rade-nugrohorade-nugroho/Agentic-Enteprises-Skill/applied-ai-architect-commercial-enterprise/SKILL.md

applied-ai-architect-commercial-enterprise

Skill9 starsChanged 6 months agoMITView on GitHub

Guides applied AI solution architecture for commercial (B2B SaaS, multi-tenant product) and enterprise (internal copilots, regulated corp data) contexts—reference patterns for RAG, agents, and copilots, platform and model selection, data boundaries, identity, observability, cost at scale, and POC-to-production hardening with security and governance gates. Use when designing customer-facing AI features, enterprise knowledge assistants, vendor LLM integration architecture, AI ADRs for commercial or IT stakeholders, or mapping AI components to SSO/DLP/residency requirements—not for implementing RAG code (ai-engineer), generic multi-service ADRs without AI (senior-system-architecture), AI policy registers (ai-risk-governance), or contract redlines (commercial-counsel). For engineering management of vertical AI product squads (hiring, roadmap, launch governance), use engineering-manager-vertical-ai-products. Labeling PM: product-management-human-data-platform—not this skill.

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.

≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.

Common
Distinctive
Very specific

Very specific:
Write application code, prompts, or eval harnesses → ai-engineer, prompt-engineer
line 32

≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.

Files

SKILL.md

Applied AI Architect — Commercial & Enterprise

When to Use

Design end-to-end AI architecture for a B2B product feature or internal copilot
Choose between RAG, fine-tuning, agents, or workflow orchestration for a use case
Define multi-tenant isolation, data boundaries, and customer data handling for AI
Map enterprise requirements: SSO, audit logs, residency, DLP, human oversight
Compare cloud AI platforms (managed endpoints, private networking, quotas)
Author AI-specific ADRs for engineering, security, and procurement
Plan POC → pilot → production with eval, safety, and ops gates

When NOT to Use

Write application code, prompts, or eval harnesses → ai-engineer, prompt-engineer
General non-AI integration ADRs → senior-system-architecture
Context packing and token budgets → ai-context-engineer
Memory store implementation → ai-memory-developer
AI risk tiering and policy documents → ai-risk-governance
SOC 2 evidence packs → compliance-engineer
Token cost improvement program → ai-token-improvement-plan-engineer
AI ops cadence and release governance → ai-lead-ops
Business case and operating model → business-consultant

Related skills

Need	Skill
Build and ship RAG/agents	`ai-engineer`
Cross-service platform ADRs	`senior-system-architecture`
Governance and use-case classification	`ai-risk-governance`
Security architecture and controls	`cybersecurity`, `information-security-engineer`
Data platform for embeddings/warehouse	`data-architect`
Product tenant isolation	`product-infrastructure-security-engineer`
Red-team before launch	`ai-redteam`
Commercial contract terms	`commercial-counsel`

Core Workflows

1. Classify context: commercial vs enterprise

Lens	Commercial (product)	Enterprise (internal IT)
Users	Customer tenants	Employees, partners
Data	Customer content + config	Corp IP, HR, regulated data
Identity	Product auth + tenant ID	IdP (Entra/Okta), groups
Isolation	Strong multi-tenancy	Network/VPC, private endpoints
Buying	SKU, usage metering	EA, private offer, on-prem option

See references/commercial_vs_enterprise.md.

2. Shape the solution

Job — user outcome in one sentence; success metric
Pattern — Q&A RAG, task agent, workflow automation, codegen assist
Data — sources, freshness, PII, retention, who may see what
Model — capability vs cost; routing; fallback
Control plane — prompts, tools, policies, human approval points
Plane separation — ingestion ≠ inference ≠ logging (scale independently)

See references/reference_architectures.md.

3. Non-functional requirements

Document targets for:

Latency (p95 first token, end-to-end task)
Availability and DR for inference path
Cost per tenant/session and guardrails
Auditability (prompt, retrieval IDs, model version—no secrets in logs)
Safety (tier, blocked topics, escalation)
Compliance (residency, retention, subprocessors)

See references/enterprise_nfr_integration.md.

4. Platform and vendor selection

Score options on: data handling, private link, model catalog, SLAs, metering, exit strategy.

See references/platform_selection.md.

5. AI ADR and review

For one-way doors (vendor, data leaves region, autonomous agents):

Options, NFR impact, security, cost model
Migration and rollback

See references/ai_architecture_decision.md.

Pair with senior-system-architecture for org-wide integration standards.

6. POC → production path

Stage	Architecture focus
POC	Single tenant, manual eval, no prod data
Pilot	Tenant isolation, observability, limited users
GA	SLOs, rate limits, on-call, eval in CI, red-team for tier-2+

See references/poc_to_production.md.

When to load references

Commercial vs enterprise → references/commercial_vs_enterprise.md
RAG, agent, copilot patterns → references/reference_architectures.md
SSO, DLP, logging, residency → references/enterprise_nfr_integration.md
Cloud AI platform choice → references/platform_selection.md
AI ADR template → references/ai_architecture_decision.md
Production hardening → references/poc_to_production.md

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Other versions1 project uses a version of this.
Word for word · 1
daemon-blockint-tech/Agentic-Enteprises-Skill★ 8100% same
Edited · 0
None found.
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
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