Cloud Solution Architect

MicrosoftOfficialPublished by Microsoft

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MIT
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From the author

Transform the agent into a Cloud Solution Architect following Azure Architecture Center best practices. Use when designing cloud architectures, reviewing system designs, selecting architecture styles, applying cloud design patterns, making technology choices, or conducting Well-Architected Framework reviews.

— the description by Microsoft

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This skill configures an AI agent to act as a Cloud Solution Architect, providing structured guidance based on the Azure Architecture Center. It enables the agent to assist with designing production-grade cloud systems, selecting appropriate architecture styles, applying proven design patterns, and conducting systematic reviews against the Well-Architected Framework (WAF). The agent is equipped with knowledge of 10 core design principles, 44 cloud design patterns, and decision frameworks for technology selection, ensuring that architectural decisions are well-reasoned and aligned with industry-standard best practices.

What it does

Provides guidance on selecting architecture styles like N-tier, Microservices, and Event-driven.
Maps 44 cloud design patterns to WAF pillars for reliability, security, and performance.
Offers a decision framework for choosing compute, storage, data, and messaging technologies.
Conducts systematic architecture reviews using the Well-Architected Framework.
Identifies performance antipatterns and suggests remediation strategies.
Supports the creation of Architecture Decision Records (ADRs) for project documentation.

When to use it

When designing a new cloud-based application or system architecture.
When reviewing an existing system design for potential improvements or bottlenecks.
When evaluating technology choices for compute, data, or messaging services.
When performing a Well-Architected Framework review to ensure system quality.
When troubleshooting performance issues caused by common cloud antipatterns.

Advantages

Ensures architectural consistency by following official Azure best practices.
Facilitates informed decision-making through structured tradeoff analysis.
Promotes the use of resilient, scalable, and cost-effective design patterns.
Standardizes the documentation of architectural decisions via ADR templates.
Helps align technical choices with specific business requirements and SLAs.

Usage examples

“I am designing a high-traffic e-commerce platform. Which architecture style should I choose?”: The agent will compare styles like Microservices versus N-tier based on your team's autonomy, domain complexity, and scaling needs, recommending the best fit.
“Our application is experiencing performance issues under load. Can you help me identify potential antipatterns?”: The agent will review your current design against known performance antipatterns like 'Busy Database' or 'Chatty I/O' and suggest specific fixes.
“Help me conduct a Well-Architected Framework review for my current Azure deployment.”: The agent will guide you through a systematic assessment of your architecture across the five WAF pillars, identifying risks and improvement opportunities.
“We need to choose a messaging service for our event-driven system. What are the options?”: The agent will present a decision framework comparing Service Bus, Event Hubs, and Event Grid based on your specific throughput and ordering requirements.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/microsoft/cloud-solution-architect/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip cloud-solution-architect.zip -d .agents/skills

Documentation by Microsoft

Cloud Solution Architect

Overview

Design well-architected, production-grade cloud systems following Azure Architecture Center best practices. This skill provides:

10 design principles for Azure applications
6 architecture styles with selection guidance
44 cloud design patterns mapped to WAF pillars
Technology choice frameworks for compute, storage, data, messaging
Performance antipatterns to avoid
Architecture review workflow for systematic design validation

Ten Design Principles for Azure Applications

#	Principle	Key Tactics
1	Design for self-healing	Retry with backoff, circuit breaker, bulkhead isolation, health endpoint monitoring, graceful degradation
2	Make all things redundant	Eliminate single points of failure, use availability zones, deploy multi-region, replicate data
3	Minimize coordination	Decouple services, use async messaging, embrace eventual consistency, use domain events
4	Design to scale out	Horizontal scaling, autoscaling rules, stateless services, avoid session stickiness, partition workloads
5	Partition around limits	Data partitioning (shard/hash/range), respect compute & network limits, use CDNs for static content
6	Design for operations	Structured logging, distributed tracing, metrics & dashboards, runbook automation, infrastructure as code
7	Use managed services	Prefer PaaS over IaaS, reduce operational burden, leverage built-in HA/DR/scaling
8	Use an identity service	Microsoft Entra ID, managed identity, RBAC, avoid storing credentials, zero-trust principles
9	Design for evolution	Loose coupling, versioned APIs, backward compatibility, async messaging for integration, feature flags
10	Build for business needs	Define SLAs/SLOs, establish RTO/RPO targets, domain-driven design, cost modeling, composite SLAs

