Workflow automation

The assistant that proposes — and learns from what you decline.

The real problem

A proactive assistant is a good idea that becomes unbearable very fast. The threshold is low: two irrelevant proposals are enough to get the feature switched off forever.

The reflex is to put restraint in the prompt — "only propose if it is genuinely useful". That does not hold: the model has no memory of its own past rejections, and no way to count.

How I solve it

A five-stage loop where the guardrail lives in code, never in the prompt:

  1. Usage journal — private by default: first 256 characters plus a SHA-256 fingerprint, enough to detect repetition without storing the text
  2. Nightly habit mining — recurring patterns extracted offline
  3. Proposal cards — under a hard cap: 3 per session, never more
  4. Auto-distillation — what is accepted becomes a capability
  5. Feedback loop — a rejection is final for that (habit, kind) pair; an acceptance buys 30 days of silence; three rejections in a row mute a whole category for 14 days

The model receives the aggregate as informative data. Never as a decision.

A trap learned in the field

The initial temptation was to let the model arbitrate its own frequency — it has the context, it should know. It does not. A stateless model cannot count its own interruptions, and a moderation instruction in the prompt is renegotiated every turn.

Moving the counter into code fixed it in one pass. The general rule: anything that must be guaranteed is not asked of the model, it is imposed around it.

What it delivers, measured

  • Five bricks shipped and deployed, each proven end to end before the next began
  • Hard cap of 3 proposals per session, not bypassable
  • Fingerprinted journal: repetition detection without retaining the text
  • File-watching agent in production, reconciliation every 6 h

Where it runs in production

This capability is engaged in

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