ENGINEERING INSIGHTS
Engineering notes, architectures and technical decisions around AI systems, backend engineering, integration and automation.
AI & ArchitectureFEATURED
An architecture deep dive into a dispatch engine combining AI qualification, business constraints, readable scoring and human validation.
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01Data & Architecture
An AI-ready data architecture should separate business data, vector knowledge, execution traces and sensitive information.
02RAG & LLM
The architecture choices that separate a convincing RAG prototype from a genuinely usable assistant.
03Frontend & Angular
Streaming, intermediate states, human validation, errors and observability: AI application frontends need a specific architecture.
04Backend & AI
Timeouts, validation, structured outputs, security and fallback: essential guardrails for backend LLM integration.
05Security & AI
An AI agent can read, combine and transmit data from multiple systems. Without guardrails, data leakage becomes an architectural risk.
06Automation
When to favor visual workflows, when to move to code, and how both can coexist in enterprise architecture.
