Taxonomy of agentic primitives in Amazon Quick

Executive Briefing

As we built Amazon Quick, its agents grew from answering questions to taking actions, and the same primitives kept getting rebuilt under different names. This post shares the taxonomy we converged on — triggers, agents, capabilities, and learned context — found by decomposing agentic work into four plain questions: When, What, Who, and How.

Strategic Industry Analysis

Strategic Context: Defining standardized primitives for agentic workflows is essential for moving beyond POC-stage AI into robust, production-grade enterprise automation.

Engineering & Architecture Perspective

Most enterprises fail at AI agents because they treat them as monolithic black boxes; by adopting this taxonomy, engineering teams can decouple logic from execution, finally allowing us to integrate AI reliably into fragile, high-stakes legacy ERP environments.

Architectural Takeaways

• Autonomous AI Agents: Impact on scalable enterprise infrastructure and operational automation.
• Workflow Orchestration: Impact on scalable enterprise infrastructure and operational automation.
• Agentic Primitives: Impact on scalable enterprise infrastructure and operational automation.
• Enterprise Automation Architecture: Impact on scalable enterprise infrastructure and operational automation.

Source & Attribution

This intelligence briefing is sourced from Taxonomy of agentic primitives in Amazon Quick and synthesized by the MTREXA Intelligence Team.