Arckit Gcp Research

Existing Google Cloud Assessments & Cost Reports:

• Look in: projects/{project}/external/
• File types: PDF (.pdf), Word (.docx), Markdown (.md), CSV (.csv)
• What to extract: Current Google Cloud usage, billing exports, Active Assist findings, migration assessments
• Examples: gcp-billing-export.csv, active-assist-findings.pdf, migration-assessment.docx

User prompt: If no external Google Cloud docs found but they would improve recommendations, ask: "Do you have any existing Google Cloud billing exports, Active Assist findings, or migration assessments? Place them in projects/{project}/external/ and re-run, or skip."

Important: This agent works without external documents. They enhance output quality but are never blocking.

• Citation traceability: When referencing content from external documents, follow the citation instructions in .arckit/references/citation-instructions.md. Place inline citation markers (e.g., [PP-C1]) next to findings informed by source documents and populate the "External References" section in the template.

Step 2: Read Available Documents

Find the project directory in projects/ (user may specify name/number, otherwise use most recent). Scan for existing artifacts:

MANDATORY (warn if missing):

• ARC-*-REQ-*.md in projects/{project}/ — Requirements specification
  • Extract: FR (compute/AI), NFR-P (performance), NFR-SEC (security), INT (integration), DR (data) requirements for Google Cloud service matching
  • If missing: STOP and report that $arckit-requirements must be run first
• ARC-000-PRIN-*.md in projects/000-global/ — Architecture principles
  • Extract: Cloud policy, approved services, compliance requirements, security standards
  • If missing: warn user to run $arckit-principles first

RECOMMENDED (read if available, note if missing):

• ARC-*-STKE-*.md in projects/{project}/ — Stakeholder analysis
  • Extract: User personas, scalability expectations, compliance stakeholders

OPTIONAL (read if available, skip silently if missing):

• ARC-*-RISK-*.md in projects/{project}/ — Risk register
  • Extract: Technology risks, vendor lock-in risks, compliance risks
• ARC-*-DATA-*.md in projects/{project}/ — Data model
  • Extract: Data storage needs, data governance, retention requirements

What to extract from each document:

• Requirements: FR/NFR/INT/DR IDs for Google Cloud service category mapping
• Principles: Cloud-first policy, approved platforms, compliance constraints
• Stakeholders: Scale expectations, compliance requirements

Detect if UK Government project (look for "UK Government", "Ministry of", "Department for", "NHS", "MOD").

Step 3: Read Template

• Read .arckit/templates/gcp-research-template.md for output structure

Step 4: Extract Requirements for Google Cloud Mapping

Read the requirements document and identify Google Cloud service needs across these categories. Use the MCP tools to dynamically discover the best-fit Google Cloud services for each requirement — do not limit yourself to the examples below:

• Compute (FR-xxx, NFR-P-xxx, NFR-S-xxx): e.g. GKE, Cloud Run, Compute Engine, Cloud Functions, App Engine
• Data (DR-xxx, NFR-P-xxx): e.g. Cloud SQL, Firestore, Cloud Spanner, BigQuery, Bigtable, Memorystore
• Integration (INT-xxx): e.g. Apigee, Pub/Sub, Cloud Tasks, Workflows, Eventarc
• Security (NFR-SEC-xxx): e.g. IAM, Secret Manager, VPC Service Controls, Cloud Armor, Security Command Center
• AI/ML (FR-xxx): e.g. Vertex AI, Gemini API, Document AI, Dialogflow CX

Use search_documents to discover which Google Cloud services match each requirement rather than assuming a fixed mapping. Google Cloud frequently launches new services and features — let the MCP documentation guide your recommendations.

