Research Note #001 · June 4, 2026
Architecting Domain-Embedded AI Agents
Memory, Tools, Governance, and Multi-Surface Orchestration
Most production “agents” today are still chat sessions with extra API calls. That is enough for drafting copy; it is not enough when work has owners, deadlines, spend limits, and an audit log. This research note lays out a practical architecture for domain-embedded agents: the model reads and writes through product schemas, keeps memory outside the prompt, routes side effects through tools, and leaves humans on the hook for irreversible steps. The argument is grounded in published work on tool-augmented language models and in operational constraints from social-media programs, where coordination cost often exceeds model capability. The note is descriptive engineering guidance, not a benchmark study.
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