git a/aws-managed-definitions/readiness-analysis/modernization-readiness-analysis.md b/aws-managed-definitions/readiness-analysis/modernization-readiness-analysis.md index 5c561f9..c0c7688 100644 --- a/aws-managed-definitions/readiness-analysis/modernization-readiness-analysis.md +++ b/aws-managed-definitions/readiness-analysis/modernization-readiness-analysis.md @@ -1719,6 +1719,14 @@ The evidence index compiles all file references cited across the detailed findin Include every file path that appears in any finding's evidence field. Group by directory for readability if the list exceeds 20 entries. +### Next Steps — Engagement Programs (Portfolio-Level) + +This per-repo report intentionally does NOT recommend AWS engagement programs (MAP, OLA, AppMod, EBA, VMCCO, etc.). Program eligibility depends on cross-repo scope, customer segment, ARR, on-prem/migration context, and partner relationships that a single-repo analysis does not have. + +Engagement-level program recommendations are produced by the **Portfolio Modernization Readiness Analysis**, which aggregates all per-repo MOD reports and evaluates them against the shared AWS Program & GTM Library. Run the portfolio TD after all per-repo reports are generated. + +Modernization guidance for this repo is captured in the pathway detail subsections, top gaps, and detailed findings above. + ## Constraints and Guardrails @@ -2184,6 +2192,8 @@ The full visual contract is defined inline below — do NOT reference external f - Modernization Recommendation footer block (emoji-headlined with top-3 High-severity recommendations). - Footer line (`Generated by AWS Transform · Modernization Readiness Analysis Report`). +> **Note on the "programs" tab / Recommended Actions:** In the per-repo MOD report these render modernization pathways (Move to X) and pathway-mapped learning materials only — NOT AWS engagement programs (MAP, OLA, AppMod, etc.). Engagement-program recommendations are produced exclusively by the Portfolio Modernization Readiness Analysis from the shared AWS Program & GTM Library. The per-repo `recommended_actions` content is limited to AWS Transform Custom (expand-analysis) plus a pointer to run the portfolio TD. + **HTML-escaping discipline.** Every data value rendered in HTML originates from the JSON artifact (MD prose is NOT part of the HTML round-trip contract). All attacker-controlled strings MUST be HTML-escaped before embedding: repo names, evidence file paths, finding titles, finding descriptions, recommendation text, pathway names, and any other string that originates from repository content or from free-text fields in `additionalPlanContext`. Escape `<`, `>`, `&`, `"`, `'` at render time. This is the same escaping discipline applied to the ARA HTML artifact. #### Slug Derivation diff --git a/aws-managed-definitions/readiness-analysis/portfolio-agentic-readiness-analysis.md b/aws-managed-definitions/readiness-analysis/portfolio-agentic-readiness-analysis.md index ab4a8c7..b980007 100644 --- a/aws-managed-definitions/readiness-analysis/portfolio-agentic-readiness-analysis.md +++ b/aws-managed-definitions/readiness-analysis/portfolio-agentic-readiness-analysis.md @@ -19,7 +19,7 @@ The transformation follows a 9-step pipeline: 6. **Cross-Cutting RISKs** (Step 4b): Identify RISKs meeting the scaling threshold (max(3, 33% of applicable repos)), split by RISK-SAFETY and RISK-QUALITY tiers 7. **Dependency Mapping** (Step 5): Construct service dependency map from dependency_overrides 8. **Remediation Guidance** (Step 6): Generate portfolio-level remediation for cross-cutting BLOCKERs -9. **Agentic Programs** (Step 7): Recommend AI DLC, AXE, Innovation EBA where triggered +9. **Agentic Programs** (Step 7): Recommend AI DLC, AXE, Innovation EBA, and programs from the shared AWS Program & GTM Library (`references/program-library.md`) where triggered 10. **Portfolio-Level Questions** (Step 8): Evaluate PORT-ARA-Q1 through PORT-ARA-Q5 — capabilities only visible across multiple repos The output is a **four-artifact bundle** containing: @@ -36,7 +36,7 @@ The MD report contains: - Cross-cutting RISKs (same risk question appearing in max(3, 33% of applicable repos)) - Service dependency map from dependency_overrides - Portfolio-level remediation guidance for cross-cutting blockers -- Agentic program recommendations (AI DLC, AXE, Innovation EBA) +- Agentic program recommendations (AI DLC, AXE, Innovation EBA, plus relevant AWS Program & GTM Library programs) - Service-by-service summary (repo name, profile, blocker count, risk count) This portfolio TD focuses exclusively on cross-cutting BLOCKER/RISK identification across multiple ARA reports. It does not include modernization pathways, roadmap phases, numeric scores, technology preferences, or resource allocation recommendations. @@ -547,39 +547,33 @@ If `context` was provided in additionalPlanContext, use it to tailor the remedia ### Step 7: Recommend Agentic Programs -Based on the portfolio-wide analysis findings, recommend relevant agentic enablement programs and engagement workshops. +Based on the portfolio-wide analysis findings, recommend relevant agentic enablement programs and AWS GTM programs from the shared **AWS Program & GTM Library** in `references/program-library.md`. Load that file when evaluating recommendations — it is the authoritative catalog and contains the full agent instructions (signal patterns, exclusions, qualification criteria, prioritization, grouping, status filtering, and a reasoning checklist), including the three ARA agentic anchor programs (AI DLC, AXE, Innovation EBA). Include a program only if its trigger condition is met. If no programs are triggered, include the brief note in 7.2 instead. -#### 7.1 Program Catalog and Trigger Logic +#### 7.1 Evaluating the Library from ARA Findings -Evaluate each program against its trigger condition. Include a program in the recommendations only if its trigger condition is met. Multiple programs can be triggered simultaneously — they serve different purposes and are not mutually exclusive. +Use the `[ARA-anchor]`, `[ARA]`, and `[ARA+MOD]`-tagged programs in the library and map ARA's vocabulary as described in the library's "Mapping findings to triggers" section: -| Program | Description | Trigger Condition | How to Evaluate | -|---------|-------------|-------------------|-----------------| -| **AI DLC (AI Driven Development Lifecycle)** | Workshop for adopting the AI Driven Development Lifecycle, emphasizing two dimensions: (1) AI Powered Execution with Human Oversight — AI creates detailed work plans, seeks clarification, and defers critical decisions to humans who possess contextual understanding and business knowledge; (2) Dynamic Team Collaboration — as AI handles routine tasks, teams unite in collaborative spaces for real-time problem solving, creative thinking, and rapid decision-making, shifting from isolated work to high-energy teamwork that accelerates innovation and delivery. | Portfolio shows teams without established AI-assisted development practices, or when engineering maturity findings indicate manual development workflows that could benefit from AI-driven automation. | Check ENG section findings across the portfolio. If 50%+ of services have RISK-QUALITY or worse findings on ENG-Q1 (Infra Governance), ENG-Q2 (CI/CD + Contracts), or ENG-Q3 (Rollback), recommend AI DLC. Also recommend if the portfolio context mentions desire for AI-assisted development practices. | -| **AXE (Agent Experience Engagement)** | A strategic methodology that helps enterprises implement agentic AI solutions by starting with desired customer and employee experience and working backwards to define AI agents and technical architecture. Built on the proven D2E methodology with 580+ successful engagements, AXE delivers a six-phase framework covering business process mapping, task identification, evaluation metrics, data architecture, governance, and guardrails. The Guardrails & Boundaries phase aligns with ARA, which evaluates whether target systems have the technical controls needed to safely support autonomous agents. Together, they provide a complete assess-to-implement pathway: ARA validates system readiness while AXE designs the agent experience and implementation roadmap. | Portfolio shows 3+ services in "Pilot-Ready" or "Agent-Ready" state, or when business has defined customer/employee experience goals but lacks technical implementation roadmap. | Count services with profile Agent-Ready or Pilot-Ready. If count >= 3, recommend AXE. Also recommend if the portfolio `context` describes experience-level goals (e.g., "customer support agent", "employee productivity") without a corresponding technical implementation plan. | -| **Innovation EBA** (AIML-GenAI) | A 3-day sprint-based, interactive engagement that enables customers to build AI/ML models or deliver a Generative AI use case in an accelerated fashion using AWS services and prescriptive guidance. Targets customers with higher cloud maturity who want to leverage AI/ML to solve business problems and innovate faster. Follows the 4-step EBA framework (Executive Alignment → Readiness → Accelerate → Transform At Scale) with workstreams including Foundations, Data Engineering, GenAI Build/Evaluate, UI Integration, ML Ops, and Command Center. Develops customer skills through learning-by-doing and builds/accelerates a GenAI use case pipeline based on a blueprint developed during the EBA. | Portfolio context indicates AI/ML or GenAI is a strategic imperative, executive sponsorship exists, use cases deliver critical business value, data strategy exists, and the customer is committed to production deployment within ~90 days. Additionally, the portfolio should show a backlog of AIML-GenAI use cases and the customer team has some AIML-GenAI skills committed to upskilling. | Check portfolio `context` for signals of: (1) AI/ML or GenAI as strategic priority, (2) executive sponsorship mentioned, (3) existing data and data strategy (e.g., services with established data pipelines, DynamoDB/Aurora/S3 data stores), (4) use cases in categories like customer experience (chatbots, post-call