Architecture Styles

Style	Description	When to Use	Key Services
N-tier	Horizontal layers (presentation, business, data)	Traditional enterprise apps, lift-and-shift	App Service, SQL Database, VNets
Web-Queue-Worker	Web frontend → message queue → backend worker	Moderate-complexity apps with long-running tasks	App Service, Service Bus, Functions
Microservices	Small autonomous services, bounded contexts, independent deploy	Complex domains, independent team scaling	AKS, Container Apps, API Management
Event-driven	Pub/sub model, event producers/consumers	Real-time processing, IoT, reactive systems	Event Hubs, Event Grid, Functions
Big data	Batch + stream processing pipeline	Analytics, ML pipelines, large-scale data	Synapse, Data Factory, Databricks
Big compute	HPC, parallel processing	Simulations, modeling, rendering, genomics	Batch, CycleCloud, HPC VMs

Selection Criteria

Domain complexity → Microservices (high), N-tier (low-medium)
Team autonomy → Microservices (independent teams), N-tier (single team)
Data volume → Big data (TB+), others (GB)
Latency requirements → Event-driven (real-time), Web-Queue-Worker (tolerant)

Cloud Design Patterns

44 patterns organized by primary concern. WAF pillar mapping: R=Reliability, S=Security, CO=Cost Optimization, OE=Operational Excellence, PE=Performance Efficiency.

Messaging & Communication

Pattern	Summary	Pillars
Asynchronous Request-Reply	Decouple request/response with polling or callbacks	R, PE
Claim Check	Split large messages; store payload separately, pass reference	R, PE
Choreography	Services coordinate via events without central orchestrator	R, OE
Competing Consumers	Multiple consumers process messages from shared queue concurrently	R, PE
Messaging Bridge	Connect incompatible messaging systems	R, OE
Pipes and Filters	Decompose complex processing into reusable filter stages	R, OE
Priority Queue	Prioritize requests so higher-priority work is processed first	R, PE
Publisher/Subscriber	Decouple senders from receivers via topics/subscriptions	R, PE
Queue-Based Load Leveling	Buffer requests with a queue to smooth intermittent loads	R, PE
Sequential Convoy	Process related messages in order while allowing parallel groups	R, PE

Reliability & Resilience

Pattern	Summary	Pillars
Bulkhead	Isolate resources per workload to prevent cascading failure	R
Circuit Breaker	Stop calling a failing service; fail fast to protect resources	R
Compensating Transaction	Undo previously committed steps when a later step fails	R
Health Endpoint Monitoring	Expose health checks for load balancers and orchestrators	R, OE
Leader Election	Coordinate distributed instances by electing a leader	R
Retry	Handle transient faults by retrying with exponential backoff	R
Saga	Manage data consistency across microservices with compensating transactions	R
Scheduler Agent Supervisor	Coordinate distributed actions with retry and failure handling	R

Data Management

Pattern	Summary	Pillars
Cache-Aside	Load data on demand into cache from data store	PE
CQRS	Separate read and write models for independent scaling	PE, R
Event Sourcing	Store state as append-only sequence of domain events	R, OE
Index Table	Create indexes over frequently queried fields in data stores	PE
Materialized View	Pre-compute views over data for efficient queries	PE
Sharding	Distribute data across partitions for scale and performance	PE, R
Static Content Hosting	Serve static content from cloud storage/CDN directly	PE, CO
Valet Key	Grant clients limited direct access to storage resources	S, PE

Design & Structure

Pattern	Summary	Pillars
Ambassador	Offload cross-cutting concerns to a helper sidecar proxy	OE
Anti-Corruption Layer	Translate between new and legacy system models	OE, R
Backends for Frontends	Create separate backends per frontend type (mobile, web, etc.)	OE, PE
Compute Resource Consolidation	Combine multiple workloads into fewer compute instances	CO
External Configuration Store	Externalize configuration from deployment packages	OE
Sidecar	Deploy helper components alongside the main service	OE
Strangler Fig	Incrementally migrate legacy systems by replacing pieces	OE, R

