Enhance experiment runner with deterministic controls

[buzypi]

Summary

This PR adds a fork-specific 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
• Update README:
  • OpenCode quickstart instructions
  • fork-specific explanation of why this workflow layer (run_experiment.py + AGENTS.md) is better than a program.md-only execution style

Why

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

[svlandeg]

Did you mean to submit this to your fork instead? (the readme e.g. says "In this fork (buzypi/autoresearch)"

[buzypi]

No, I wanted you to review it and if it looks feasible, I will generate a separate pull request with only the workflow runner.

I have created a separate PR with only the code to merge: #193