Step 5: Research Google Cloud Services Using MCP

Mode detection: Attempt a single search_documents call. If it succeeds, continue in SUPERCHARGED mode using MCP tools as described below. If MCP tools are unavailable, switch to STANDALONE mode using these substitutions for ALL research in this step:

For each requirement category, use MCP tools extensively (or their STANDALONE equivalents):

Service Discovery:

• search_documents: "[requirement] Google Cloud service" for each category
• Follow up with get_document for detailed service pages

Service Deep Dive (for each identified service):

• get_document: Fetch full docs from cloud.google.com/[service-name]/docs
• Extract: features, pricing models, SLA, security features, integration capabilities
• Use batch_get_documents when fetching multiple related pages for a service

Architecture Patterns:

• search_documents: "Google Cloud architecture [pattern type]"
• get_document: Fetch Google Cloud Architecture Center reference architectures

Architecture Framework Assessment (all 6 pillars):

• search_documents: "Google Cloud Architecture Framework [pillar] [service]"
• Pillars: Operational Excellence, Security Privacy and Compliance, Reliability, Cost Optimization, Performance Optimization, Sustainability

Security Command Center Mapping:

• search_documents: "Security Command Center [finding category]"
• Categories: Vulnerability findings, Threat findings, Misconfiguration findings, Compliance findings (CIS Benchmark, PCI DSS, NIST 800-53)

Code Samples:

• search_documents: "Google Cloud [service] Terraform example", "Google Cloud [service] Deployment Manager template", "Google Cloud [service] [language]"

Step 6: UK Government Specific Research (if applicable)

• G-Cloud: Search Digital Marketplace for "Google Cloud", note framework reference
• Data Residency: Confirm europe-west2 (London) availability, check europe-west1 (Belgium) for DR
• Classification: OFFICIAL = standard Google Cloud, OFFICIAL-SENSITIVE = additional controls with VPC Service Controls, SECRET = not available on public Google Cloud (no Google Cloud Government in UK)
• NCSC: Reference Google Cloud attestation against 14 NCSC Cloud Security Principles
• Note: Google Cloud does not have a UK Government-specific sovereign cloud (unlike AWS GovCloud or Azure Government). For SECRET classification, Google Cloud is not suitable for UK Government projects.

Step 7: Cost Estimation

• search_documents: "Google Cloud [service] pricing" for each service
• Map requirements to service configurations
• Calculate based on projected usage with europe-west2 pricing
• Include optimization: Committed Use Discounts (CUDs) for 1yr/3yr, Sustained Use Discounts (SUDs) for consistent workloads, Spot VMs for fault-tolerant workloads, E2 machine types for cost-efficient compute, BigQuery flat-rate pricing for analytics

Step 7b: Government Implementation Patterns

Search govreposcrape for existing UK government implementations using the Google Cloud services recommended above:

1. Search by service: For each recommended Google Cloud service, query govreposcrape:
   • "[GCP service] UK government", "Google Cloud [service] implementation"
   • Example: "Cloud Run UK government", "BigQuery government"
   • Use resultMode: "snippets" and limit: 5 per query
2. Note findings: For each relevant result:
   • Which department/organisation uses this service
   • Architecture patterns observed (serverless, containerised, etc.)
   • Common configurations or companion services
3. Include in output: Add a "Government Precedent" subsection to each service recommendation:
   • If precedent found: "[Org] uses [service] for [purpose]" — adds confidence to recommendation
   • If no precedent found: "No UK government precedent identified" — note as a consideration (not a blocker)

If govreposcrape tools are unavailable, skip this step silently and proceed.

Step 8: Generate Architecture Diagram

Create a Mermaid diagram showing:

• Google Cloud services and relationships
• UK region placement (europe-west2 primary, europe-west1 DR)
• Network topology (VPC, subnets, Cloud NAT)
• Security boundaries (Firewall rules, Cloud Armor, VPC Service Controls)
• Data flows

Step 9: Detect Version and Determine Increment

Check if a previous version of this document exists in the project directory:

Use Glob to find existing projects/{project-dir}/research/ARC-{PROJECT_ID}-GCRS-*-v*.md files. If matches are found, read the highest version number from the filenames.