Security & Access

Pattern	Summary	Pillars
Federated Identity	Delegate authentication to an external identity provider	S
Gatekeeper	Protect services using a dedicated broker that validates requests	S
Quarantine	Isolate and validate external assets before allowing use	S
Rate Limiting	Control consumption rate of resources by consumers	R, S
Throttling	Control resource consumption to sustain SLAs under load	R, PE

Deployment & Scaling

Pattern	Summary	Pillars
Deployment Stamps	Deploy multiple independent copies of application components	R, PE
Edge Workload Configuration	Configure workloads differently across diverse edge devices	OE
Gateway Aggregation	Aggregate multiple backend calls into a single client request	PE
Gateway Offloading	Offload shared functionality (SSL, auth) to a gateway	OE, S
Gateway Routing	Route requests to multiple backends using a single endpoint	OE
Geode	Deploy backends to multiple regions for active-active serving	R, PE

See Design Patterns Reference for detailed implementation guidance.

Technology Choices

Decision Framework

For each technology area, evaluate: requirements → constraints → tradeoffs → select.

Area	Key Options	Selection Criteria
Compute	App Service, Functions, Container Apps, AKS, VMs, Batch	Hosting model, scaling, cost, team skills
Storage	Blob Storage, Data Lake, Files, Disks, Managed Lustre	Access patterns, throughput, cost tier
Data stores	SQL Database, Cosmos DB, PostgreSQL, Redis, Table Storage	Consistency model, query patterns, scale
Messaging	Service Bus, Event Hubs, Event Grid, Queue Storage	Ordering, throughput, pub/sub vs queue
Networking	Front Door, Application Gateway, Load Balancer, Traffic Manager	Global vs regional, L4 vs L7, WAF
AI services	Azure OpenAI, AI Search, AI Foundry, Document Intelligence	Model needs, data grounding, orchestration
Containers	Container Apps, AKS, Container Instances	Operational control vs simplicity

See Technology Choices Reference for detailed decision trees.

Best Practices

Practice	Key Guidance
API design	RESTful conventions, resource-oriented URIs, HATEOAS, versioning via URL path or header
API implementation	Async operations, pagination, idempotent PUT/DELETE, content negotiation, ETag caching
Autoscaling	Scale on metrics (CPU, queue depth, custom), cool-down periods, predictive scaling, scale-in protection
Background jobs	Use queues or scheduled triggers, idempotent processing, poison message handling, graceful shutdown
Caching	Cache-aside pattern, TTL policies, cache invalidation strategies, distributed cache for multi-instance
CDN	Static asset offloading, cache-busting with versioned URLs, geo-distribution, HTTPS enforcement
Data partitioning	Horizontal (sharding), vertical, functional partitioning; partition key selection for even distribution
Partitioning strategies	Hash-based, range-based, directory-based; rebalancing approach, cross-partition query avoidance
Host name preservation	Preserve original host header through proxies/gateways for cookies, redirects, auth flows
Message encoding	Schema evolution (Avro/Protobuf), backward/forward compatibility, schema registry
Monitoring & diagnostics	Structured logging, distributed tracing (W3C Trace Context), metrics, alerts, dashboards
Transient fault handling	Retry with exponential backoff + jitter, circuit breaker, idempotency keys, timeout budgets

See Best Practices Reference for implementation details.

Performance Antipatterns

Avoid these common patterns that degrade performance under load:

Antipattern	Problem	Fix
Busy Database	Offloading too much processing to the database	Move logic to application tier, use caching
Busy Front End	Resource-intensive work on frontend request threads	Offload to background workers/queues
Chatty I/O	Many small I/O requests instead of fewer large ones	Batch requests, use bulk APIs, buffer writes
Extraneous Fetching	Retrieving more data than needed	Project only required fields, paginate, filter server-side
Improper Instantiation	Recreating expensive objects per request	Use singletons, connection pooling, HttpClientFactory
Monolithic Persistence	Single data store for all data types	Polyglot persistence — right store for each workload
No Caching	Repeatedly fetching unchanged data	Cache-aside pattern, CDN, output caching, Redis
Noisy Neighbor	One tenant consuming all shared resources	Bulkhead isolation, per-tenant quotas, throttling
Retry Storm	Aggressive retries overwhelming a recovering service	Exponential backoff + jitter, circuit breaker, retry budgets
Synchronous I/O	Blocking threads on I/O operations	Async/await, non-blocking I/O, reactive streams

