OSVauco/docs/ml-integration.md

ML-integrasjon i OSVauco

Fase: ML-1 implementert | ML-2 og ML-3 planlagt
Sist oppdatert: 2026-05-26

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Hvorfor ML i OPAX?

OPAX er et multi-agent system. ML-laget gjør agentene sterkere på tre måter:

1. Parallell kjøring (ML-1): Dask DAG lar agenter kjøre samtidig i stedet for sekvensielt.
2. Adaptiv intelligens (ML-2): Agentene lærer fra faktisk bruk — heavy-mode aktiveres basert på historikk, ikke hardkodede regler.
3. Strategisk innsikt (ML-3): XGBoost feature importance avslører hvilke agenter og signaler som faktisk driver vellykkede utfall.

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Fase ML-1: Grunnlag (implementert)

Arkitektur

main.py  →  build_agent_dag([agent_a, agent_b], payload)
                    │
             Dask task graph
                    │
         ┌──────────┴──────────┐
         ▼                     ▼
   run_agent_task(a)    run_agent_task(b)    ← parallelt
         │                     │
         └──────────┬──────────┘
                    ▼
           execute_dag(tasks, scheduler="threads")
                    │
                    ▼
           [result_a, result_b]  →  AgentStateStore.push()
                                            │
                                     telemetry.log_agent_call()

Koble ML-laget inn i main.py

# Eksempel: erstatt sekvensielle kall med parallell DAG
from ml import build_agent_dag, execute_dag, get_store, log_agent_call
import time

@app.post("/run")
def run_agent(req: RunRequest):
    store = get_store()
    payload = {"message": req.message, "user_id": req.user_id,
               "session_id": req.session_id, "mode": req.mode}

    # Velg agenter basert på mode
    if req.mode == "heavy":
        agent_fns = [billing_agent_heavy, rag_agent, multi_agent]
    else:
        agent_fns = [billing_agent, rag_agent]

    start = time.monotonic()
    tasks = build_agent_dag(agent_fns, payload)
    results = execute_dag(tasks, scheduler="threads")
    total_dur = time.monotonic() - start

    # Push resultater til state store
    for r in results:
        if r["success"]:
            store.push(r["agent"], "last_result", r["result"])
            store.push(r["agent"], "last_duration_s", r["duration_s"])

    # Logg DAG-kjøring
    from ml.telemetry import log_dag_execution
    log_dag_execution(dag_id=req.session_id, agent_results=results,
                      total_duration_s=total_dur)

    # Returner kombinert svar
    return {"response": [r["result"] for r in results if r["success"]]}

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Fase ML-2: Adaptiv intelligens (planlagt)

Komponent	Fil	Repo
Inkrementell feedback-loop	ml/feedback_loop.py	vauco-gemini-tui-bridge
ASHA hyperparametertuning	ml/hypertuner.py	vauco-bootstrap
Heavy-mode predictor	ml/heavy_predictor.py	OSVauco

Krav: dask-ml>=2024.4.0, ray[tune]>=2.10.0 (vauco-bootstrap only)

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Fase ML-3: Strategisk innsikt (planlagt)

Komponent	Fil	Repo
XGBoost feature importance	ml/agent_importance.py	OSVauco
Population-Based Training	experiments/pbt.py	deep-dream

Krav: xgboost>=2.0.0 (legg til requirements.txt i Fase ML-3)

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Telemetri-ruting

Miljø	TELEMETRY_DIR	Arkivering
Lokal dev	/tmp/vauco_telemetry/	Manuell
Cloud Run	/tmp/vauco_telemetry/	10-cost-guard.sh cron → GCS
Prod skala	gs://propane-will-491900-m5-telemetry/	Auto via GCS sink

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Cross-repo referanser

Repo	ML-relatert fil	Fase
OSVauco	ml/, AGENTS.md, docs/ml-integration.md	ML-1
vauco-gemini-tui-bridge	ml/feedback_loop.py (planlagt)	ML-2
vauco-bootstrap	ml/hypertuner.py (planlagt)	ML-2
vauco-os	Konsumerer beste konfig fra state store	ML-2
deep-dream	experiments/pbt.py (planlagt)	ML-3