AI agents and agentic workflows

An AI agent, in the sense used here, is a system that decides what to do next rather than following a fixed path — it reads a document, judges what kind it is, and routes it accordingly, instead of running the same five steps regardless. That flexibility is what makes agents useful for professional-services work, where the input is messy and the correct next step depends on what arrived. It is also what makes them harder to deploy safely, since a system with discretion needs boundaries, logging and a human checkpoint on anything consequential. The posts below cover where agents genuinely outperform fixed workflows, what implementation looks like in a law firm, a CPA practice or an advisory firm, the ROI evidence that exists so far, and the technical patterns for building multi-agent systems that stay debuggable.