Mission-Critical Design

For workloads targeting 99.99%+ SLO, address these design areas:

Design Area	Key Considerations
Application platform	Multi-region active-active, availability zones, Container Apps or AKS with zone redundancy
Application design	Stateless services, idempotent operations, graceful degradation, bulkhead isolation
Networking	Azure Front Door (global LB), DDoS Protection, private endpoints, redundant connectivity
Data platform	Multi-region Cosmos DB, zone-redundant SQL, async replication, conflict resolution
Deployment & testing	Blue-green deployments, canary releases, chaos engineering, automated rollback
Health modeling	Composite health scores, dependency health tracking, automated remediation, SLI dashboards
Security	Zero-trust, managed identity everywhere, key rotation, WAF policies, threat modeling
Operational procedures	Automated runbooks, incident response playbooks, game days, postmortems

See Mission-Critical Reference for detailed guidance.

Well-Architected Framework (WAF) Pillars

Every architecture decision should be evaluated against all five pillars:

Pillar	Focus	Key Questions
Reliability	Resiliency, availability, disaster recovery	What is the RTO/RPO? How does it handle failures? Is there redundancy?
Security	Threat protection, identity, data protection	Is identity managed? Is data encrypted? Are there network controls?
Cost Optimization	Cost management, efficiency, right-sizing	Is compute right-sized? Are there reserved instances? Is there waste?
Operational Excellence	Monitoring, deployment, automation	Is deployment automated? Is there observability? Are there runbooks?
Performance Efficiency	Scaling, load testing, performance targets	Can it scale horizontally? Are there performance baselines? Is caching used?

WAF Tradeoff Matrix

Optimizing for...	May impact...
Reliability (redundancy)	Cost (more resources)
Security (isolation)	Performance (added latency)
Cost (consolidation)	Reliability (shared failure domains)
Performance (caching)	Cost (cache infrastructure), Reliability (stale data)

Architecture Review Workflow

When reviewing or designing a system, follow this structured approach:

Step 1: Identify Requirements

Functional: What must the system do?
Non-functional:
  - Availability target (e.g., 99.9%, 99.99%)
  - Latency requirements (p50, p95, p99)
  - Throughput (requests/sec, messages/sec)
  - Data residency and compliance
  - Recovery targets (RTO, RPO)
  - Cost constraints

Step 2: Select Architecture Style

Match requirements to architecture style using the selection criteria table above.

Step 3: Choose Technology Stack

Use the technology choices decision framework. Prefer managed services (PaaS) over IaaS.

Step 4: Apply Design Patterns

Select relevant patterns from the 44 cloud design patterns based on identified concerns.

Step 5: Address Cross-Cutting Concerns

Identity & access — Microsoft Entra ID, managed identity, RBAC
Monitoring — Application Insights, Azure Monitor, Log Analytics
Security — Network segmentation, encryption at rest/in transit, Key Vault
CI/CD — GitHub Actions, Azure DevOps Pipelines, infrastructure as code

Step 6: Validate Against WAF Pillars

Review each pillar systematically. Document tradeoffs explicitly.

Step 7: Document Decisions

Use Architecture Decision Records (ADRs):

# ADR-NNN: [Decision Title]

## Status: [Proposed | Accepted | Deprecated]

## Context
[What is the issue we're addressing?]

## Decision
[What did we decide and why?]

## Consequences
[What are the positive and negative impacts?]

References

Design Patterns Reference — Detailed pattern implementations
Technology Choices Reference — Decision trees for Azure services
Best Practices Reference — Implementation guidance
Mission-Critical Reference — High-availability design

Source

Content derived from the Azure Architecture Center — Microsoft's official guidance for cloud solution architecture on Azure. Covers design principles, architecture styles, cloud design patterns, technology choices, best practices, performance antipatterns, mission-critical design, and the Well-Architected Framework.

