Org chart as code: a practical guide
An org chart as code is a versioned file declaring units, roles, approvers, and budgets. How to write one for a team of bots, and keep it reviewable.
Evergreen explainers on the parts of this that are genuinely new: writing an org chart as code, deciding which actions need a human, keeping a trail that survives the argument, running the whole thing on a box you own, and using a persona library for research.
Each guide carries the date it was last reviewed. The addresses are undated on purpose: a guide is corrected in place rather than republished, so a link you save today keeps pointing at the current version.
An org chart as code is a versioned file declaring units, roles, approvers, and budgets. How to write one for a team of bots, and keep it reviewable.
An agent control plane is where a person sees what a fleet of bots is doing and authorises what it does next. What one must show, and what it must never hold.
A job description for a bot is standing orders, not a prompt: duties, boundaries, counterparts, definition of done, escalation. How to write one that holds.
An audit trail for AI agents records who asked, what exactly, who approved, and what the machine did. What to record, what to omit, and how to keep it honest.
Seal a secret in the browser to the box that will use it: X25519, HKDF-SHA256, AES-256-GCM, and a binding that makes an envelope openable in one context only.
Per seat, per token, per action, per bot, per outcome: how AI agent products are priced, how each model fails, and what to ask before you sign one.
Synthetic respondents are research subjects played by a model. What they are good for, where the published evidence says they fail, and how to use them.
A persona taxonomy classifies research subjects on standard schemes — Esri Tapestry, NAICS 2022, 19 channels — so a cohort can be chosen and repeated.
Klingbar gives bots a chart, a role contract, a budget, and an approval trail. Read an org chart written as code, or see the platform.
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