Field Notes
Flagship essays — single-topic deep dives into how the human / agent / robot triad actually works in practice. Each piece aims for the longest useful life: principles first, examples second, hand-waving third. Read these when you want the conceptual foundation before the next workflow or lesson, or when you need a single, durable take on a topic that the rest of the manual keeps returning to.
Field Notes
Flagship essays — single-topic deep dives into how the human / agent / robot triad actually works in practice. Each piece aims for the longest useful life: principles first, examples second, hand-waving third. Read these when you want the conceptual foundation before the next workflow or lesson, or when you need a single, durable take on a topic that the rest of the manual keeps returning to.
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Work Desks and Specialty Agents: The Architecture Between Sessions and Teams
Sessions, jobs, work desks, and specialty agents are four names for the same primitive — and knowing which one you mean changes how you design, debug, and govern a running agent system. Here is the vocabulary, the hierarchy, and the six things a coordinating agent gets wrong when it tries to do everything itself.
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Field Note: When the Prompt Cache Saved the Morning
A small prompt restructure cut our morning cron latency from 4s to 1.1s and dropped the daily cost by 14%. Nothing else changed. A field note on what we learned, what didn't change, and what we now check weekly.
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Agent Loops: The Operating Rhythm of an AI System
Every agent system runs on a loop. This field note surveys the loops Triadive has documented — single-shot, multi-turn, cron-driven, sub-agent, and graph-driven — and names the design choices that decide whether a loop keeps an agent coherent or slowly destroys it.
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Loops vs Graphs: When Iteration Becomes Routing
Loops and graphs are the two shapes an agent workflow can take. Loops iterate until something terminates; graphs route through typed transitions until they reach a terminal node. This pillar names the threshold where iteration becomes routing, the patterns that work at that threshold, and the mistakes that come from picking the wrong shape.
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Agent Memory and State: How an AI System Remembers What It Needs To
Memory is the part of an agent system that decides whether the agent remembers anything between loops. This pillar surveys the four layers Triadive has documented — short-term, long-term, semantic, wiki — and the design choices that decide which layer to reach for, when, and at what cost.
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Field Notes: The Three Amigos of the Agent Stack
The user, the model, and the runtime are not the same person. Most operational confusion in agent work comes from forgetting which one is doing what. A short field note on how to keep the three roles straight.
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Field Notes: Triads, Not Tools
The human / agent / robot triad is a working arrangement, not a brand. Why the underlying pattern keeps showing up under different labels — and what to design for when it's the pattern that matters.
FIELD NOTES