software-engineering-eng-architect

What problem does it solve?

It solves the problem of making risky software architecture decisions without measurable goals, evidence, or clear trade-offs—especially when requirements evolve and coupling costs accumulate over time.

Core Features & Use Cases

• Evolutionary architecture guidance: Produces architecture recommendations driven by explicit architectural fitness functions rather than opinions.
• Coupling- and evidence-first evaluation: Guides discovery with a minimum viable evidence checklist (team size, deployment frequency, prioritized -ilities, current architecture, domain complexity, operational maturity) and requests only what is missing.
• Decision support and documentation: Maps requests to tasks like architecture design, coupling evaluation, trade-off analysis, ADR creation, design reviews, and migration strategies (strangler fig).
• Use cases: Greenfield system design, brownfield architecture assessment and migration planning, and decision-time trade-off discussions (e.g., monolith vs microservices, event-driven, ADRs).

Quick Start

Ask the AI to design an evolvable architecture for your system by stating your team size, deployment frequency, and the top architectural concerns you want to optimize.