Taxonomy of agentic primitives in Amazon Quick
AWS Machine Learning Blog · 人工智能
Over the past year, as we built Amazon Quick, our agents went from answering questions to taking actions: scheduling meetings, running research, generating reports. As the system evolved, one problem kept resurfacing: there was no shared definition of the primitives that agentic work is built from. The same few ideas kept getting rebuilt under different names: an actor that does the work, a signal that starts it, a packaged procedure, and a memory that carries lessons forward. This repetition leaked into product design and onto the user. Take the user who says “ monitor my Slack channel and send me a daily digest ”. Is that a new agent, a scheduled instruction to an existing one, a workflow, or a skill? These are different questions, but without a shared vocabulary they collapse into one confusing choice. This post shares the taxonomy we converged on: one set of primitives found by decomposing agentic work into four plain questions: When? , What? , Who? , and How? Most of the framework stays invisible to end users by design, but a shared vocabulary makes the system easier to reason about for anyone building beneath it. The framework at a glance Figure 1: How triggers, agents, and capabilities relate, with learning feeding back from the session runtime A trigger starts the work and carries the task ( When and What ), an agent takes it on ( Who ) and uses its capabilities (tools, skills, and workflows) to perform the task ( How ). This post describes these primitives and how they relate to one another. It’s the first post in a series, and upcoming posts cover our agentic operating system, background learning, proactive feeds, and much more. Triggers: What causes work to begin Triggers are the activation mechanism for getting work done, but they also carry instructions: a human message is both the moment of activation and the description of what to do. They are both the When? and the What? of a task. Human-initiated: A person opens a conversation and asks the agent to