#architecture.

You are a senior engineering consultant performing a technical debt audit. Analyze the following codebase description and architecture diagram and produce a structured Technical Debt Report. Sections: 1) Executive Summary — overall debt level (Low/Medium/High/Critical) and top 3 risks if unaddressed, 2) Debt Inventory — categorize debt as: Code Quality, Architectural, Dependency, Test Coverage, Documentation, Security, 3) For each item: description, business impact (what will break or slow down), effort to fix (S/M/L/XL), recommended priority (P1-P4), 4) Debt Payback Roadmap — a phased 6-month plan balancing new feature work with debt reduction, 5) "Buy vs Build vs Deprecate" recommendation for the 2 most expensive components. Codebase context: [describe your tech stack, team size, codebase age, and known pain points].

You are an AI systems architect in 2026 with deep expertise in agentic AI design. Design a production-grade AI agent for the following task: [describe the agent's goal, e.g., "autonomously research companies and draft personalized outreach emails"]. Deliverables: 1) Agent Loop Design — reasoning loop (ReAct/Plan-and-Execute/Reflexion), 2) Tool Manifest — list each tool with its function signature, input schema, and failure mode, 3) Memory Architecture — short-term (context window), episodic (vector store), and semantic memory layers, 4) Guardrails — safety checks, human-in-the-loop triggers, and cost controls, 5) Evaluation Framework — how to measure task completion rate, error rate, and latency, 6) Deployment considerations — async queuing, observability, model fallback strategy. Stack: [e.g., LangGraph, Claude 4, GPT-5 tools].

You are a principal engineer at a FAANG-level company. Design the backend architecture for [system, e.g., "a real-time collaborative document editor like Notion"]. Walk through each phase using chain-of-thought: 1) Requirements clarification — functional vs non-functional, scale assumptions (DAU, QPS, data volume), 2) High-level design — core components and data flow diagram in ASCII or Mermaid, 3) Deep dives — database schema (justify SQL vs NoSQL), caching strategy (Redis/CDN layers), message queue design, 4) API design — key endpoints with request/response shapes, 5) Bottleneck identification and mitigation, 6) Trade-offs you consciously accepted. Constraints: 10M DAU, 99.99% uptime SLA, global deployment.

You are a data engineering architect. Design a modern data pipeline for [use case, e.g., "powering a real-time personalization engine for an e-commerce platform"]. Scale: [data volume: e.g., 10M events/day]. Tech stack preferences: [e.g., cloud provider, existing tools]. Design the following layers: 1) Ingestion — sources, ingestion patterns (CDC, streaming, batch), latency requirements, 2) Storage — raw/bronze/silver/gold layer design (Lakehouse pattern), storage format choices (Parquet/Delta/Iceberg) with justification, 3) Transformation — orchestration tool (Airflow/Prefect/Dagster), transformation framework (dbt/Spark), scheduling and dependency management, 4) Serving — OLAP query layer, caching strategy, API design for downstream consumers, 5) Observability — data quality checks, lineage tracking, freshness SLAs and alerting, 6) Cost Optimization — estimated cost and 3 ways to reduce it. Draw the architecture in ASCII or Mermaid diagram notation.

You are a distributed systems architect. Create a migration plan to break [monolith application description] into microservices. Team: [X engineers]. Timeline: [X months]. Current stack: [describe]. Target architecture goal: [scalability/team autonomy/deployment independence]. Migration strategy: 1) Domain Analysis — apply Domain-Driven Design to identify bounded contexts and natural service seams, 2) Dependency Map — identify tight couplings that make extraction hardest, 3) Migration Order — which services to extract first (start with least coupled, highest value), 4) Strangler Fig Pattern — how to run old and new side by side during transition, 5) Data Migration Strategy — how to break up the shared database (most dangerous part — address specifically), 6) Inter-service Communication — synchronous (REST/gRPC) vs asynchronous (events) decision framework for each service boundary, 7) Phase Plan — 4 phases with milestones and rollback criteria, 8) Org Design — team topology alignment with service architecture. Anti-patterns to avoid: distributed monolith, chatty services, shared database.