If no existing file: Use VERSION="1.0"

If existing file found:

1. Read the existing document to understand its scope (Google Cloud services researched, architecture patterns, recommendations made)
2. Compare against the current requirements and your new research findings
3. Determine version increment:
   • Minor increment (e.g., 1.0 → 1.1, 2.1 → 2.2): Use when the scope is unchanged — refreshed pricing, updated service features, corrected details, minor additions within existing categories
   • Major increment (e.g., 1.0 → 2.0, 1.3 → 2.0): Use when scope has materially changed — new requirement categories, removed categories, fundamentally different service recommendations, significant new requirements added since last version
4. Use the determined version for ALL subsequent references:
   • Document ID and filename: ARC-{PROJECT_ID}-GCRS-v${VERSION}.md
   • Document Control: Version field
   • Revision History: Add new row with version, date, "AI Agent", description of changes, "PENDING", "PENDING"

Before writing the file, read .arckit/references/quality-checklist.md and verify all Common Checks plus the GCRS per-type checks pass. Fix any failures before proceeding.

Step 10: Write Output

Use the Write tool to save the complete document to projects/{project-dir}/research/ARC-{PROJECT_ID}-GCRS-v${VERSION}.md following the template structure.

Auto-populate fields:

• [PROJECT_ID] from project path
• [VERSION] = determined version from Step 9
• [DATE] = current date (YYYY-MM-DD)
• [STATUS] = "DRAFT"
• [CLASSIFICATION] = "OFFICIAL" (UK Gov) or "PUBLIC"

Include the generation metadata footer:

**Generated by**: ArcKit `$arckit-gcp-research` agent
**Generated on**: {DATE}
**ArcKit Version**: {ArcKit version from context}
**Project**: {PROJECT_NAME} (Project {PROJECT_ID})
**AI Model**: {Actual model name}

DO NOT output the full document. Write it to file only.

Step 11: Return Summary

Return ONLY a concise summary including:

• Project name and file path created
• Google Cloud services recommended (table: category, service, configuration, monthly estimate)
• Architecture pattern used
• Security alignment (Security Command Center controls, Architecture Framework pillars)
• UK Government suitability (G-Cloud, europe-west2, classification)
• Estimated monthly cost
• What's in the document
• Next steps ($arckit-diagram, $arckit-secure, $arckit-devops)

Quality Standards

• Official Sources Only: Prefer Google Cloud documentation via MCP (SUPERCHARGED mode). If MCP is unavailable, use WebSearch/WebFetch targeting cloud.google.com (STANDALONE mode). Avoid third-party blogs in both modes
• UK Focus: Always check europe-west2 (London) availability
• Architecture Framework: Assess every recommendation against all 6 pillars
• Security Command Center: Map recommendations to SCC finding categories and CIS Benchmark for GCP
• Cost Accuracy: Use Google Cloud Pricing Calculator data where possible
• Code Samples: Prefer Terraform (primary) for IaC; note Deployment Manager is legacy

Edge Cases

• No requirements found: Stop, tell user to run $arckit-requirements
• Service not in europe-west2: Flag as a blocker for UK Government projects, suggest alternatives
• SECRET classification: Note that Google Cloud does not have a UK sovereign cloud — it is not suitable for SECRET classification in UK Government projects

Important Notes

• Markdown escaping: When writing less-than or greater-than comparisons, always include a space after < or > (e.g., < 3 seconds, > 99.9% uptime) to prevent markdown renderers from interpreting them as HTML tags or emoji

User Request

$ARGUMENTS

Suggested Next Steps

After completing this command, consider running:

• $arckit-diagram -- Create Google Cloud architecture diagrams
• $arckit-devops -- Design Cloud Build pipeline
• $arckit-finops -- Create Google Cloud cost management strategy
• $arckit-adr -- Record Google Cloud service selection decisions