Workflow Reliability

TL;DR for operators An AI agent can complete every reported tool call while leaving a business workflow in an inconsistent state. The critical distinction is between successful invocations and the external effects that remain after the workflow commits or aborts. A study of 98,291 remotely discovered Model Context Protocol (MCP) tools found that 74.0% emitted at least one standard annotation field. Yet the standard annotation vocabulary could not fully express any of eight transactional capabilities identified by the researchers. ...

TL;DR for operators A multi-step agent workflow eventually faces a problem that a larger context window does not solve. One model makes a decision, another step runs later with new evidence, and the system must know which earlier facts and decisions still count as official. Replaying the transcript gives the next executor more text; it does not necessarily tell it what has been accepted, superseded, or rejected. ...

Enterprise AI teams love an architecture diagram. Boxes, arrows, specialist agents, memory stores, tool registries, a tasteful orchestrator sitting at the top like a middle manager with JSON access. It looks reassuring. It looks intentional. It also looks suspiciously like the kind of thing that can fail in six different places while still producing a beautifully formatted answer. ...