17h ago

Builder Outlines Seven-Step Process For Creating Custom AI Agents

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Original post

how I design a new Hermes Agent marketing specialist from scratch every agent I run started as a pattern I noticed in my own work. here is the process I follow when I want to create agents 1. prototype in your main agent first > open a session and try to do the work yourself > run the same shape of task 2 or 3 times > notice what you keep repeating, what context the agent needs, what tools it reaches for > save the prompts and skills that worked do not jump straight to a new agent. you do not know enough about the workflow yet. 2. decide open or closed > open if the work is exploratory, requires taste, or has unpredictable inputs > closed if the shape is clear, inputs are predictable, output is verifiable > when in doubt, keep it open one round longer 3. write the soul. md this is the agent's identity and purpose. > what is this agent > what outcome does it own > what voice and standards does it follow > what does it never do skip the marketing fluff. the agent reads this every run. 4. pick the skill bundle > what skills does this agent need to do its job > bundle them so they load together when the agent fires > reuse skills from existing frameworks (gStack, Superpowers, matt pocock's skills) > if a skill does not exist yet, write the smallest version of it that works 5. define inputs, outputs, and triggers > input: what does the agent receive (a row in a sheet, a topic, a URL, a brief) > output: what does it return (a file, a row, a Slack message, a published article) > trigger: cron, backlog, chat, or all three if you cannot describe "done" in one sentence, the agent is not ready. 6. scope the tools > allowed: only what is needed for the job > not allowed: anything that touches production without approval (send email, edit CRM, post live, transfer money) > default to least privilege, expand only when you need to 7. spin up an isolated environment > own docker container > own env vars and credentials > own memory and skills > own model choice (cheap and fast for throughput, stronger for review steps) > register it in the control room never share keys across specialists. if one gets compromised, you want the blast radius small. 8. run it on real work, iterate > give it 5 to 10 jobs > watch where it drifts, where it asks for help it should not > tighten the soul, the skills, the eval > retire the prototype version in your main agent once the new one is solid 9. promote it to production > set the cron or wire the trigger > add monitoring (output quality, run cost, runtime) > document it in the control room: inventory, runbook, env-map, backup

5:31 AM · May 29, 2026 View on X
Builder Outlines Seven-Step Process For Creating Custom AI Agents · Digg