Agentic-Engineering

The question I get asked most often by engineers starting to build with language models is some variation of: “should we fine-tune or should we do RAG?” It is almost always the wrong question, but it is the wrong question in an instructive way. The reason it gets asked so much is that the choice feels architectural, and architectural choices feel like the kind of thing you commit to once and live with. In practice, the choice is closer to “should I use a database or a cache” - the answer is usually some of both, applied to different problems, and the ratio shifts as the system matures. ...

The word “hallucination” is one of the most successful pieces of accidental marketing in our industry. It is a soft, almost endearing way to describe an LLM stating with full confidence that a function exists when it does not, that a court case was decided when it was not, that a paper was written by an author who has never published in that field. It makes the failure sound like a quirk rather than the central reliability problem of the entire technology. ...

We’ve embraced the future. AI agents like Cline are now the primary “builders” of software, executing complex engineering plans from high-level specifications. As I’ve argued in “The Architect vs The Builder”, the human role is shifting from execution to architectural oversight and defining intent. The patterns that determine whether agents stay shipped are covered in “AI agents that actually work”, and the wider safety framing sits in “AI safety from first principles”. But this shift introduces a profound, often uncomfortable, question: How do we know it actually works? ...