# Agent-driven calibration loop

The controller is an **external CLI agent**, not an in-graph node. ComfyUI is the
execution environment (prompt receptor → T2I → VLM judge); the agent is the brain that
reads the analysis, calibrates the prompt generator, and queues the next iteration.

```
 CLI AGENT (controller / brain)                 COMFYUI (execution, running with --listen)
 ───────────────────────────────                ──────────────────────────────────────────
  1. build/calibrate a prompt
  2. agent_bridge.py --prompt ... ───POST /prompt──►  CalibratorPromptReceptor (injection point)
                                                          │ prompt / negative / seed
                                                          ▼
                                                       T2I (SDXL / Flux / Krea2)
                                                          │ generated image
                                                          ▼
                                                       Qwen3-VL Image Judge
                                                          │ writes calib_<tag>.json + latest.json
  3. poll /history/{id} (bridge does this)  ◄───────────┘
  4. read report JSON (overall_score,
     per-axis diffs, fix_suggestions)
  5. adjust Prompt-Builder knobs / prompt
  └──► go to 1 until overall_score ≥ target
```

## Why API-driven, not file-watch

A passive "watch a file and auto-run" receptor is fragile in ComfyUI (no native file
watcher / auto-queue, and prompt↔image↔analysis can desync). Driving `POST /prompt`
instead makes every iteration **synchronous and ordered** — one `prompt_id` ties the
prompt, the image, and the analysis together. The receptor node is still the clean
injection point; the agent just overrides its widgets per queue. (The receptor *also*
supports a `source_file` for file-first workflows if you ever want it.)

## The three pieces

| Piece | Role |
|---|---|
| `CalibratorPromptReceptor` (`SxCP External Prompt (Receptor)`) | Stable node the agent injects `prompt/negative/seed` into. Feeds the sampler. |
| `QwenVLImageJudge` (`Qwen3-VL Image Judge (Calibrator)`) | Scores generated vs reference; writes `calib_<run_tag>.json`, `latest.json`, `calib_<run_tag>.md` to `report_dir`. |
| `agent_bridge.py` | One CLI call = one iteration: inject prompt → queue → wait → print the analysis JSON to stdout. Stdlib only. |

## One iteration (what the agent runs)

```bash
python agent_bridge.py \
  --server 127.0.0.1:8188 \
  --workflow workflow_api.json \
  --prompt   "1 woman, red lingerie, bedroom, full body, warm rim light" \
  --negative "blurry, deformed" \
  --seed 12345 \
  --run-tag  iter003 \
  --analysis-dir /media/p5/Comfyui/output/calibrator
```

Stdout (captured by the agent) is the report:

```json
{
  "run_tag": "iter003",
  "overall_score": 0.62,
  "axes": {
    "pose":     {"score": 0.40, "diff": "ref standing, gen seated"},
    "clothing": {"score": 0.85, "diff": "close; gen lacks lace detail"}
  },
  "fix_suggestions": ["set pose=standing", "add 'lace trim' to clothing"],
  "prompt_used": "1 woman, red lingerie, ...",
  "_prompt_id": "…", "_report_path": "…/calib_iter003.json"
}
```

## Agent calibration policy (suggested)

The agent maps the lowest-scoring axes onto Prompt-Builder knobs and applies the
`fix_suggestions`, regenerates, and keeps changes that raise `overall_score`
(greedy per-axis hill-climb). Keep the **T2I seed fixed** while searching prompt axes so
the score reflects the prompt, not sampler noise; vary the seed only once you're near the
target. Stop at `overall_score ≥ target` (e.g. 0.85) or a max-iteration budget. Log every
`(prompt, knobs, score)` so the search is auditable/resumable.

## Setup checklist

1. Run ComfyUI with `--listen` (so the bridge can POST). Install this node pack.
2. Build a workflow with: `CalibratorPromptReceptor` → (Prompt-Builder formatting, optional) → T2I → `QwenVLImageJudge` (feed the **reference** image into `reference_image`, the T2I output into `generated_image`).
3. Set the Judge's `report_dir` to a known path; pass the same path as `--analysis-dir`.
4. Export the workflow in **API format** (`workflow_api.json`).
5. Drive it from the agent with `agent_bridge.py`, once per iteration.