analytics, personalization), productivity (intelligent search, summarization, code generation), business operations (IDP, fraud detection, predictive maintenance), or content creation. Also recommend if 3+ services have production-ready data stores AND the portfolio context describes GenAI ambitions beyond what AXE alone covers. | +- **Agentic anchor programs** (AI DLC, AXE, Innovation EBA) — defined in the library's "Agentic Enablement Programs (ARA Anchors)" section with their own signal patterns and evaluation logic. These are ARA-only and group under Engagement Models. +- **"3+ High-severity findings across any dimension"** → count unified `High` findings (cross-cutting BLOCKERs from Step 4, plus conditional BLOCKERs resolved as High) across the portfolio. +- **Readiness profiles** → use the exact ARA profile names: `Agent-Ready`, `Pilot-Ready`, `Pilot-Ready (Safety Concerns)`, `Remediation Required`, `Not Agent-Integrable`. Use the profile distribution from the executive dashboard (Step 3). +- **A dimension "showing problems"** → that ARA category has 2+ `Medium`/`High` findings (e.g., Well-Architected Review when `Engineering Maturity` or `Observability` has 2+ Medium+ findings). +- **Customer segment** (Enterprise / SMB / ISV / Startup / WWPS) → infer from the portfolio `context` and `service_inventory`. -#### 7.2 Program Sequencing Guidance +Apply the library's selection rules: cap recommendations at **3–5**, prioritize by direct finding match → segment fit → entry-point-before-follow-on, group as **Funded Programs → Engagement Models**, sequence logically (Assessment → Funding → Execution → Optimization, and for the anchor programs AI DLC → AXE → Innovation EBA), and never recommend `Retiring` or not-yet-imminent `Launching` programs. Run the library's reasoning checklist before finalizing. -When multiple programs are triggered, recommend them in this order: - -1. **AI DLC** (if triggered) — Run first to establish AI-driven development practices before agentic work -2. **AXE** (if triggered) — Run after AI DLC to design the agent experience -3. **Innovation EBA** (if triggered) — Run when the customer is ready to accelerate an AI/ML or GenAI use case into production (can run in parallel with AXE if use cases are independent) - -If only one program is triggered, recommend it directly without sequencing context. - -#### 7.3 Program Recommendations Output +#### 7.2 Program Recommendations Output For each triggered program: -- **Program name** — AI DLC, AXE, or Innovation EBA +- **Program name** — e.g., AI DLC, AXE, Innovation EBA, or any library program (MAP, AI Assessment, ACP, etc.) - **Relevance** — Why this program is recommended based on portfolio findings -- **Trigger findings** — Specific portfolio metrics that triggered the recommendation +- **Trigger findings** — Specific portfolio metrics that triggered the recommendation (using ARA profile/severity language) - **What it provides** — Brief description of the program's value - **Suggested timing** — When to run relative to other programs or analysis phases - **Next step** — Recommended action (e.g., "Request engagement via AWS Solutions Architect") +Group the rendered output under **Funded Programs → Engagement Models** (the three agentic anchor programs are Engagement Models). Cap at 3–5 total. Do not expose any internal numeric maturity score. + If no programs are triggered, include a brief note: "No specific agentic program recommendations based on current findings. As the portfolio's agentic readiness improves, re-assess to identify program eligibility." @@ -910,11 +904,21 @@ Group related BLOCKERs that can be addressed together.> > These are engagement-level recommendations based on the portfolio's agentic readiness > profile. Discuss with your AWS Solutions Architect to determine eligibility and timing. +> Recommendations are capped at 3–5 and grouped by type. (No internal maturity scores are exposed.) + +#### Funded Programs | Program | Relevance | Trigger Findings | Suggested Timing | Next Step | |---------|-----------|-----------------|------------------|-----------| | | | | | | -| | + +#### Engagement Models + +| Program | Relevance | Trigger Findings | Suggested Timing | Next Step | +|---------|-----------|-----------------|------------------|-----------| +| | | | | | + + ### Program Details @@ -1102,7 +1106,7 @@ The Portfolio ARA JSON artifact MUST emit these top-level keys in the order show | `findings[]` | Per-repo findings propagated up. Each entry is a 12-field per-repo finding plus `repo_name`. One entry per (repo × question_id). Used by webapp Findings tab. | | `cross_cutting_findings[]` | Portfolio-aggregated findings where the same question_id fires at the same tier across 2+ repos (BLOCKER) or meets the scaling threshold (RISK, max(3, 33% of applicable repos)). One entry per question_id. Used by webapp Cross-Cutting view. | | `remediation_roadmap` | See §"Remediation Roadmap" below | -| `recommended_actions[]` | Canonical agentic programs (AI DLC, AXE, Innovation EBA) | +| `recommended_actions[]` | Agentic anchor programs (AI DLC, AXE, Innovation EBA) plus triggered AWS Program & GTM Library programs, grouped Funded → Engagement | | `portfolio_level_findings[]` | PORT-ARA-Q* cross-portfolio findings | | `dependency_map` | Dependency map | @@ -1246,13 +1250,15 @@ The ARA execution-sequencing narrative (Phase 1 BLOCKER resolution, Phase 2 RISK ### Recommended Actions -The Portfolio ARA JSON emits `recommended_actions[]` as an array of agentic-program entries. Minimum-set coverage: +The Portfolio ARA JSON emits `recommended_actions[]` as an array of program entries. Entries come from two sources: the three ARA-specific agentic anchor programs, and triggered programs from the AWS Program & GTM Library (`references/program-library.md`). The anchor programs always appear (with their resolved `status`); library programs appear only when triggered. + +Anchor program coverage: -| `id` | `name` | `acronym` | `type` | -|---|---|---|---| -| `ai-dlc` | AI Driven Development Lifecycle | AI DLC | workshop | -| `axe` | Agent Experience Engagement | AXE | program | -| `innovation-eba` | Innovation EBA | Innovation EBA | program | +| `id` | `name` | `acronym` | `type` | `group` | +|---|---|---|---|---| +| `ai-dlc` | AI Driven Development Lifecycle | AI DLC | workshop | Engagement Models | +| `axe` | Agent Experience Engagement | AXE | program | Engagement Models | +| `innovation-eba` | Innovation EBA | Innovation EBA | program | Engagement Models | Each entry carries: @@ -1262,6 +1268,7 @@ Each entry carries: "name": "Agent Experience Engagement", "acronym": "AXE", "type": "program", + "group": "Engagement Models", "status": "Triggered", "trigger_reason": "19 BLOCKERs across authentication and data classification; structured implementation engagement recommended.", "suggested_timing": "After initial triage", @@ -1270,9 +1277,9 @@ Each entry carries: } ``` -`status` ∈ {Triggered, Applicable, Not Triggered}. `trigger_reason` is non-empty prose explaining why the program fires. +`group` ∈ {Funded Programs, Engagement Models}. `status` ∈ {Triggered, Applicable, Not Triggered}. `trigger_reason` is non-empty prose explaining why the program fires (using ARA profile/severity language, never an internal numeric score). The total triggered set is capped at 3–5 per the library's selection rules; library entries carry `acronym: null` where the program has no acronym. -The MD artifact renders this under an H2 heading **"## Recommended Actions"**. The "## Agentic Program Recommendations" label is retained as an H3 subheading under that H2 to preserve rich program prose without creating duplicate H2s. +The MD artifact renders this under an H2 heading **"## Recommended Actions"**, grouped Funded Programs → Engagement Models. The "## Agentic Program Recommendations" label is retained as an H3 subheading under that H2 to preserve rich program prose without creating duplicate H2s. --- @@ -1294,7 +1301,7 @@ Subsections: 1. **Portfolio Status** — "Out of {N} repositories analyzed, {A} are agent-ready and can integrate with AI agents immediately, {B} are pilot-ready for read-only operations, and {C} require remediation before agent deployment. The analysis identified {H} high severity findings (blockers) and {M} medium severity findings (risks)." 2. **Key Findings** — Top 3 cross-cutting high severity areas as bullet list with repo counts 3. **Remediation Plan** — 3-phase numbered list with finding counts and timelines -4. **Recommended Actions** — Bullet list of triggered programs (AI DLC, AXE, Innovation EBA) with reasons +4. **Recommended Actions** — Bullet list of triggered programs (agentic anchors AI DLC/AXE/Innovation EBA plus any triggered AWS Program & GTM Library programs) with reasons **Stats Card Row** (4 cards): @@ -1347,10 +1354,12 @@ Source: `remediation_roadmap.items[]` grouped by phase #### Recommended AWS Programs Tab -Table columns: `Program`, `Description`, `Why Recommended`, `Duration` +Table columns: `Program`, `Group`, `Description`, `Why Recommended`, `Duration` - Source: `recommended_actions[]` filtered to `status == "Triggered"` -- Canonical programs: AI DLC, AXE, Innovation EBA +- Group values: `Funded Programs`, `Engagement Models` — render as grouped sections in that order (or a Group column), omitting any group with no triggered programs +- The triggered set can include ANY `[ARA]` / `[ARA+MOD]` / `[ARA-anchor]` program from the AWS Program & GTM Library — not just the agentic anchors. Examples that may appear from ARA findings: MAP / MAP for AI Modernization (Funded), AI Assessment Program (Funded), SHIP (Funded — only for infrastructure security gaps like missing CloudTrail/encryption/secrets management, NOT for app-layer agent auth), Well-Architected Review (Funded), AI DLC / AXE / Innovation EBA / ACP / GenAI Innovation Center (Engagement Models), AgentStorming Workshop (Engagement Models) +- Capped at 3–5 total per the library's selection rules. Do not display any internal numeric maturity score. #### Footer diff --git a/aws-managed-definitions/readiness-analysis/portfolio-modernization-readiness-analysis.md