
software-architecture-design
by vasilyu1983
SKILL.md
name: software-architecture-design description: System design, architecture patterns, scalability tradeoffs, and distributed systems for production-grade software. Covers microservices, event-driven, CQRS, modular monoliths, and reliability patterns.
Software Architecture Design — Quick Reference
Use this skill for system-level design decisions rather than implementation details within a single service or component.
Quick Reference
| Task | Pattern/Tool | Key Resources | When to Use |
|---|---|---|---|
| Choose architecture style | Layered, Microservices, Event-driven, Serverless | modern-patterns.md | Greenfield projects, major refactors |
| Design for scale | Load balancing, Caching, Sharding, Read replicas | scalability-reliability-guide.md | High-traffic systems, performance goals |
| Ensure resilience | Circuit breakers, Retries, Bulkheads, Graceful degradation | modern-patterns.md | Distributed systems, external dependencies |
| Document decisions | Architecture Decision Record (ADR) | adr-template.md | Major technical decisions, tradeoff analysis |
| Define service boundaries | Domain-Driven Design (DDD), Bounded contexts | microservices-template.md | Microservices decomposition |
| Model data consistency | ACID vs BASE, Event sourcing, CQRS, Saga patterns | event-driven-template.md | Multi-service transactions |
| Plan observability | SLIs/SLOs/SLAs, Distributed tracing, Metrics, Logs | architecture-blueprint.md | Production readiness |
When to Use This Skill
Invoke when working on:
- System decomposition: Deciding between monolith, modular monolith, microservices
- Architecture patterns: Event-driven, CQRS, layered, hexagonal, serverless
- Data architecture: Consistency models, sharding, replication, CQRS patterns
- Scalability design: Load balancing, caching strategies, database scaling
- Resilience patterns: Circuit breakers, retries, bulkheads, graceful degradation
- API contracts: Service boundaries, versioning, integration patterns
- Architecture decisions: ADRs, tradeoff analysis, technology selection
Decision Tree: Choosing Architecture Pattern
Project needs: [New System or Major Refactor]
├─ Single team, evolving domain?
│ ├─ Start simple → Modular Monolith (clear module boundaries)
│ └─ Need rapid iteration → Layered Architecture
│
├─ Multiple teams, clear bounded contexts?
│ ├─ Independent deployment critical → Microservices
│ └─ Shared data model → Modular Monolith with service modules
│
├─ Event-driven workflows?
│ ├─ Asynchronous processing → Event-Driven Architecture (Kafka, queues)
│ └─ Complex state machines → Saga pattern + Event Sourcing
│
├─ Variable/unpredictable load?
│ ├─ Pay-per-use model → Serverless (AWS Lambda, Cloudflare Workers)
│ └─ Batch processing → Serverless + queues
│
└─ High consistency requirements?
├─ Strong ACID guarantees → Monolith or Modular Monolith
└─ Distributed data → CQRS + Event Sourcing
Decision Factors:
- Team size threshold: <10 developers → modular monolith typically outperforms microservices (operational overhead)
- Team structure (Conway's Law) — architecture mirrors org structure
- Deployment independence needs
- Consistency vs availability tradeoffs (CAP theorem)
- Operational maturity (monitoring, orchestration)
Industry Data (CNCF 2025): 42% of organizations that adopted microservices have consolidated at least some services back into larger deployable units. Primary drivers: debugging complexity, operational overhead, network latency.
See references/modern-patterns.md for detailed pattern descriptions.
Modern Architecture Patterns (Jan 2026)
Data Mesh Architecture
Use when data silos impede cross-functional analytics.
Principles:
- Domain-oriented data ownership
- Data as a product
- Self-serve data platform
- Federated computational governance
| Do | Avoid |
|---|---|
| Assign data ownership to domain teams | Centralized data lake without ownership |
| Publish data with SLAs and documentation | Schema changes without consumer notification |
| Use standard interfaces (APIs, SQL) | Proprietary formats without discoverability |
Composable Architecture
Use when business demands rapid capability assembly.
Characteristics:
- Packaged business capabilities (PBCs)
- API-first integration
- Low-code/no-code composition layer
- Event-driven coordination
| Do | Avoid |
|---|---|
| Design components with clear contracts | Tightly coupled monolithic modules |
| Use standard protocols (REST, GraphQL, gRPC) | Custom integration patterns |
| Enable runtime composition | Build-time-only assembly |
Continuous Architecture
Architecture evolves with software, not separate from it.
Practices:
- Just-enough upfront design
- Delay decisions to responsible moment
- Architect roles on delivery teams
- Architecture fitness functions (automated checks)
Edge Computing Patterns
Use when latency or bandwidth constraints require local processing.
| Pattern | Use Case |
|---|---|
| Edge gateway | Protocol translation, local caching |
| Edge compute workloads | Validation, transforms, local control loops |
| Edge-cloud hybrid | Local processing, cloud aggregation |
Platform Engineering (2026)
Internal developer platforms (IDPs) for self-service infrastructure. By 2026, 80% of large software engineering organizations will have platform teams (Gartner).
IDP Stack:
| Component | Tools | Purpose |
|---|---|---|
| Portal | Backstage (89% market share), Port | Service catalog, tech docs |
| Golden paths | Scaffolder templates | Standardized project creation |
| Infrastructure | Terraform, Crossplane | Self-service provisioning |
| AI agents | First-class citizens with RBAC | Automated workflows |
FinOps Integration: Platforms now implement pre-deployment cost gates that block services exceeding unit-economic thresholds.
Unified Delivery: Single pipeline for app developers, ML engineers, and data scientists.
Optional: AI/Automation Extensions
Note: This section covers AI-specific architectural patterns. Skip if building traditional systems.
