100 lines
4.7 KiB
Markdown
100 lines
4.7 KiB
Markdown
# AGENTS.md — ML-roller i Vauco OS
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**Sist oppdatert:** 2026-06-01
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**Formål:** Definerer hver agents rolle i ML-laget (Parameter Server-mønster + Dask DAG).
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---
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## Oversikt
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```
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┌─────────────────────────────┐
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│ JASON VAUGER │
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│ (Personlig assistent / │
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│ persona-lag) │
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└──────────┬──────────────────┘
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│ delegerer til
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┌──────────▼──────────────────┐
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│ OPAX Orchestrator │
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│ (PS Server-node / DAG hub) │
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└──────────┬──────────────────┘
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│
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┌──────────┬──────────┼──────────┬──────────┐
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▼ ▼ ▼ ▼ ▼
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billing- rag-agent memory- tui- multi-
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agent agent bridge agent
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(worker) (worker) (worker) (worker+ (worker)
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input-src)
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```
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---
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## Agenttabell
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| Agent | Katalog | PS-rolle | Dask-rolle | Online learning | Heavy mode |
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| **Jason Vauger** | `agents/jason-vauger.md` | **Persona-wrapper** (over OPAX) | Trigger for OPAX DAG via naturlig-språk | Nei — kontekstuell | Nei |
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| OPAX Orchestrator | `agents/core-logic/` | **Server-node** (aggregerer state, dispatcher DAG) | DAG scheduler | Nei | Ja (koordinerer heavy sub-agenter) |
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| billing-agent | `agents/core-logic/` | Worker (pusher kostdata til state store) | `dask.delayed`-task | Nei — strukturert data | Nei |
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| rag-agent | `agents/rag/` | Worker (pusher retrieval-resultater) | `dask.delayed`-task | **Ja** — `partial_fit` på nye docs (ML-2) | Ja |
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| memory-agent | `agents/memory/` | Worker (pusher/puller memory-oppslag) | `dask.delayed`-task | Nei — faktahenting | Nei |
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| eval-agent | `agents/eval/` | Worker (pusher evalueringsmetrikker) | `dask.delayed`-task | **Ja** — kvalitetsscore-feedback (ML-2) | Nei |
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| multi-agent | `agents/multi_agent/` | Worker + sub-orchestrator | DAG-node med barn | Nei | **Ja — primær heavy-mode bruker** |
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| tui-bridge | `vauco-gemini-tui-bridge` | Worker + **primær input-kilde** | Streaming input til DAG | **Ja** — real-time feedback-loop (ML-2) | Ja |
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| vauco-bootstrap | `vauco-bootstrap` | **Config-aggregator** (analog til PS shard) | Trigger for nye DAGer | **Ja** — ASHA-tuning (ML-2) | Nei |
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---
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## ML-fase per agent
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| Agent | ML-1 (nå) | ML-2 (neste) | ML-3 (fremtidig) |
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|---|---|---|---|
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| **Jason Vauger** | Naturlig-språk interface mot OPAX | Kontekstuell tilpasning til bruker | Personlig læringsloop + proaktive forslag |
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| OPAX Orchestrator | Kjør DAG via `execute_dag()` | Bytt heavy-mode til ML-predictor | PBT-eksperiment |
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| billing-agent | `log_agent_call()` + `state.push()` | — | Feature importance-input |
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| rag-agent | `log_agent_call()` + `state.push()` | `partial_fit` på nye docs | Top-K feature importance |
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| memory-agent | `log_agent_call()` | — | — |
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| eval-agent | `log_agent_call()` + `state.push()` | Kvalitetsscore-feedback | Feature importance-target |
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| multi-agent | `log_agent_call()` | — | PBT worker |
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| tui-bridge | `log_agent_call()` | `feedback_loop.py` (ML-2.1) | — |
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| vauco-bootstrap | — | `hypertuner.py` ASHA (ML-2.3) | PBT vinner-konfig → Terraform |
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---
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## State store nøkkelkonvensjon
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Format: `"<agent_id>:<key>"`
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| Agent | Nøkkel | Eksempelverdi |
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| `billing-agent` | `last_cost_usd` | `12.5` |
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| `billing-agent` | `last_report_ts` | `"2026-05-26T01:00:00Z"` |
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| `rag-agent` | `last_retrieval_score` | `0.87` |
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| `rag-agent` | `chunks_retrieved` | `5` |
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| `eval-agent` | `quality_score` | `0.91` |
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| `multi-agent` | `active_sub_agents` | `["billing-agent", "rag-agent"]` |
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| `tui-bridge` | `last_user_feedback` | `"positive"` |
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---
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## Telemetri-felter (referanse)
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Alle agenter logger via `ml.telemetry.log_agent_call()`. Feltene er:
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```json
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{
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"ts": "2026-05-26T01:00:00.000Z",
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"agent": "billing-agent",
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"model": "gemini-2.0-flash",
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"mode": "standard",
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"duration_s": 0.312,
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"success": true,
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"error": null,
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"input_tokens": 284,
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"output_tokens": 512
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}
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```
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Filer lagres i: `$TELEMETRY_DIR/YYYY-MM-DD.jsonl` (default: `/tmp/vauco_telemetry/`)
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For GCS-arkivering: sett `TELEMETRY_DIR=gs://...` eller bruk `10-cost-guard.sh`-cron.
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