Operating manual
Tag: AI Agents
A focused reading path for AI Agents: related field notes, evidence trails, and operating questions from the archive.
- the-next-agent-bottleneck-is-operational-control-not-model-capability.md
The next agent bottleneck is operational control, not model capability
Frontier models keep improving. The agent bottleneck is moving to state, permissions, evals, recovery, and proof that work actually shipped.
open artifact → - the-agent-stack-governance-checklist-authority-review-provenance-and-handoffs.md
The Agent-Stack Governance Checklist: Authority, Review, Provenance, and Handoffs
A practical checklist for designing authority, review, provenance, and handoff gates around MCP, A2A, and agent SDK workflows before they touch production work.
open artifact → - green-status-is-not-shipped-work.md
Green Status Is Not Shipped Work
Agent workflows need deliverable-level acceptance tests because internal completion state is not the same thing as user-facing proof.
open artifact → - protocols-make-agent-exchange-legible-governance-still-lives-above-the-protocol.md
Protocols Make Agent Exchange Legible. Governance Still Lives Above the Protocol.
A2A and MCP make agent exchange easier to standardize, but they do not decide authority, review, provenance, memory, escalation, or accountability.
open artifact → - your-ai-shouldnt-be-one-assistant-it-should-be-a-room-full-of-desks.md
Your AI Shouldn't Be One Assistant. It Should Be a Room Full of Desks.
One generalist assistant is the wrong unit of deployment. The right unit is a small, named fleet — each desk owns a queue, a memory, and a hand-off. Operator sits in the loop.
open artifact → - the-personal-ai-os-wont-be-a-chatbot-it-will-be-a-continuity-layer.md
The Personal AI OS Won't Be a Chatbot. It Will Be a Continuity Layer.
Agents are becoming abundant. The scarce platform is the continuity layer that carries goals, memory, permissions, workflow state, and verified outcomes across apps, devices, agents, APIs, and humans.
open artifact →