AGENTS.md — ML-roller i Vauco OS

Sist oppdatert: 2026-06-01
Formål: Definerer hver agents rolle i ML-laget (Parameter Server-mønster + Dask DAG).

Oversikt

                    ┌─────────────────────────────┐
                    │   JASON VAUGER               │
                    │   (Personlig assistent /     │
                    │    persona-lag)              │
                    └──────────┬──────────────────┘
                               │ delegerer til
                    ┌──────────▼──────────────────┐
                    │   OPAX Orchestrator          │
                    │   (PS Server-node / DAG hub) │
                    └──────────┬──────────────────┘
                               │
         ┌──────────┬──────────┼──────────┬──────────┐
         ▼          ▼          ▼          ▼          ▼
    billing-    rag-agent  memory-    tui-       multi-
     agent                 agent     bridge     agent
    (worker)   (worker)   (worker)  (worker+   (worker)
                                    input-src)

Agenttabell

Agent	Katalog	PS-rolle	Dask-rolle	Online learning	Heavy mode
Jason Vauger	`agents/jason-vauger.md`	Persona-wrapper (over OPAX)	Trigger for OPAX DAG via naturlig-språk	Nei — kontekstuell	Nei
OPAX Orchestrator	`agents/core-logic/`	Server-node (aggregerer state, dispatcher DAG)	DAG scheduler	Nei	Ja (koordinerer heavy sub-agenter)
billing-agent	`agents/core-logic/`	Worker (pusher kostdata til state store)	`dask.delayed`-task	Nei — strukturert data	Nei
rag-agent	`agents/rag/`	Worker (pusher retrieval-resultater)	`dask.delayed`-task	Ja — `partial_fit` på nye docs (ML-2)	Ja
memory-agent	`agents/memory/`	Worker (pusher/puller memory-oppslag)	`dask.delayed`-task	Nei — faktahenting	Nei
eval-agent	`agents/eval/`	Worker (pusher evalueringsmetrikker)	`dask.delayed`-task	Ja — kvalitetsscore-feedback (ML-2)	Nei
multi-agent	`agents/multi_agent/`	Worker + sub-orchestrator	DAG-node med barn	Nei	Ja — primær heavy-mode bruker
tui-bridge	`vauco-gemini-tui-bridge`	Worker + primær input-kilde	Streaming input til DAG	Ja — real-time feedback-loop (ML-2)	Ja
vauco-bootstrap	`vauco-bootstrap`	Config-aggregator (analog til PS shard)	Trigger for nye DAGer	Ja — ASHA-tuning (ML-2)	Nei

ML-fase per agent

Agent	ML-1 (nå)	ML-2 (neste)	ML-3 (fremtidig)
Jason Vauger	Naturlig-språk interface mot OPAX	Kontekstuell tilpasning til bruker	Personlig læringsloop + proaktive forslag
OPAX Orchestrator	Kjør DAG via `execute_dag()`	Bytt heavy-mode til ML-predictor	PBT-eksperiment
billing-agent	`log_agent_call()` + `state.push()`	—	Feature importance-input
rag-agent	`log_agent_call()` + `state.push()`	`partial_fit` på nye docs	Top-K feature importance
memory-agent	`log_agent_call()`	—	—
eval-agent	`log_agent_call()` + `state.push()`	Kvalitetsscore-feedback	Feature importance-target
multi-agent	`log_agent_call()`	—	PBT worker
tui-bridge	`log_agent_call()`	`feedback_loop.py` (ML-2.1)	—
vauco-bootstrap	—	`hypertuner.py` ASHA (ML-2.3)	PBT vinner-konfig → Terraform

State store nøkkelkonvensjon

Format: "<agent_id>:<key>"

Agent	Nøkkel	Eksempelverdi
`billing-agent`	`last_cost_usd`	`12.5`
`billing-agent`	`last_report_ts`	`"2026-05-26T01:00:00Z"`
`rag-agent`	`last_retrieval_score`	`0.87`
`rag-agent`	`chunks_retrieved`	`5`
`eval-agent`	`quality_score`	`0.91`
`multi-agent`	`active_sub_agents`	`["billing-agent", "rag-agent"]`
`tui-bridge`	`last_user_feedback`	`"positive"`

Telemetri-felter (referanse)

Alle agenter logger via ml.telemetry.log_agent_call(). Feltene er:

{
  "ts": "2026-05-26T01:00:00.000Z",
  "agent": "billing-agent",
  "model": "gemini-2.0-flash",
  "mode": "standard",
  "duration_s": 0.312,
  "success": true,
  "error": null,
  "input_tokens": 284,
  "output_tokens": 512
}

Filer lagres i: $TELEMETRY_DIR/YYYY-MM-DD.jsonl (default: /tmp/vauco_telemetry/)
For GCS-arkivering: sett TELEMETRY_DIR=gs://... eller bruk 10-cost-guard.sh-cron.

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