Memory bugs in AI agents usually come from bad writes, not weak recall. Learn how to design write policies that decide what an agent should persist, when it should write, and when it should not.
Retrieval is not memory. In production AI agents for developers, retrieval should stay a read path to external systems, while memory remains a write path for learned continuity like preferences, instructions, and successful procedures.
The most adaptable systems are often built through collaboration, not strict sequencing.
The easiest systems to scale are often the ones whose components know the least about each other.
Systems become fragile when correctness depends on invisible timing assumptions.
Learn how to design an eval set for a tool-using agent using trace-level evaluation, dataset splits, layered scoring, and realistic failure cases that catch regressions before production.
Prompting can improve a single run, but it cannot prove that an agent workflow is reliable. This article explains how traces, scorecards, offline evals, and online monitoring turn agent quality into an engineering discipline.
The less your code knows about its surroundings, the easier it is to change safely.
Most confusion about AI agents starts with weak definitions. This article explains the mental model that holds up in real systems: an agent is a control loop around an LLM with tools, state, and observable execution.
The best code speaks fluently in its domain.