b/aws-managed-definitions/readiness-analysis/portfolio-modernization-readiness-analysis.md index 29ac18e..564ee01 100644 --- a/aws-managed-definitions/readiness-analysis/portfolio-modernization-readiness-analysis.md +++ b/aws-managed-definitions/readiness-analysis/portfolio-modernization-readiness-analysis.md @@ -22,7 +22,7 @@ The transformation follows these implementation steps: 9. **Dependency-Aware Phased Roadmap** (Step 6): Generate 4-phase roadmap with dependency-based service ordering 10. **Pathway Aggregation** (Step 7): Aggregate pathway triggers across the portfolio 11. **Synthesis** (Step 8): Integration opportunities, risk analysis, resource allocation -12. **AWS Programs & Engagement Recommendations** (Step 9): Recommend MAP, MMP, WAMP, EBA, OLA, VMP, ISV WMP where triggered +12. **AWS Programs & Engagement Recommendations** (Step 9): Recommend programs and GTM motions from the shared AWS Program & GTM Library (`references/program-library.md`) where triggered 13. **Portfolio-Level Questions** (Step 10): Evaluate PORT-MOD-Q1 through PORT-MOD-Q5 — capabilities only visible across multiple repos The output is a **four-artifact bundle** (per the Four-Artifact Output Contract below) containing: @@ -42,7 +42,7 @@ The MD report contains: - Integration opportunities - Risk analysis with likelihood-impact matrix - Resource allocation recommendations -- AWS Programs & Engagement Recommendations (MAP, OLA, MMP, VMP, WAMP, EBA, ISV WMP) +- AWS Programs & Engagement Recommendations (from the shared AWS Program & GTM Library — MAP, OLA, AppMod, EBA, VMCCO, SHIP, etc., where triggered) - Learning materials mapped to portfolio skill gaps - Service-by-service summary @@ -1019,19 +1019,20 @@ For each identified risk: > **This section appears ONLY in portfolio reports, NEVER in individual reports.** AWS programs are engagement-level decisions scoped to the customer's overall estate, not per-repo. The portfolio view has the right scope to make these recommendations. -Based on the portfolio-wide analysis findings from previous steps, evaluate each of the 8 AWS engagement programs below against its trigger condition. Include a program in the recommendations only if its trigger condition is met. If no programs are triggered, include a brief note instead. +Based on the portfolio-wide analysis findings from previous steps, recommend relevant AWS programs and GTM motions from the shared **AWS Program & GTM Library** in `references/program-library.md`. Load that file when evaluating recommendations — it is the authoritative catalog and contains the full agent instructions (signal patterns, exclusions, qualification criteria, prioritization, grouping, status filtering, and a reasoning checklist). Include a program only if its trigger condition is met. If no programs are triggered, include the brief note in 9.2 instead. -#### 9.1 Programs Catalog and Trigger Logic +#### 9.1 Evaluating the Library from MOD Findings -| Program | Acronym | Trigger Condition | How to Evaluate | -|---------|---------|-------------------|-----------------| -| Migration Acceleration Program | MAP | Portfolio has 3+ repos with workloads NOT yet on AWS (on-premises or another cloud provider) | Check individual report findings for non-AWS hosting signals: (1) no AWS IaC detected (no CDK, CloudFormation, or Terraform with AWS provider), (2) no AWS SDK references in application code, (3) deployment targets referencing non-AWS infrastructure (Azure, GCP, bare-metal, VMware, on-prem data centers, physical server configs), (4) self-hosted CI/CD with no cloud provider integration (Jenkins on-prem, GitLab self-hosted without cloud runners). If 3+ repos show these signals, recommend MAP. MAP is ONLY for net-new workloads migrating to AWS — it does NOT apply to workloads already running on AWS in any form, including EC2-hosted legacy applications. A PHP monolith on EC2 is already on AWS and does not qualify for MAP regardless of how unmodernized it is. | -| Optimization and Licensing Analysis | OLA | Any repo has Oracle, SQL Server, VMware, or commercial license findings | Check individual report findings for DATA-Q4 (stored procedures / commercial SQL) and INF-Q2 (managed DB) scores. If any repo's findings mention Oracle, SQL Server, VMware, or other commercial database/license references, recommend OLA. | -| Microsoft Modernization Program | MMP | Any repo has .NET or Windows workloads detected | Check APP-Q1 (Programming Languages) findings. If any repo uses C#, .NET, ASP.NET, or VB.NET, recommend MMP. | -| VMware Modernization Program | VMP | Any repo has VMware references in IaC or deployment configs | Check individual report findings for VMware, vSphere, ESXi, or vCenter references. If found, recommend VMP. | -| Windows App Modernization Program | WAMP | Any repo has Windows-based deployment targets | Check individual report findings for Windows Server, IIS, or Windows-specific deployment references. If found, recommend WAMP. | -| Experience-Based Acceleration | EBA | Portfolio has 2+ repos with triggered pathways AND overall score < 3.0 | Count repos with at least one triggered pathway AND overall score < 3.0. If count >= 2, recommend EBA. Specify which pathway(s) are most prevalent for the EBA engagement focus. | -| ISV Workload Migration Program | ISV WMP | Portfolio includes ISV or third-party software workloads | Check findings for references to third-party commercial software, ISV applications, or packaged software deployments. If found, recommend ISV WMP. | +Use the `[MOD]` / `[ARA+MOD]`-tagged programs in the library and map MOD's vocabulary as described in the library's "Mapping findings to triggers" section: + +- **Pathways** → use the exact MOD pathway names (`Move to Cloud Native`, `Move to Containers`, `Move to Open Source`, `Move to Managed Databases`, `Move to Managed Analytics`, `Move to Modern DevOps`, `Move to AI`). Use the aggregated pathway plan from Step 7. +- **"Multiple High-severity findings"** → count unified `High` findings across services (from cross-cutting analysis). **"High Effort"** → the finding `effort` field (`High`/`Medium`/`Low`). +- **Classification / category health** → use `severity_status` (`Ready`/`Needs Work`/`Critical`) and `score_rating` (`Mature`/`Partial`/`Needs Work`/`Not Ready`). **Do NOT use or expose the internal 1–4 score** anywhere in the recommendations output. +- **Workload-specific triggers** (VMware, Windows/.NET, Oracle, SAP, Kafka, mainframe) → use the technology stack detected during discovery and the per-service findings. +- **On-prem / migration triggers** (MAP, OLA, Migration Evaluator, VMCCO) → MOD scope is AWS-targeting workloads and excludes on-prem. Trigger these from portfolio/service `context` references (on-prem, data center, VMware estate, "migrating from…") or IaC evidence of non-AWS/VMware providers (`vsphere_*`, bare-metal) — NOT from MOD findings about workloads already on AWS. A legacy app already on EC2 does not qualify for MAP. +- **Customer segment** (Enterprise / SMB / ISV / Startup / WWPS) → infer from portfolio `context` and `service_inventory`. + +Apply the library's selection rules: cap recommendations at **3–5**, prioritize by direct finding match → segment fit → entry-point-before-follow-on, group as **Funded Programs → Engagement Models**, sequence logically (Assessment → Funding → Execution → Optimization), and never recommend `Retiring` or not-yet-imminent `Launching` programs. Run the library's reasoning checklist before finalizing. #### 9.2 Program Recommendations Output @@ -1039,10 +1040,12 @@ For each triggered program: - **Program name and acronym** - **Relevance** — Why this program is recommended based on portfolio findings -- **Trigger findings** — Specific portfolio metrics that triggered the recommendation (e.g., "4 of 6 services have overall score < 2.5") +- **Trigger findings** — Specific portfolio metrics that triggered the recommendation (using severity/classification language, never the internal numeric score — e.g., "4 of 6 services classified Remediation Required", "Move to Containers triggered for 3 services") - **What it provides** — Brief description of the program's value - **Next step** — Recommended action (e.g., "Request MAP engagement via AWS Solutions Architect") +Group the rendered output under **Funded Programs → Engagement Models**. Cap at 3–5 total. + If no programs are triggered, include: "No specific AWS program recommendations based on current findings. As the portfolio evolves, re-assess to identify program eligibility." @@ -1725,11 +1728,24 @@ in exactly one column per pathway row. ### Recommended Programs +> Recommendations are capped at 3–5 and grouped by type. Trigger findings use severity and +> classification language only — the internal 1–4 maturity score is never exposed here. + +#### Funded Programs + +| Program | Acronym | Relevance | Trigger Findings | Next Step | +|---------|---------|-----------|-----------------|-----------| +| | | | | | + +#### Engagement Models + | Program | Acronym | Relevance | Trigger Findings | Next Step | |---------|---------|-----------|-----------------|-----------| | | | | | | -> If no programs are triggered: + +> Omit any group heading that has no triggered programs. +> If no programs are triggered at all: > "No specific AWS program recommendations based on current findings. As the > portfolio evolves, re-assess to identify program eligibility." @@ -2044,7 +2060,7 @@ The Portfolio MOD JSON artifact MUST emit these top-level keys in the order show | `repositories[]` | Per-repo roll-up | | `findings[]` | Lightweight portfolio finding index. See "Portfolio `findings[]` entry shape" below. | | `remediation_roadmap` | See §"Remediation Roadmap" — grouping `pathway` | -| `recommended_actions[]` | Canonical AWS programs (MAP, MMP, WAMP, EBA, OLA, VMP, ISV WMP) | +| `recommended_actions[]` | Triggered AWS programs from the AWS Program & GTM Library (`references/program-library.md`), grouped Funded → Engagement | | `pathways[]` | All 7 AWS Modernization Pathways with JSON-pointer back-references; see §"Pathways Aggregation" | | `dependency_map` | Portfolio dependency map | | `roadmap_phases[]` | Optional, additive | @@ -2200,19 +2216,21 @@ MD rendering under an H2 heading **"## Remediation Roadmap"** matching the webap ### Recommended Actions -The Portfolio MOD JSON emits `recommended_actions[]` with minimum-set coverage: +The Portfolio MOD JSON emits `recommended_actions[]`. Programs are drawn from the AWS Program & GTM Library (`references/program-library.md`); each entry carries a `group` field (`Funded Programs` / `Engagement Models`) so the webapp can render the grouped output. The library is the authoritative source for the program set, trigger logic, and grouping. -| `id` | `name` | `acronym` | `type` | -|---|---|---|---| -| `map` | Migration Acceleration Program | MAP | program | -| `mmp` | Microsoft Modernization Program | MMP | program | -| `wamp` | Windows App Modernization Program | WAMP | program | -| `eba` | Experience-Based Acceleration | EBA | program | -| `ola` | Optimization and Licensing Analysis | OLA | program | -| `vmp` | VMware Migration Program | VMP | program | -| `isv-wmp` | ISV Workload Migration Program | ISV WMP | program | +Each entry envelope: + +| Field | Description | +|---|---| +| `id` | Stable slug for the program (e.g., `map`, `ola`, `appmod-assessment`, `ship`) | +| `name` | Full program name (e.g., Migration Acceleration Program) | +| `acronym` | Program acronym where one exists, else `null` | +| `type` | `program` | +| `group` | `Funded Programs` / `Engagement Models` | +| `status` | `Triggered` / `Applicable` / `Not Triggered` | +| `trigger_reason` | Non-empty prose stating the finding(s) that triggered (or did not trigger) the program — using severity/classification language, never the internal 1–4 score | -Same entry envelope as Portfolio ARA. `status ∈ {Triggered, Applicable, Not Triggered}` with non-empty `trigger_reason`. Emitted under the H2 heading **"## Recommended Actions"**. +The triggered set is capped at 3–5 per the library's selection rules. Same entry envelope as Portfolio ARA. Emitted under the H2 heading **"## Recommended Actions"**. --- @@ -2332,11 +2350,13 @@ Source: `remediation_roadmap.items[]` grouped by phase + `roadmap_phases[]` #### AWS Programs & Engagement Recommendations -Table columns: `Program`, `Relevance`, `What You Get`, `Suggested Timing` +Table columns: `Program`, `Group`, `Relevance`, `What You Get`, `Suggested Timing` - Source: `recommended_actions[]` +- Group values: `Funded Programs`, `Engagement Models` — render as grouped sections in that order (or a Group column), omitting any group with no triggered programs - Relevance values: `Triggered`, `Applicable`, `Not Triggered` -- Show ALL programs (not just triggered) +- The triggered set can include ANY `[MOD]` / `[ARA+MOD]` program from the AWS Program & GTM Library. Examples that may appear from MOD findings: MAP, OLA, AMA, AppMod PoC Funding, Well-Architected Review, AWS Transform tools (Funded Programs); AML, Immersion Days, ProServe Residency, MMA Workshop (Engagement Models) +- Show the recommended set only (capped at 3–5 per the library's selection rules) — do NOT render the full program catalog (88 programs). Do not display any internal numeric maturity score. #### Pathways Tab (MOD-only — ARA does not have this) diff --git a/aws-managed-definitions/readiness-analysis/references/program-library.md b/aws-managed-definitions/readiness-analysis/references/program-library.md new file mode 100644 index 0000000..0d49ac3 --- /dev/null +++ b/aws-managed-definitions/readiness-analysis/references/program-library.md @@ -0,0 +1,508 @@ +# AWS Program & GTM Library + +> **Purpose:** This reference is loaded by the Portfolio Agentic Readiness (portfolio-ara) and Portfolio Modernization Readiness (portfolio-mod) Task Definitions during their program-recommendation step. The analysis agent uses it to recommend relevant AWS programs and GTM motions in the "Next Steps / Recommended Programs" section of the generated portfolio report, based on the actual findings already produced. +> +> **Scope:** Program recommendations are an engagement-level decision and are produced ONLY by the two portfolio TDs — never by the per-repo ARA or MOD TDs. The portfolio view has the cross-repo and customer-segment context required to qualify these programs. +> +> **Source:** APN Programs Roadmap (Apr 2026) + AWS Highspot (Jun 2026). Compiled by Kevin Shin, 2026-06-15. Programs indexed: 42 — Tier 1 detailed: 34 (31 from the source library + 3 ARA agentic anchor programs); Tier 2 compact index: 8. + +--- + +## How the Agent Uses This File + +After the portfolio analysis has produced its findings (cross-cutting BLOCKERs/RISKs and readiness profiles for ARA; aggregated pathways, severity counts, and classification tiers for MOD), evaluate which programs are relevant and emit a short recommendation list. Follow these rules exactly: + +1. **Recommend only when qualification criteria are met.** Each program lists signal patterns, "DO NOT recommend when" exclusions, and qualification criteria. A program is eligible only when its signal patterns match the findings AND none of its exclusions apply. +2. **Cap at 3–5 recommendations per report.** Never exceed 5. Fewer is better than overwhelming the customer. +3. **Prioritize in this order:** + 1. Direct finding match (the strongest signal pattern matches an actual finding) + 2. Customer segment fit (Enterprise / SMB / ISV / Startup / WWPS — inferred from portfolio `context` and `service_inventory`) + 3. Entry-point programs (no prerequisites) before follow-on programs (with prerequisites) +4. **Group recommendations under two headings, in this order:** `Funded Programs` → `Engagement Models`. Assessments, credits, tools, and self-service resources map under Funded Programs; hands-on engagements, workshops, and training map under Engagement Models. +5. **Never recommend programs marked `Retiring`** and never recommend programs marked `Launching` unless the launch is imminent (see Status Key below). +6. **Sequence logically:** Assessment → Funding → Execution → Optimization. Do not recommend a follow-on program without surfacing its prerequisite. +7. **Do not expose internal scoring.** The MOD internal 1–4 maturity score is internal only. When citing MOD evidence in a recommendation, reference unified severity (`High` / `Medium` / `Low`), `severity_status` (`Ready` / `Needs Work` / `Critical`), `score_rating` (`Mature` / `Partial` / `Needs Work` / `Not Ready`), or pathway/profile names — never the numeric score. +8. **Assessment overlap rule:** Recommend only ONE assessment per domain. For database findings, pick ONE of: DBC (speed, days), DBOLA (licensing depth, weeks), RDS Cost Assessment (rehost-only TCO), or OLA for Databases (general infra). Never stack multiple assessments for the same problem. +9. **EBA vs AML rule:** EBA = "learn by doing on real production workloads" (execution-focused, for stalled deals). AML = "structured training program with guided labs" (learning-focused, for skill building). Pick one, not both. +10. **Self-service alternative:** If the customer may not have a dedicated AWS account team (SMB, startup), always include at least one self-service option (Transform tools, Migration Evaluator, Skill Builder, workshop catalog). +11. **Run the reasoning checklist** (at the end of this file) before finalizing the list. + +### Status Key + +| Status | Meaning | +|--------|---------| +| `Active` | Generally available, recommend freely | +| `Launching` | Announced but not yet GA; recommend only if launch ≤30 days away | +| `Retiring` | Being phased out; NEVER recommend | +| `Pilot` | Limited availability; note region/segment restrictions | + +### Mapping findings to triggers — vocabulary alignment + +This library is consumed by two different analyses with different finding vocabularies. When a signal pattern below references a "finding" or "dimension," interpret it against the actual emitted vocabulary: + +**ARA (portfolio-agentic-readiness):** +- ARA emits findings with unified severity `High` / `Medium` / `Low` and native severity `BLOCKER` / `RISK-SAFETY` / `RISK-QUALITY` / `INFO`. +- ARA readiness profiles are exactly: `Agent-Ready`, `Pilot-Ready`, `Pilot-Ready (Safety Concerns)`, `Remediation Required`, `Not Agent-Integrable`. +- ARA category (dimension) display names are exactly: `API Surface`, `Authentication & Authorization`, `State Management`, `Human-in-the-Loop`, `Data Accessibility`, `Discovery & Documentation`, `Observability`, `Engineering Maturity`. +- "3+ High-severity findings across any dimension" → count unified `High` findings (BLOCKERs + conditional BLOCKERs resolved as High) across the portfolio / a service. +- A dimension "showing problems" → that category has 2+ `Medium`/`High` findings. ARA has no "Blocked"/"Needs Work" labels (those are MOD-only); translate accordingly. + +**MOD (portfolio-modernization-readiness):** +- MOD emits findings with unified severity `High` / `Medium` / `Low`; per-category `severity_status` is `Ready` / `Needs Work` / `Critical`; `score_rating` is `Mature` / `Partial` / `Needs Work` / `Not Ready`. +- MOD pathway names are exactly: `Move to Cloud Native`, `Move to Containers`, `Move to Open Source`, `Move to Managed Databases`, `Move to Managed Analytics`, `Move to Modern DevOps`, `Move to AI`. +- MOD finding `effort` field is `High` / `Medium` / `Low` — use it for "High Effort" signal patterns. +- **On-prem / VMware / data-center triggers:** MOD analyzes workloads already targeting AWS and explicitly excludes on-prem from scope. Programs whose signal is "on-prem workloads" (OLA, Migration Evaluator, VMCCO, MAP) must trigger from portfolio/service `context` references (e.g., "running on EC2 from a legacy data center", "VMware estate", "migrating from on-prem") or from IaC evidence of non-AWS/VMware providers (`vsphere_*`, bare-metal configs) — NOT from MOD findings about workloads that already run on AWS. + +`[ARA]`, `[MOD]`, or `[ARA+MOD]` tags on each program indicate which analysis the program is most relevant to. A program may be surfaced by either portfolio TD when tagged for both. Programs tagged `[ARA-anchor]` are agentic enablement programs surfaced only by the portfolio ARA TD. + +--- + +## AGENTIC