How a message becomes an agent rule
Most people think a message is disposable. Send it, move on.
ILURA treats repeated messages differently. If the same situation will happen again, the message contains training material.
The loop
- You write or paste a rough message.
- The agent rewrites it.
- You correct what feels wrong.
- ILURA turns the correction into a learned preference.
- The role agent applies that rule next time.
That is how a message becomes memory.
Example
Original correction:
Do not over-apologize. The delay is real, but keep the new deadline visible.
Agent rule:
When explaining a delay, acknowledge once, name the blocker, keep the new deadline in the first paragraph and avoid apology loops.
That rule can now help with future delay messages.
Why this matters
Writing is the easiest place to see whether an agent is learning correctly. You can inspect the output, reject it, edit it and save the rule.
The bigger vision starts there: agents trained by real corrections, not generic prompts.
Use this as agent training material
This guide defines part of the ILURA training model: a private agent learns from roles, routines, decision rules and corrections, then applies that behavior when you invoke it.
- Name the role or routine
- Save the rule in plain language
- Review the next output before you trust it
Try it now
Put this to work on a real message.
Open ILURA, bring in a message you actually need to handle today, and get it done in your voice — free, on device, no account. It learns the preference, so the behavior carries to the next one.
Free to start · No account · Data Not CollectedQuick answers
- What part of a message should become a rule?
- Save the part that will repeat: tone, boundary, sequence, decision criteria, forbidden phrase, preferred next step, or role-specific fact.
- Is every message worth saving?
- No. Save rules from patterns you expect to see again. A one-off message can stay a one-off.
- Why is this better than writing prompts?
- A prompt tells the AI what to do once. An agent rule saves the reusable lesson so the agent can start from that behavior next time.