Files

ATTRIBUTION.mdAdded by AutoGineer879 B
LICENSELicence file added by AutoGineer1.1 KiB
SKILL.md17.1 KiB
references/architecture-styles.md15.0 KiB
references/best-practices.md11.5 KiB
references/design-patterns.md37.9 KiB
references/design-principles.md16.5 KiB
references/mission-critical.md14.7 KiB
references/performance-antipatterns.md7.6 KiB
references/technology-choices.md7.6 KiB

references/performance-antipatterns.md

Performance Antipatterns

Ten common performance antipatterns in cloud applications and how to resolve them.

1. Busy Database

Description: Offloading too much processing to a data store. Using stored procedures for formatting, string manipulation, or complex calculations that belong in the application tier.

Why it happens:

Database viewed as a service rather than a repository
Developers write queries that format data for direct display
Attempts to correct Extraneous Fetching by pushing compute to the database

Symptoms:

Disproportionate decline in throughput and response times for database operations

How to detect:

Performance monitoring of database activity
Examine work performed during slow periods

How to fix:

Move processing (formatting, string manipulation, calculations) to application tiers
Limit the database to data access operations (aggregation is acceptable)
Don't move processing out if it causes the database to transfer far more data (Extraneous Fetching)

Example scenario: A SQL query performs XML formatting, string concatenation, and locale-specific formatting in T-SQL instead of returning raw data and letting application code handle presentation.

2. Busy Front End

Description: Moving resource-intensive tasks onto foreground/UI threads instead of background threads.

Why it happens:

Processing done synchronously in request handlers

Symptoms:

High latency on requests
Poor user responsiveness

How to detect:

Monitor thread utilization and CPU usage on the front-end tier

How to fix:

Move resource-intensive tasks to background threads or services (Azure Functions, WebJobs)

Example scenario: A web API controller performs image resizing synchronously within the HTTP request pipeline, blocking the thread and causing timeouts for other users during peak load.

3. Chatty I/O

Description: Continually sending many small network requests instead of fewer larger ones.

Why it happens:

Following object-oriented patterns that make many small calls
Individual property gets instead of batch reads

Symptoms:

High number of I/O operations
High latency due to network round trips

How to detect:

Monitor the number of I/O requests and their sizes

How to fix:

Bundle multiple smaller requests into fewer larger ones
Use caching to avoid repeated calls
Read data in bulk

Example scenario: An application retrieves a user profile by making separate API calls for name, email, address, and preferences instead of a single call that returns the complete profile object.

4. Extraneous Fetching

Description: Retrieving more data than needed, resulting in unnecessary I/O and memory consumption.

Why it happens:

SELECT * queries
Fetching all columns or rows when only a subset is needed

Symptoms:

High memory usage
Excessive bandwidth consumption

How to detect:

Profile queries and check data transfer sizes

How to fix:

Request only needed data columns and rows
Use pagination and projections

Example scenario: An order history page runs SELECT * FROM Orders and filters in application code, transferring millions of rows when the user only sees the 20 most recent orders.

5. Improper Instantiation

Description: Repeatedly creating and destroying objects designed to be shared and reused, such as HttpClient, database connections, or service clients.

Why it happens:

Not understanding object lifecycle
Creating new instances per request

Symptoms:

Port exhaustion
Socket exhaustion
Connection pool depletion

How to detect:

Monitor connection counts, socket usage, and object creation rates

How to fix:

Use singleton or static instances for shared clients
Use connection pooling
Use IHttpClientFactory for HTTP clients

Example scenario: A web application creates a new HttpClient instance for every incoming request. Under load, available sockets are exhausted and requests fail with SocketException errors.

6. Monolithic Persistence

Description: Using one data store for data with very different usage patterns.

Why it happens:

Simplicity of a single database
Legacy design decisions

Symptoms:

Performance degradation as different workloads compete for the same resources

How to detect:

Monitor data store metrics and identify competing access patterns

How to fix:

Separate data by usage pattern into appropriate stores (hot/warm/cold)
Apply polyglot persistence — use the right store for each data type

Example scenario: A single SQL database handles both high-throughput transactional writes and complex analytical reporting queries, causing lock contention that degrades both workloads.

