AI Automation Engineer

A practical view of agentic systems, workflow orchestration, and where companies can start safely.

Monolith, modular monolith, or microservices: how product stage should guide architecture decisions.

The gap between a working prototype and a reliable production LLM system is wider than most teams expect. Here is what actually breaks — and how to fix it.

Most teams jump straight to fine-tuning when RAG would solve their problem faster and cheaper. Understanding when each approach is appropriate will save your team months of wasted effort.

Single agents hit complexity ceilings quickly. Multi-agent architectures built with LangGraph unlock the next level of AI-powered automation — but they introduce new design challenges that require careful thinking.

Performance, metadata, structured data, and rendering choices that help modern products get discovered.

The evolution from simple chatbots to goal-directed AI agents capable of planning, tool use, and sustained autonomous action represents the most significant shift in applied AI since the launch of large language models.

The practical phases behind discovery, UX, MVP engineering, QA, deployment, and continuous improvement.

Semantic search, RAG pipelines, recommendation systems, and memory-enabled AI agents all depend on vector databases. Here is how they work and when to use Pinecone, Milvus, or pgvector.

Prompt engineering has evolved from a curiosity into a systematic engineering discipline. These are the techniques that separate production-quality AI outputs from inconsistent, unreliable responses.