RAG Architecture Patterns
Retrieval-Augmented Generation for enterprise AI.
| Component | Purpose |
|---|---|
| Vector store | Embedding storage (Pinecone, Weaviate, pgvector) |
| Retriever | Semantic search over documents |
| Generator | LLM produces responses with context |
| Orchestrator | Chains retrieval and generation |
Google's 8 Multi-Agent Design Patterns (Jan 2026)
The agentic AI field is experiencing its "microservices revolution" — single all-purpose agents are being replaced by orchestrated teams of specialized agents.
Three foundational execution patterns: Sequential, Loop, Parallel
| Pattern | Description | Use Case |
|---|---|---|
| Sequential Pipeline | Agents in assembly line, output → next input | Document processing, ETL |
| Parallel Fan-out | Concurrent agent execution, results merged | Multi-source research |
| Loop/Iterative | Agent refines until condition met | Code review, optimization |
| Hierarchical | Manager delegates to worker agents | Complex task decomposition |
| Bidding/Auction | Agents compete for task assignment | Resource allocation |
| Human-in-the-loop | Approval gates for critical decisions | High-stakes workflows |
| Reflection | Agent critiques and improves own output | Quality assurance |
| Tool Use | Agent selects and invokes external tools | API integration |
Anti-patterns:
- Unbounded agent loops without termination conditions
- Missing human-in-the-loop for critical decisions
- No observability into agent actions and reasoning
- Single monolithic agent trying to do everything
Agent Communication Protocols
| Protocol | Purpose | Standard |
|---|---|---|
| MCP (Model Context Protocol) | LLM-to-data source connection | Anthropic open standard |
| A2A (Agent-to-Agent) | Inter-agent communication at scale | Google Cloud Agent Engine |
MCP enables: Agents access external data (databases, APIs, file systems) through standardized interfaces.
A2A enables: Cross-system agent orchestration, discovery, and collaboration between agents from different platforms.
Navigation
Core Resources
- references/modern-patterns.md — 10 contemporary architecture patterns with decision trees (microservices, event-driven, serverless, CQRS, modular monolith, service mesh, edge computing)
- references/scalability-reliability-guide.md — CAP theorem, database scaling, caching strategies, circuit breakers, SRE patterns, observability
- data/sources.json — 60 curated external resources (AWS, Azure, Google Cloud, Martin Fowler, microservices.io, SRE books, multi-agent patterns, MCP/A2A protocols, platform engineering 2026)
Templates
Planning & Documentation (assets/planning/):
- assets/planning/architecture-blueprint.md — Service blueprint template (dependencies, SLAs, data flows, resilience, security, observability)
- assets/planning/adr-template.md — Architecture Decision Record (ADR) for documenting design decisions with tradeoff analysis
Architecture Patterns (assets/patterns/):
- assets/patterns/microservices-template.md — Complete microservices design template (API contracts, resilience, deployment, testing, cost optimization)
- assets/patterns/event-driven-template.md — Event-driven architecture template (event schemas, saga patterns, event sourcing, schema evolution)
Operations & Scalability (assets/operations/):
- assets/operations/scalability-checklist.md — Comprehensive scalability checklist (database scaling, caching, load testing, auto-scaling, DR)
Related Skills
Implementation Details:
- ../software-backend/SKILL.md — Backend engineering, API implementation, data layer
- ../software-frontend/SKILL.md — Frontend architecture, micro-frontends, state management
- ../dev-api-design/SKILL.md — REST, GraphQL, gRPC design patterns
Reliability & Operations:
Security & Data:
- ../software-security-appsec/SKILL.md — Threat modeling, authentication, authorization, secure design
Quality & Code:
- ../software-code-review/SKILL.md — Code review practices, architectural review
Documentation:
- ../docs-codebase/SKILL.md — Architecture documentation, C4 diagrams, ADRs
Trend Awareness Protocol
IMPORTANT: When users ask recommendation questions about software architecture, you MUST use WebSearch to check current trends before answering.
Trigger Conditions
- "What's the best architecture for [use case]?"
- "What should I use for [microservices/serverless/event-driven]?"
- "What's the latest in system design?"
- "Current best practices for [scalability/resilience/observability]?"
- "Is [architecture pattern] still relevant in 2026?"
- "[Monolith] vs [microservices] vs [modular monolith]?"
- "Best approach for [distributed systems/data consistency]?"
Required Searches
- Search:
"software architecture best practices 2026" - Search:
"[microservices/serverless/event-driven] architecture 2026" - Search:
"system design patterns 2026" - Search:
"[specific pattern] vs alternatives 2026"
What to Report
After searching, provide:
- Current landscape: What architecture patterns are popular NOW
- Emerging trends: New patterns gaining traction (AI-native, edge)
- Deprecated/declining: Approaches that are losing relevance
- Recommendation: Based on fresh data and real-world case studies
Example Topics (verify with fresh search)
- Modular monolith renaissance
- AI-native architecture patterns
- Edge computing and CDN-first design
- Event-driven microservices evolution
- Platform engineering and internal developer platforms
- Observability-driven development
Operational Playbooks
Shared Foundation
- ../software-clean-code-standard/references/clean-code-standard.md - Canonical clean code rules (
CC-*) for citation - Legacy playbook: ../software-clean-code-standard/references/code-quality-operational-playbook.md -
RULE-01–RULE-13, operational procedures, and design patterns - ../software-clean-code-standard/references/design-patterns-operational-checklist.md - GoF pattern triggers and guardrails, when to apply vs avoid patterns
Architecture-Specific
- references/operational-playbook.md — Detailed architecture questions, decomposition patterns, security layers, and external references
Score
Total Score
Based on repository quality metrics
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