ENABLEMENT PROGRAMS (ARA Anchors) + +> These three programs are surfaced **only by the portfolio ARA TD**. They are agentic-readiness-specific and have no MOD equivalent. They group under **Engagement Models** in the rendered output. When multiple are triggered, sequence them: AI DLC → AXE → Innovation EBA (Innovation EBA may run in parallel with AXE when use cases are independent). + +### AI DLC (AI Driven Development Lifecycle) `[ARA-anchor]` `Active` +- **Signal patterns:** Portfolio shows teams without established AI-assisted development practices, or engineering-maturity findings indicate manual development workflows that could benefit from AI-driven automation. ARA shows `Pilot-Ready` or `Agent-Ready`; customer wants a structured methodology for AI-powered software development; MOD `Move to AI` pathway detected; customer has development teams ready to adopt AI-augmented workflows. +- **DO NOT recommend when:** Customer needs modernization before they can build with AI (recommend MAP/AppMod first); customer has no development team; findings show architecture is not ready for AI integration. +- **How to evaluate:** Check `Engineering Maturity` (ENG) findings across the portfolio. If 50%+ of services have a `Medium`+ finding on ENG-Q1 (Infra Governance), ENG-Q2 (CI/CD + Contracts), or ENG-Q3 (Rollback), recommend AI DLC. Also recommend if the portfolio `context` mentions a desire for AI-assisted development practices, or if architecture is at/near `Pilot-Ready`/`Agent-Ready`. +- **What the customer gets:** A structured methodology where AI handles planning, task decomposition, and code generation while developers retain control of validation and decisions. Delivers 2–5x development velocity gains. Includes hands-on engagement with AWS experts to implement the methodology across the development lifecycle. +- **How to engage:** Talk to your AWS account team about AI-DLC. Based on your agentic readiness classification, your architecture is positioned to adopt AI-driven development practices. +- **Time to value:** Weeks (methodology adoption); ongoing velocity gains. +- **Segment:** All. +- **Sequencing:** Run first — establishes AI-driven development practices before agentic work. +- **Prerequisite:** Architecture at or near `Pilot-Ready` / `Agent-Ready` per ARA findings. +- **Pairs with:** AXE, Innovation EBA, Agentic Catalyst Program. + +### AXE (Agent Experience Engagement) `[ARA-anchor]` `Active` +- **Signal patterns:** Portfolio shows 3+ services in `Pilot-Ready` or `Agent-Ready` state, or business has defined customer/employee experience goals but lacks a technical implementation roadmap. +- **DO NOT recommend when:** Customer has fewer than 3 services at `Pilot-Ready`+; customer needs modernization before AI (recommend MAP/AppMod); customer already has an agentic implementation plan. +- **How to evaluate:** Count services with profile `Agent-Ready` or `Pilot-Ready`. If count >= 3, recommend AXE. Also recommend if the portfolio `context` describes experience-level goals (e.g., "customer support agent", "employee productivity") without a corresponding technical implementation plan. +- **What the customer gets:** A strategic methodology built on the proven D2E methodology (580+ successful engagements) delivering a six-phase framework: business process mapping, task identification, evaluation metrics, data architecture, governance, and guardrails. The Guardrails & Boundaries phase aligns with ARA — together they provide a complete assess-to-implement pathway: ARA validates system readiness while AXE designs the agent experience and implementation roadmap. +- **How to engage:** Talk to your AWS account team about AXE. Given your portfolio's agentic readiness, this engagement designs the agent experience and implementation roadmap. +- **Segment:** Enterprise. +- **Sequencing:** Run after AI DLC to design the agent experience. +- **Pairs with:** AI DLC, Innovation EBA, ACP. + +### Innovation EBA (AIML-GenAI) `[ARA-anchor]` `Active` +- **Signal patterns:** Portfolio `context` indicates AI/ML or GenAI is a strategic imperative, executive sponsorship exists, use cases deliver critical business value, a data strategy exists, and the customer is committed to production deployment within ~90 days. The portfolio should also show a backlog of AIML-GenAI use cases and a customer team committed to upskilling. +- **DO NOT recommend when:** Customer has no executive sponsorship; no data strategy; no GenAI use cases identified; customer needs modernization before AI. +- **How to evaluate:** Check portfolio `context` for: (1) AI/ML or GenAI as strategic priority, (2) executive sponsorship, (3) existing data and data strategy (services with established data pipelines, DynamoDB/Aurora/S3 data stores), (4) use cases in customer experience (chatbots, post-call analytics, personalization), productivity (intelligent search, summarization, code generation), business operations (IDP, fraud detection, predictive maintenance), or content creation. Also recommend if 3+ services have production-ready data stores AND the `context` describes GenAI ambitions beyond what AXE alone covers. +- **What the customer gets:** A 3-day sprint-based, interactive engagement following the 4-step EBA framework (Executive Alignment → Readiness → Accelerate → Transform At Scale) with workstreams including Foundations, Data Engineering, GenAI Build/Evaluate, UI Integration, ML Ops, and Command Center. Develops skills through learning-by-doing and builds/accelerates a GenAI use case pipeline. +- **How to engage:** Talk to your AWS account team about Innovation EBA. Your portfolio shows strong data foundations and GenAI ambitions — this engagement accelerates a use case into production. +- **Segment:** Enterprise (higher cloud maturity). +- **Sequencing:** Run when the customer is ready to accelerate a use case into production; may run in parallel with AXE if use cases are independent. +- **Pairs with:** AI DLC, AXE, GenAI Innovation Center. + +--- + +## FUNDED PROGRAMS + +> Assessments (no cost to customer) and credit/funding programs group here. Assessments are entry points; funded programs require qualification. + +### OLA (Optimization & Licensing Assessment) `[MOD]` `Active` +- **Signal patterns:** MOD findings indicate on-prem workloads needing migration; customer hasn't quantified savings; VMware or Microsoft licensing referenced in portfolio `context`. +- **DO NOT recommend when:** Customer already has business case data; workloads already on AWS; customer only has cloud-native workloads. +- **Qualification:** Customer has on-prem workloads; willing to share infrastructure data. +- **What the customer gets:** Comprehensive analysis of current compute, storage, and licensing costs with modeled AWS savings (avg 36% compute savings, 45% licensing reduction). +- **How to engage:** Talk to your AWS account team about an Optimization & Licensing Assessment. This no-cost assessment analyzes your current infrastructure and licensing, showing projected savings on AWS. +- **Time to value:** 2–3 weeks. +- **Prerequisite:** None (entry point). +- **Pairs with:** MAP, Migration Evaluator. + +### OLA for Databases `[MOD]` `Active` +- **Signal patterns:** MOD `Move to Managed Databases` pathway triggered; on-prem Oracle/SQL Server detected in `context`; licensing cost is a migration blocker. +- **DO NOT recommend when:** Customer already committed to DB migration path; no database licensing concerns. +- **What the customer gets:** Database-specific licensing analysis with migration options and cost modeling. +- **How to engage:** Talk to your AWS account team about a Database Optimization Assessment. +- **Prerequisite:** None. +- **Pairs with:** MAP, DBC. + +### OLA for VMware `[MOD]` `Active` +- **Signal patterns:** VMware infrastructure detected in portfolio `context`; Broadcom licensing concerns; evaluating alternatives. +- **DO NOT recommend when:** Customer not running VMware; no VMware references in `context` or IaC. +- **What the customer gets:** VMware-specific analysis with modeled costs across AWS migration pathways. +- **How to engage:** Talk to your AWS account team about a VMware optimization assessment. +- **Prerequisite:** None. +- **Pairs with:** MAP, VMware Modernization Program. + +### DBC (Directional Business Case) `[MOD]` `Active` +- **Signal patterns:** MOD `Move to Managed Databases` pathway detected; customer needs quick financial justification for database migration; early-stage conversation. +- **DO NOT recommend when:** Customer already has detailed TCO data (recommend DBOLA for deeper analysis); customer already committed to migration (recommend MAP). +- **What the customer gets:** Rapid, high-level financial comparison of current database costs vs. AWS managed services. Delivered in days, not weeks. +- **How to engage:** Talk to your AWS account team about running a Directional Business Case. +- **Time to value:** Days. +- **Prerequisite:** None (entry point, often first step before DBOLA). +- **Pairs with:** DBOLA, AWS Transform for SQL Server, MAP. + +### DBOLA (Database Optimization & Licensing Assessment) `[MOD]` `Active` +- **Signal patterns:** MOD `Move to Managed Databases` pathway with Oracle or SQL Server detected; customer has licensing concerns blocking migration decision; needs prescriptive post-migration licensing position. +- **DO NOT recommend when:** Customer doesn't have licensing concerns (recommend DBC instead); customer already resolved licensing questions. +- **What the customer gets:** Prescriptive Oracle/SQL Server licensing guidance delivered by a licensing specialty partner. Provides detailed post-migration effective licensing position. +- **How to engage:** Talk to your AWS account team about a Database Optimization & Licensing Assessment. +- **Time to value:** Weeks. +- **Prerequisite:** None (can run in parallel with DBC). +- **Pairs with:** DBC, OLA for Databases, MAP. + +### Migration Evaluator `[MOD]` `Active` +- **Signal patterns:** Early-stage; customer considering migration but has no data; needs directional business case; referenced in portfolio `context`. +- **DO NOT recommend