7. No Caching

Description: Failing to cache frequently accessed data that changes infrequently.

Why it happens:

Not considering a caching strategy
Assuming the database can handle all read traffic

Symptoms:

Repeated identical queries
High database load
Slow response times

How to detect:

Monitor cache hit ratios and look for repeated identical queries

How to fix:

Implement the Cache-Aside pattern
Use Azure Cache for Redis
Set appropriate TTLs based on data volatility

Example scenario: A product catalog page queries the database on every page load for data that only changes once a day, generating thousands of identical queries per hour.

8. Noisy Neighbor

Description: A single tenant consumes a disproportionate amount of shared resources, starving other tenants.

Why it happens:

Multi-tenant systems without resource isolation or throttling

Symptoms:

Other tenants experience degraded performance
Resource starvation for well-behaved tenants

How to detect:

Monitor per-tenant resource usage and identify outliers

How to fix:

Implement tenant isolation, throttling, and quotas
Use the Bulkhead pattern to partition resources
Separate compute per tenant if needed

Example scenario: In a shared SaaS platform, one tenant runs a bulk data import that saturates the database connection pool, causing timeout errors for all other tenants.

9. Retry Storm

Description: Retrying failed requests too aggressively, amplifying failures during outages.

Why it happens:

Aggressive retry policies without backoff
All clients retry simultaneously (thundering herd)

Symptoms:

Cascading failures
Overwhelming services that are trying to recover

How to detect:

Monitor retry rates and correlate with service recovery time

How to fix:

Use exponential backoff with jitter
Implement the Circuit Breaker pattern
Cap retry attempts

Example scenario: A downstream service goes offline for 30 seconds. Hundreds of clients immediately retry every 100ms with no backoff, generating 10x normal traffic and preventing the service from recovering for several minutes.

10. Synchronous I/O

Description: Blocking the calling thread while I/O completes.

Why it happens:

Using synchronous APIs for network or disk operations

Symptoms:

Thread pool exhaustion
Poor scalability under load
UI freezing in client applications

How to detect:

Monitor thread pool usage and identify blocking calls

How to fix:

Use async/await patterns
Use non-blocking I/O and async APIs

Example scenario: A web API makes synchronous HTTP calls to three downstream services sequentially. Under load, all thread pool threads are blocked waiting for responses, and the server can no longer accept new requests.

Source: Azure Architecture Center

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

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Attribution and licence

Published by

Microsoft

Publisher

Microsoft (Official)

Source

microsoft/skills · .github/skills/cloud-solution-architect · commit 0696600

Licence

MIT License (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-08

AutoGineer is not affiliated with or endorsed by Microsoft.

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From the author

AutoGineer summary

What it does

When to use it

Advantages

Usage examples

Install

Documentation by Microsoft

Cloud Solution Architect

Overview

Ten Design Principles for Azure Applications

Architecture Styles

Selection Criteria

Cloud Design Patterns

Messaging & Communication

Reliability & Resilience

Data Management

Design & Structure

Security & Access

Deployment & Scaling

Technology Choices

Decision Framework

Best Practices

Performance Antipatterns

Mission-Critical Design

Well-Architected Framework (WAF) Pillars

WAF Tradeoff Matrix

Architecture Review Workflow

Step 1: Identify Requirements

Step 2: Select Architecture Style

Step 3: Choose Technology Stack

Step 4: Apply Design Patterns

Step 5: Address Cross-Cutting Concerns

Step 6: Validate Against WAF Pillars

Step 7: Document Decisions

References

Source

Files

references/performance-antipatterns.md

Performance Antipatterns

1. Busy Database

2. Busy Front End

3. Chatty I/O

4. Extraneous Fetching

5. Improper Instantiation

6. Monolithic Persistence

7. No Caching

8. Noisy Neighbor

9. Retry Storm

10. Synchronous I/O

Requirements and compatibility

Security

Attribution and licence

Related skills

Setup TS Deep Modules

To Spec

Improve Codebase Architecture

Domain Modeling

Codebase Design

Prototype

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