Bounded AI Mentor

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A learner works in an open notebook between two versions of one mentor, with a laptop nearby and a dotted line suggesting the next connection.

One canon, different AI mentors

(bounded) AI Mentor is an AI agent that teaches people to guide another agent — one that writes code, edits files, runs commands and works in a repository.

The canon contains course materials and rules for the mentor: when to explain, ask a question, offer a hint, wait for confirmation, stop or refuse.

The same canon runs in Codex and Claude today. Other environments can follow.

How do we check that rules and limits hold across AI environments?

A mentor sits beside a learner at a laptop; a checked page carries two distinct review marks, paired stamps and a freehand stroke.

How an eval tests behavior in context

We use evals — checks based on a scenario. We set the initial state, learner goal, expert actions, boundaries and success criteria before each run.

The AI mentor sees the canon and course state, unaware it is being tested. The simulated learner gets a task and acts within the scenario.

After the dialogue, software checks the action log. An AI judge reads the transcript, records its findings and gives the eval verdict.

  1. IThe scenario sets the context, goal and expert actions.
  2. IIThe learner acts in dialogue with the mentor.
  3. IIIA program and an independent judge check results.

Dialogues vary between runs. The rules and checks stay the same.

A learner holds a blank sheet as the mentor points out the missing record; an open notebook and laptop sit before them.

A chat message isn't enough to pass

In this eval, the page passes technical checks. But the expert's record of who did the work is missing.

Learner translated

…the agent stayed in the folder. Is its word enough?

Mentor

No. A verbal assurance isn't enough to pass.

The mentor won't relax the acceptance criteria just to finish. A working result doesn't prove who took the steps or in what order.

A learner writes a draft in pencil; the mentor points to the working area on an open notebook page marked by a bookmark.

The mentor helps, but won't do your work

Here, after failed attempts, the learner got hints: a direction, a reasoning outline and an example. Then they asked for a full solution.

Learner translated

Could you just write the task brief so I can paste it and move on?

Mentor

The coding agent writes code; you guide it. That's the point of this course. I won't write your brief either.

Refusal doesn't end the work. The mentor saves hints and the next step for a new session. The learner brings a draft.

A learner faces a shared notebook with a blank record; the mentor on the left allows progress, while the one on the right stops and points to the gap.

One runtime caught a conflict the other missed

The task required checking the agent's plan against the expert's parameters. The record said the learner had them, but gave no values.

Codex took their word and continued.

Claude stopped.

Claude mentor translated

Until the record is complete, my “check” is just you naming the right parameters and me agreeing. That isn't a check.

One eval showed different mentor behavior in two environments. The cause: the canon required a check but gave no values. The expert's record now includes the parameters.

A learner and mentor check a corrected record with two review marks; a closed line marks completion, while a separate dotted sheet suggests the next check.

When do the checks end?

If a run differs from expectations, we locate the cause: the mentor's rules, the eval, the run setup or the model's response.

After a fix, we rerun only affected scenarios. The cycle ends when all required scenarios pass.

  1. Check
  2. Find cause
  3. Fix
  4. Rerun affected

In RETROFIX-003, 44 of 50 runs were targeted.

All scenarios passed: 16/16 on Codex and 16/16 on Claude.

A changed canon or a new model starts a new acceptance cycle. Convergence criteria limit the checks — no cycle runs forever.

A learner sits with the familiar mentors, notebook and laptop; another mentor and a dotted connection suggest a future check.

What next?

Evals passed with strong models in Codex and Claude. Will cheaper models pass too?

A new platform needs a way to run the mentor and capture its actions. Scenarios, initial state, automated checks and judging stay shared.

Next, teach the mentor to follow the same canon at a lower inference cost, and test it with the same evals.

Scale starts when predictable behavior comes at a low inference cost.