when:** Customer already has OLA or detailed inventory data. +- **What the customer gets:** Data-driven business case for migration with projected costs and savings, at no cost. +- **How to engage:** Self-service at https://aws.amazon.com/migration-evaluator/ or request through your AWS team. +- **Time to value:** 1–2 weeks. +- **Prerequisite:** None (entry point). +- **Pairs with:** OLA, MAP. + +### Well-Architected Review `[ARA+MOD]` `Active` +- **Signal patterns:** ARA `Engineering Maturity` or `Observability` dimensions have 2+ `Medium`/`High` findings; MOD findings span multiple architectural categories. +- **DO NOT recommend when:** Customer already completed a recent review; ARA/MODA findings already comprehensively cover the customer's architectural concerns (ARA IS an architecture review); findings are narrowly focused on one dimension. +- **What the customer gets:** Free architecture review against 6 pillars with prioritized recommendations. +- **How to engage:** Request through your AWS account team or an AWS Partner. +- **Time to value:** 1–2 weeks. +- **Prerequisite:** None. +- **Pairs with:** MAP. + +### AI Assessment `[ARA]` `Active` +- **Signal patterns:** ARA shows `Pilot-Ready` or `Agent-Ready`; customer wants to define AI/agentic strategy but hasn't started; needs ROI justification. +- **DO NOT recommend when:** Customer already has AI strategy defined; findings are all about modernization not AI. +- **Qualification:** Any customer considering AI adoption. +- **Funding:** Up to $15K (SMB) / $30K (Enterprise). +- **What the customer gets:** Funded AI strategy assessment including use case discovery, feasibility analysis, and ROI modeling. +- **How to engage:** Talk to your AWS account team about the AI Assessment Program. +- **Time to value:** 2–3 weeks. +- **Prerequisite:** None (entry point). +- **Pairs with:** Agentic Catalyst Program, GenAI Innovation Center. + +### MAP (Migration Acceleration Program) `[ARA+MOD]` `Active` +- **Signal patterns:** 3+ High-severity findings across any ARA dimension; MOD shows multiple pathways requiring significant investment; portfolio-scale modernization needed; customer has migration-scale workload referenced in `context`. +- **DO NOT recommend when:** Single-app modernization only (recommend AppMod PoC); customer only needs assessment (recommend OLA); opportunity <$500K ARR; workload already running on AWS with no net-new migration. +- **Qualification:** $500K+ ARR opportunity; committed migration/modernization plan; partner engaged; workloads identified. +- **Funding:** Credits + partner cash (up to 30% ARR via SPI). +- **What the customer gets:** AWS credits, partner funding, tools, automation, training, and expert support to accelerate migration and modernization. +- **How to engage:** Talk to your AWS account team about the Migration Acceleration Program (MAP). +- **Time to value:** 3–6 months. +- **Prerequisite:** OLA or Migration Evaluator recommended first. +- **Pairs with:** EBA, OLA, AppMod Assessment. + +### MAP for AI Modernization `[ARA+MOD]` `Active` +- **Signal patterns:** ARA profile is `Remediation Required` or `Pilot-Ready` AND customer has a modernization need to enable agentic/AI workloads; MOD `Move to AI` pathway triggered. +- **DO NOT recommend when:** No modernization needed; architecture already agent-ready (ARA `Agent-Ready`). +- **Qualification:** MAP-eligible opp + AI modernization use case. +- **Funding:** MAP credits (AI use cases). +- **What the customer gets:** MAP credits and support specifically for modernizing applications to support AI/agent workloads. +- **How to engage:** Ask your AWS account team about MAP for AI Modernization. +- **Prerequisite:** Standard MAP qualification + AI use case validation. +- **Pairs with:** AI Assessment, Agentic Catalyst Program. + +### AppMod PoC Funding `[MOD]` `Active` +- **Signal patterns:** MOD pathway detected (`Move to Containers` or `Move to Cloud Native`) and customer wants to validate approach on a specific application before scaling. +- **DO NOT recommend when:** Customer needs full business case first (recommend DBC or OLA); scope is portfolio-wide (recommend MAP). +- **Qualification:** Identified modernization target; technical team available; needs feasibility proof. +- **Funding:** Up to $100K in co-funding for partner-led engagements. +- **What the customer gets:** Funded proof-of-concept to validate containers/serverless modernization on a real application. +- **How to engage:** Ask your AWS account team about AppMod PoC funding. +- **Time to value:** 2–4 weeks. +- **Prerequisite:** None (can be entry point or post-assessment). +- **Pairs with:** MAP. + +### Microsoft Modernization Program `[MOD]` `Active` +- **Signal patterns:** MOD findings reference Windows Server, .NET Framework, IIS, SQL Server; `Move to Containers` or `Move to Cloud Native` for Windows workloads. +- **DO NOT recommend when:** No Windows/Microsoft workloads. +- **Qualification:** Windows/.NET workloads with modernization path identified. +- **What the customer gets:** AWS-funded partner solutions to accelerate Windows and .NET modernization. +- **How to engage:** Ask your AWS account team about the Microsoft Modernization Program. +- **Pairs with:** AML, AWS Transform for Windows. + +### VMware Modernization Program `[MOD]` `Active` +- **Signal patterns:** Large VMware estate detected in portfolio `context`; infrastructure modernization needed; Broadcom licensing pressure. +- **DO NOT recommend when:** No VMware workloads; small VM count; no VMware references in `context` or IaC. +- **Qualification:** VMware workloads migrating to AWS; partner engaged. +- **What the customer gets:** Partner-funded VMware modernization support including assessment, migration planning, and execution. +- **How to engage:** Ask your AWS account team about VMware migration programs. +- **Pairs with:** OLA for VMware, MAP. + +### AWS Modernization Assurance (AMA) `[MOD]` `Active` +- **Signal patterns:** Large VMware estate (2000+ VMs) referenced in `context`; competitive risk; urgent migration timeline. +- **DO NOT recommend when:** <2000 VMs; opportunity <$2M; no competitive pressure; timeline >12 months. +- **Qualification:** 2,000+ VMs; $2M+ scope; migration within 12 months; executive commitment. +- **What the customer gets:** Funding covering migration costs, up to 50% of first-year run costs, training, and licensing support. +- **How to engage:** Ask your AWS account team about enterprise VMware migration funding. +- **Time to value:** 6–12 months. +- **Prerequisite:** OLA completed; MAP qualified. +- **Pairs with:** MAP, VMware Modernization Program. + +### AWS-Funded ISV Tooling `[MOD]` `Active` +- **Signal patterns:** MOD findings show complex migration/modernization scope where specialized third-party tools would accelerate execution; customer or partner needs automated discovery, assessment, or migration tooling beyond AWS native services. +- **DO NOT recommend when:** Simple migration achievable with AWS native tools (Application Migration Service, DMS); customer at prospect/pilot/PoC stage (must be Qualified+). +- **Qualification:** Active migration/modernization project; working with AWS Partner with Migration & Modernization Competency or MSP designation. +- **What the customer gets:** Fully funded access to specialized third-party ISV tools (CAST, CloudHedge, Cloudamize, vFunction, RiverMeadow, MontyCloud) for automated discovery, assessment, planning, and migration execution. +- **How to engage:** Talk to your AWS account team about funded ISV tooling for your migration. +- **Time to value:** Weeks (tool deployment + automated assessment). +- **Prerequisite:** Active migration/modernization project with an AWS Partner. +- **Pairs with:** MAP, OLA, EBA, AppMod PoC Funding. + +### AWS Activate (Startup Credits) `[ARA+MOD]` `Active` +- **Signal patterns:** Customer is an early-stage startup (pre-Series B); needs credits to fund modernization or build agentic applications. +- **DO NOT recommend when:** Customer is an established enterprise; customer already has significant AWS spend. +- **Qualification:** Self-funded or pre-Series B; funded within last 12 months; founded in past 10 years. +- **What the customer gets:** Tiered AWS credits: Bootstrapped ($1K), Pre-Seed ($5–25K), Seed ($100K), Series A ($200K). Covers AWS services including Bedrock 3rd-party models. +- **How to engage:** Apply at https://aws.amazon.com/activate/ or through your accelerator/incubator program. +- **Prerequisite:** None. +- **Pairs with:** IW Programs. + +### IW (Incremental Workloads) Programs for Startups `[ARA+MOD]` `Active` +- **Signal patterns:** Startup customer with findings indicating migration from competitive platform, or needing AI/ML assessment, or adopting new AWS services strategically. +- **DO NOT recommend when:** Customer is not in startup segment; enterprise customer (use MAP instead). +- **Qualification:** Startup company. +- **What the customer gets:** Funded programs tailored for startups: IW Assess ($5K flat for AI/ML assessments; up to 8% ARR capped at $10K for migration assessments), IW Build (up to 25% of incremental ARR), IW Migrate (up to 25% of ARR for migrating from competitive platforms). +- **How to engage:** Talk to your AWS startup account team about Incremental Workloads funding. +- **Time to value:** Weeks. +- **Prerequisite:** Active startup. +- **Pairs with:** AWS Activate, MAP (if graduating to enterprise). + +### Activate4GF (Greenfield Credits) `[ARA+MOD]` `Active` +- **Signal patterns:** Customer is new to AWS (greenfield); any segment; portfolio `context` shows workloads currently on-prem or competitive cloud; customer exploring AWS for the first time. +- **DO NOT recommend when:** Customer already has significant AWS footprint; customer already MAP-qualified. +- **Qualification:** Greenfield customer (new to AWS) across all segments. +- **What the customer gets:** AWS Service Credits to accelerate cloud adoption: explore services, migrate workloads, or build AI/ML solutions. +- **How to engage:** Talk to your AWS account team about greenfield credits. +- **Prerequisite:** Must