For two years we have talked about building agents as if an agent were a thing you construct. It isn’t. The harness — the loop, the tools, the context discipline — is the artifact you actually build, version, and debug. This post reframes the agent as emergent behavior, explains why agent frameworks now feel wrong-shaped in production, and offers a way to decide when to own the loop and when to rent it.
Most senior engineers think about what role they play on a team. Fewer think about how long they should play it — and what happens when they don’t move on. This post examines two complementary frameworks for PE role clarity, and why intentional role transitions — not just role awareness — are what actually let Principal Engineers scale their impact across an organization.
Large Language Models don’t just introduce new tools — they demand new skills. This post explains what actually changes for engineers and technical leaders when LLMs become part of production systems, and where traditional instincts need to be updated.
Retrieval-Augmented Generation (RAG) is one of the most important architectural patterns to emerge alongside Large Language Models. This post explains why RAG exists, how it works, and what problems it actually solves — without turning it into a tooling tutorial.
Large Language Models often fail in ways that feel inconsistent or irrational. This post explains why those failures are not bugs, but natural consequences of probabilistic systems trained on language — and what that means for real product and system design.
Once you understand how LLMs work, the next real question is model choice. This post explains how to reason about LLM tradeoffs — capability, cost, controllability, and domain fit — without relying on benchmarks alone.