Summary

This PR adds a execution workflow for autonomous experiments, replacing session-by-session program.md interpretation with a deterministic runner plus agent runbook.

What’s included

• Add workflows/run_experiment.py as the single experiment orchestrator:
  • start, resume, status commands
  • top-level stage controls: setup, baseline, loop
  • loop sub-stage controls: propose, apply, commit, train, triage, record, decide
  • resumable checkpointing under workflows/runs/<run_id>/
  • run-id policy: <branch-slug>-rNNN
• Add AGENTS.md runbook with explicit natural-language to command mapping for agent sessions.
• Setup robustness:
  • auto-run uv run prepare.py when cache/tokenizer is missing (default on, opt-out via --no-auto-prepare)
  • explicit setup precondition checks before baseline/loop
• Background training support:
  • training stages start in background by default (--background-train)
  • resume polls/continues in-flight baseline/train jobs
• Human intervention support:
  • proposal override via run-scoped workflows/runs/<run_id>/next_proposal.json
  • proposal override via explicit --proposal-file <path> on start/resume
  • canonical override schema at workflows/schemas/proposal.schema.json
  • deterministic precedence in propose stage: --proposal-file -> next_proposal.json -> stochastic proposal -> deterministic fallback
  • consumed run-scoped proposals are archived to workflows/runs/<run_id>/consumed_proposals/iter_<NNNN>.json

Why

In long-running autonomous sessions, prose-only execution is fragile and inconsistent. Multiple complaints regarding this on X.
This PR makes runs repeatable, resumable, inspectable, and human-steerable while preserving program.md as the policy/objective layer.