be greenfield (new to AWS). +- **Pairs with:** OLA, MAP, AWS Activate (startups). + +--- + +## ENGAGEMENT MODELS + +> Hands-on engagements, workshops, skill-building programs, and training map here. These require customer team commitment. + +### EBA (Experience-Based Acceleration) `[ARA+MOD]` `Active` +- **Signal patterns:** Multiple `High` effort remediation items in findings; customer team lacks hands-on experience with target architecture; deal stalled on technical validation. (MOD: 2+ services with a triggered pathway AND `Partial`/`Needs Work`/`Not Ready` classification.) +- **DO NOT recommend when:** Customer has strong internal engineering team; findings are mostly Low effort; customer just needs assessment; customer needs training not execution (recommend AML). +- **Qualification:** Customer commits team for immersive multi-day engagement; executive sponsor; >$500K ARR. +- **What the customer gets:** Structured, immersive engagement where your team and AWS/partner experts migrate real workloads together, building skills while delivering results. +- **How to engage:** Talk to your AWS account team about Experience-Based Acceleration. +- **Time to value:** 4–8 weeks. +- **Prerequisite:** Modernization target identified; technical scope defined. +- **Pairs with:** MAP, AML. + +### AML (Application Modernization Lab) `[MOD]` `Active` +- **Signal patterns:** MOD `Move to Containers` or `Move to Cloud Native` pathway + customer team needs both training and guided execution on modernization techniques. +- **DO NOT recommend when:** Customer just needs a PoC (recommend AppMod PoC); team already experienced with target architecture; customer needs execution not learning (recommend EBA). +- **Qualification:** Customer team available for training + guided modernization. +- **What the customer gets:** Hands-on modernization training combined with guided execution on your actual applications. +- **How to engage:** Talk to your AWS account team about the Application Modernization Lab. +- **Time to value:** 4–8 weeks. +- **Prerequisite:** Modernization target identified. +- **Pairs with:** EBA, MAP, Microsoft Modernization Program. + +### Agentic Catalyst Program (ACP) `[ARA]` `Active` +- **Signal patterns:** ARA shows `Pilot-Ready` or `Agent-Ready`; customer (ISV) wants to build agentic AI products; in ideation stage of agentic strategy. +- **DO NOT recommend when:** Customer is not an ISV; needs modernization before AI; already has agentic products in production. +- **Qualification:** ISV; in ideation stage; willing to dedicate executive + technical team for 1 week. +- **What the customer gets:** Week-long accelerated engagement: executive alignment, curated use cases, technical build days, solution architecture, and ROI preview. +- **How to engage:** Talk to your AWS account team about the Agentic Catalyst Program. +- **Time to value:** 1 week (engagement) → 12 months (production). +- **Prerequisite:** None (entry point for ISVs). +- **Pairs with:** ISV Booster, AI Assessment. + +### Immersion Days `[ARA+MOD]` `Active` +- **Signal patterns:** ARA findings show specific technology skill gaps (containers, serverless, observability); MOD pathway requires technology customer hasn't used before. +- **DO NOT recommend when:** Customer team already skilled in target technology; problem is funding/strategy not skills. +- **Qualification:** Technical team available for 1-day hands-on workshop. +- **What the customer gets:** Free, hands-on deep-dive workshop on specific AWS services relevant to your modernization path. +- **How to engage:** Ask your AWS account team about Immersion Days for [specific technology]. +- **Time to value:** 1 day. +- **Prerequisite:** Specific skill gap identified from findings. +- **Pairs with:** EBA, AML. + +### GenAI Innovation Center `[ARA]` `Active` +- **Signal patterns:** ARA shows `Agent-Ready`; customer wants to co-innovate on GenAI/agentic solutions (not just integrate agents into existing apps). +- **DO NOT recommend when:** Customer needs modernization first; only wants agent integration (recommend ACP or AI Assessment). +- **Qualification:** Strategic customer building GenAI solutions; willing to co-innovate with AWS. +- **What the customer gets:** Comprehensive GenAI innovation program with AWS experts to develop cutting-edge solutions from ideation through delivery. +- **How to engage:** Talk to your AWS account team about the Generative AI Innovation Center. +- **Prerequisite:** AI strategy defined; architecture ready. +- **Pairs with:** AI Assessment, ACP. + +### ProServe Residency `[ARA+MOD]` `Active` +- **Signal patterns:** ARA shows `Remediation Required` with 10+ `High`/`Medium` findings spanning most dimensions; MOD shows complex multi-pathway modernization across 5+ services; sustained expert support needed over months. +- **DO NOT recommend when:** Findings are focused (1–2 dimensions); workshops + PoC would suffice. +- **Qualification:** Complex transformation; budget for engagement; 3–12 month program. +- **What the customer gets:** Dedicated AWS experts embedded alongside your team for months, providing continuous architecture guidance and hands-on execution support. +- **How to engage:** Ask your AWS account team about ProServe Residency engagements. +- **Time to value:** 3–12 months. +- **Prerequisite:** Scope defined; budget available. +- **Pairs with:** MAP (to fund), EBA (lighter alternative). + +### AgentStorming Workshop `[ARA]` `Active` +- **Signal patterns:** ARA report generated but customer wants to identify WHERE to deploy agents across their business processes (beyond code-level readiness). +- **DO NOT recommend when:** Customer only needs code-level remediation; not ready for process-level thinking. +- **What the customer gets:** Facilitated workshop combining cognitive complexity analysis with process discovery to identify high-value opportunities for AI agent deployment across your business. +- **How to engage:** Talk to your AWS account team about an AgentStorming workshop. +- **Pairs with:** ARA/MODA (code-level), Agentic Catalyst (ISVs). + +### AWS AI League `[ARA]` `Active` +- **Signal patterns:** ARA `Engineering Maturity` dimension has 2+ `Medium`/`High` findings indicating team skill gaps in AI/agent development; customer wants to upskill teams before implementing agents. +- **What the customer gets:** Gamified team-based AI tournament focused on model customization and agent building. +- **How to engage:** Ask your AWS account team about hosting an AWS AI League event. +- **Pairs with:** Immersion Days, Agentic Catalyst. + +### MMA Workshop (Migration and Modernization Acceleration) `[MOD]` `Active` +- **Signal patterns:** MOD `Move to Managed Databases` pathway with SQL Server, Oracle, or Sybase detected; customer team needs hands-on migration experience before committing; customer wants to understand GenAI-accelerated migration approach. +- **DO NOT recommend when:** Customer has no database migration need; customer already experienced with AWS DMS/SCT and Aurora PostgreSQL; opportunity <$100K ARR. +- **Qualification:** Customer with identified database modernization opportunity; technical team available (Cloud Architects, DBAs, App Developers); targets SQL Server, Oracle, or Sybase to Aurora PostgreSQL. +- **What the customer gets:** Full-day immersive workshop covering end-to-end database migration to Aurora PostgreSQL using GenAI-powered tools. Hands-on labs with real applications: schema conversion, stored procedure migration with GenAI, application SQL conversion, data migration with AWS DMS, and automated test generation. +- **How to engage:** Talk to your AWS account team about the MMA Workshop. +- **Time to value:** 1 day (workshop). +- **Prerequisite:** Aurora PostgreSQL Immersion Day recommended (not required). +- **Available tracks:** SQL Server (.NET), Oracle (Java), Sybase ASE. +- **Self-paced links:** [SQL Server](https://catalog.workshops.aws/mma-mssql-pg/en-US) | [Oracle](https://catalog.workshops.aws/mma-oracle-pg/en-US) | [Sybase](https://catalog.workshops.aws/mma-sybase-pg) +- **Pairs with:** DBC, DBOLA, AWS Transform for SQL Server, EBA, MAP. + +### SHIP (Security Health Improvement Program) `[ARA+MOD]` `Active` +- **Signal patterns:** ARA findings reference hardcoded credentials, missing secrets management, no CloudTrail/audit trail, unmonitored network exposure, missing encryption, or lack of vulnerability scanning. Also relevant when MOD identifies modernization pathways and the target AWS environment lacks foundational security services. +- **DO NOT recommend when:** ARA Auth findings are purely application-layer agent readiness gaps (machine identity for agents, scoped agent permissions, agent identity suspension, on-behalf-of flows). These require application code changes, not AWS service enablement. Also skip if customer already has GuardDuty, Security Hub, Config, and IAM Access Analyzer active. +- **Important distinction:** SHIP = infrastructure-layer security (are the right AWS security services enabled and operationalized?). ARA Auth dimension = application-layer agent readiness (can your app safely interact with autonomous agents?). Different layers. Don't conflate them. +- **What the customer gets:** Free security posture assessment covering 9 foundational use cases (threat detection, CSPM, vulnerability management, configuration monitoring, application firewall, application security testing, credentials protection, network protection, key management). Establishes a baseline, delivers a prioritized improvement roadmap, and routes to deeper engagements as needed. Repeatable unlimited times. +- **How to engage:** Talk to your AWS account team about the Security Health Improvement Program. Based on the security infrastructure findings in this report, SHIP maps your gaps to specific AWS services and provides a prioritized remediation roadmap at no cost. +- **Time to value:** 2 weeks (Discovery Call → Delivery Meeting → Actionable Roadmap). +- **Prerequisite:** None. Any customer, any size, any support tier. +- **Directly resolves these findings:** + - "Hardcoded credentials / no secrets