Operations
AuraScore 83/100

Microservice Decomposition and Architectural Risk Matrix

Assess monolith boundaries and shared dependencies to build an operational decoupling risk matrix.

Use this template when planning the operational extraction of domain services from a monolithic codebase. It generates a multi-dimensional risk matrix balancing data consistency, transaction boundaries, and team topology.

Template

Role: Principal Enterprise Systems Architect and Technical Operations Lead

Context

  • Existing Monolith Dependency Map: {{monolith_dependency_graph}}
  • Core Transaction and Domain Boundaries: {{critical_transaction_boundaries}}
  • Current State Persistence and Consistency Models: {{data_consistency_models}}
  • Engineering Squad Ownership and Topology: {{team_topologies_ownership}}
  • Latency and Throughput Thresholds: {{latency_sla_thresholds}}
  • Permissible Migration Risk Budget: {{migration_risk_budget}}

Task

Develop a Microservice Decomposition and Architectural Risk Matrix that systematically evaluates extraction feasibility, runtime blast radius, and migration trade-offs for isolating legacy monolithic modules into discrete operational services.

Method

  1. Analyze {{monolith_dependency_graph}} to isolate coupled runtime dependencies, shared database tables, and cyclic references.
  2. Cross-reference domain components against {{critical_transaction_boundaries}} to evaluate the impact of replacing ACID transactions with eventual consistency.
  3. Assess {{data_consistency_models}} to identify required dual-write, outbox pattern, or CDC (Change Data Capture) requirements.
  4. Map extraction candidates against {{team_topologies_ownership}} to align architectural boundaries with Conway's Law.
  5. Project network overhead and serialization latency to ensure adherence to {{latency_sla_thresholds}}.
  6. Evaluate operational complexity (observability, distributed tracing, network failure modes) within {{migration_risk_budget}}.
  7. Construct a prioritized decoupling matrix ranking service extraction targets by business value and operational difficulty.

Constraints

  • MUST NOT recommend microservice extraction for domains lacking clear transactional autonomy or single-team ownership.
  • Eventual consistency mechanisms MUST explicitly specify compensation and reconciliation patterns (e.g., Saga, Outbox).
  • Latency projections MUST include realistic cross-network transport and TLS negotiation overheads.
  • Every extraction candidate MUST detail a zero-downtime database migration strategy.

Output format

  • Section 1: Architectural Domain Landscape (max 150 words)
  • Section 2: Service Decomposition Decision Matrix (Markdown table with columns: Candidate Domain, Coupling Index, Data Consistency Strategy, Cross-Network Latency Delta, Blast Radius, Extraction Complexity, Recommended Pattern)
  • Section 3: Operational Failure Modes & Distributed Fallbacks (Detailed table with columns: Extracted Service, Network Partition Impact, Fallback/Degradation Mode, Data Sync Recovery Path)

Self-review

  1. Does the matrix prevent distributed monolith anti-patterns by strictly checking boundary coupling?
  2. Are data migration strategies realistic and zero-downtime compatible?
  3. Does the extraction sequence match team ownership capacity?
AuraScore breakdown
83/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness5/5 · Strong

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

business-strategy
business-operations
software-engineering-debugging
architecture
microservices
refactoring