management" → Credentials Protection (Secrets Manager) + - "No audit logging / no CloudTrail" → Configuration Monitoring + Threat Detection + - "Missing encryption / no key management" → Key Management (KMS) + - "Unmonitored network exposure" → Network Protection + Vulnerability Management +- **Does NOT resolve these ARA findings (recommend EBA or ProServe instead):** + - "No machine identity authentication for agents" + - "No scoped permissions per agent" + - "No agent identity suspension mechanism" + - "No action-level authorization" +- **Inline remediation note:** For simple findings like "missing encryption at rest", the report should first recommend the direct fix (e.g., enable KMS encryption on the resource) in the remediation section. SHIP is recommended when there are **multiple** infrastructure security gaps that suggest a systemic posture problem, not for isolated single-service fixes. +- **Pairs with:** Well-Architected Review, MAP (if remediation needs funding), Immersion Days (go deeper on specific services). + +--- + +## SELF-SERVICE TOOLS + +> These are customer-facing, no-cost tools that customers can use immediately. They group under **Funded Programs** in the rendered output. + +### AWS Transform Custom (ARA/MODA) `[ARA+MOD]` `Active` +- **Signal patterns:** Customer has additional repositories not yet analyzed; wants to expand assessment scope. +- **What the customer gets:** AI-powered code analysis producing agentic readiness scoring and modernization recommendations for additional applications. +- **How to engage:** Self-service via AWS Transform Custom console. +- **Prerequisite:** None. +- **Pairs with:** MAP. + +### AWS Transform for Windows `[MOD]` `Active` +- **Signal patterns:** MOD findings reference Windows/.NET/IIS workloads; `Move to Containers` or `Move to Cloud Native` pathway. +- **What the customer gets:** AI-powered automated analysis and transformation planning for Windows and .NET workloads. +- **How to engage:** Self-service via AWS Transform console. +- **Pairs with:** Microsoft Modernization Program, AML. + +### AWS Transform for SQL Server `[MOD]` `Active` +- **Signal patterns:** MOD `Move to Managed Databases` pathway with SQL Server detected; stored procedures, T-SQL, or SQL Server schemas referenced in findings. +- **DO NOT recommend when:** Customer wants to stay on SQL Server (recommend RDS for SQL Server instead); no SQL Server workloads detected. +- **What the customer gets:** Agentic AI-powered SQL Server to Aurora PostgreSQL modernization: intelligent schema conversion, stored procedure transformation, coordinated application refactoring, and three-tier schema validation. Accelerates migration by up to 5x. +- **How to engage:** Self-service via AWS Transform console at https://aws.amazon.com/transform/windows/sql-server/ +- **Time to value:** Days to weeks (depending on schema complexity). +- **Prerequisite:** None (entry point for SQL Server modernization). +- **Pairs with:** DBC, DBOLA, MAP. + +### RDS for SQL Server Cost Assessment `[MOD]` `Active` +- **Signal patterns:** Customer considering SQL Server migration but unsure about costs; MOD findings show SQL Server workloads; customer wants to evaluate RDS for SQL Server vs. on-prem TCO. +- **DO NOT recommend when:** Customer already committed to Aurora PostgreSQL migration (recommend AWS Transform for SQL Server instead). +- **What the customer gets:** TCO assessment estimating costs for migrating on-premises SQL Server databases to Amazon RDS for SQL Server. +- **How to engage:** Available within AWS Transform console. +- **Time to value:** Days. +- **Prerequisite:** None. +- **Pairs with:** DBC, DBOLA, MAP. + +--- + +## TIER 2: COMPACT INDEX (Situational programs) + +> Scan this index if the customer's workload type, technology stack, or segment suggests a specialized program not covered in Tier 1 above. Only surface these when directly relevant to findings. + +### Workload-Specific Programs + +| Program | Tag | Status | When to Surface | What Customer Gets | +|---------|-----|--------|----------------|-------------------| +| Oracle DB@AWS Pilot Promotion | `[MOD]` | Active | MOD `Move to Managed Databases` + Oracle detected (NAMER only) | Funded Oracle-to-AWS database migration | +| Kafka Migration Program | `[MOD]` | Active | MOD `Move to Managed Analytics` + Kafka/streaming detected | Funded Kafka-to-MSK migration with MSK Replicator | +| SAP on AWS | `[MOD]` | Active | SAP workloads detected in findings or `context` | Funded SAP migration and optimization on AWS | +| Mainframe Modernization | `[MOD]` | Active | COBOL/mainframe workloads detected in `context` | Assessment, replatform, or refactor for mainframe applications | +| End of Support Migration | `[MOD]` | Active | EOL software detected (Windows Server 2012, SQL 2014, RHEL 7) | Funded migration path off end-of-life platforms | + +### ISV/Partner Programs + +| Program | Tag | Status | When to Surface | What Customer Gets | +|---------|-----|--------|----------------|-------------------| +| ISV Accelerate | `[ARA]` | Active | Customer is ISV wanting to co-sell with AWS | Joint go-to-market support, co-sell incentives, Marketplace integration | +| ISV Booster Program | `[ARA]` | Active | ISV with $1M–$50M ARR experiencing slow growth | Accelerated growth through Agentic Catalyst + Enhanced Passport + Valuation Readiness | +| AWS SaaS Factory | `[MOD]` | Active | ISV building multi-tenant SaaS; MOD shows SaaS architecture gaps | Architecture guidance and reference implementations for multi-tenant SaaS | + +--- + +## SELF-SERVICE & COMMUNITY RESOURCES + +> After listing funded programs and engagements, include 1–2 relevant self-service resources based on findings. These give customers an immediate next step without waiting for account team engagement. + +### AWS Connected Community `[ARA+MOD]` `Active` +- **Signal patterns:** Customer is SMB or startup; wants peer networking, expert access, or upcoming events. +- **What the customer gets:** Access to AWS experts, exclusive events, peer community, and curated resources. +- **Link:** https://aws-experience.com + +### AWS Skill Builder `[ARA+MOD]` `Active` +- **Signal patterns:** ARA `Engineering Maturity` dimension has findings indicating team skill gaps; MOD pathways require technologies the team hasn't used before. +- **What the customer gets:** On-demand digital training with 600+ free courses, hands-on labs, learning plans, and certification prep. +- **Link:** https://skillbuilder.aws/ +- **Finding to learning path mapping:** + - `Move to Containers` → Containers Learning Plan, EKS/ECS courses + - `Move to Managed Databases` → Database Migration learning path, Aurora/DynamoDB courses + - `Move to Cloud Native` → Serverless Learning Plan, Lambda/API Gateway courses + - ARA `Observability` gaps → Observability learning path, CloudWatch/X-Ray courses + - ARA `Authentication & Authorization` gaps → Security Learning Plan, IAM/Cognito courses + - `Move to AI` / Agentic readiness → Generative AI Learning Plan, Bedrock/Agents courses + +### Public Workshop Catalog (Self-Paced) `[ARA+MOD]` `Active` +- **Signal patterns:** MOD findings show specific technology pathways; customer team wants hands-on practice at their own pace. +- **What the customer gets:** Self-paced, hands-on labs covering database migration, containers, serverless, and AI. +- **Link:** https://catalog.workshops.aws/ +- **Finding-based workshop mapping:** + +| MOD Pathway / ARA Dimension | Relevant Workshop | Self-Paced Link | +|-----------------------------|-------------------|-----------------| +| `Move to Managed Databases` + SQL Server | MMA Workshop (SQL Server track) | https://catalog.workshops.aws/mma-mssql-pg/en-US | +| `Move to Managed Databases` + Oracle | MMA Workshop (Oracle track) | https://catalog.workshops.aws/mma-oracle-pg/en-US | +| `Move to Managed Databases` + Sybase | MMA Workshop (Sybase track) | https://catalog.workshops.aws/mma-sybase-pg | +| `Move to Managed Databases` (any) | Aurora PostgreSQL Immersion Day | https://catalog.workshops.aws/apgimmday | +| `Move to Containers` / `Move to Cloud Native` | EKS Workshop / ECS Workshop | https://catalog.workshops.aws/ (search containers) | +| ARA `Engineering Maturity` gaps | Well-Architected Labs | https://wellarchitectedlabs.com | +| ARA `Observability` gaps | One Observability Workshop | https://catalog.workshops.aws/observability | +| `Move to AI` / `Agent-Ready` | Bedrock Workshop / Agentic AI labs | https://catalog.workshops.aws/ (search bedrock agents) | + +--- + +## AGENT REASONING CHECKLIST + +Before finalizing the recommendation list, verify each item: + +1. ☐ Does every recommended program meet its qualification criteria based on actual findings? +2. ☐ Have I checked the "DO NOT recommend when" exclusions for each selected program? +3. ☐ Is the total count between 3 and 5 recommendations? +4. ☐ Are recommendations grouped correctly? (`Funded Programs` → `Engagement Models`) +5. ☐ Did I check workload-specific programs (VMware, Windows, Oracle, SAP, Kafka, Mainframe)? +6. ☐ Did I check customer segment (Startup → Activate/IW; ISV → ACP/Booster/SaaS Factory; SMB → self-service options)? +7. ☐ Did I include at least one entry-point program (no prerequisites)? +8. ☐ Are programs sequenced logically? (Assessment → Funding → Execution → Optimization; for ARA anchors: AI DLC → AXE → Innovation EBA) +9. ☐ Did I apply the assessment overlap rule (only ONE assessment per domain)? +10. ☐ Did I apply the EBA vs AML rule (pick one, not both)? +11. ☐ Am I using the correct vocabulary? (ARA: unified severity + profile names; MOD: severity_status + score_rating + pathway names; NEVER internal 1–4 scores) +12. ☐ Is no `Retiring` program included? Is no `Launching` program included unless launch ≤30 days away? +13. ☐ For SMB/startup customers, did I include at least one self-service option? +14. ☐ Is "How to engage" framed from the customer's perspective? + +--- + +*Last updated: June 15, 2026* +*Source: APN Programs Roadmap (Apr 2026) + AWS Highspot (Jun 2026)* +*Total programs indexed: 42 — Tier 1 detailed: 34 (31 from source + 3 ARA agentic anchors); Tier 2 compact: 8*