Agentic Learning Series — Tabular ML Pipeline Demo

A multi-agent ML pipeline that turns a client’s spreadsheet into a trained model and a one-page decision-support report — all inside hardened NemoClaw sandboxes.

One-line story: “Drop a CSV in, get a model and a report out — four agents, two sandboxes, zero trust.”

What It Does

Architecture

Two NemoClaw sandboxes with different security levels:

• data-pipeline — NVIDIA inference API + local gateway only
• reporter — Zero network (completely blocked)

Both use Landlock filesystem locks, seccomp syscall filtering, and run as an unprivileged sandbox user.

Prerequisites

• NemoClaw installed — nemoclaw and openshell on PATH
• Hermes installed — hermes on PATH with a configured model and provider
• Docker — running (for OpenShell sandboxes)
• NVIDIA inference API — configured in ~/.nemoclaw/credentials.json

Installation

Run from the repository root.

1. Create the sandboxes

Each uses an interactive wizard. When prompted, select:

• Agent runtime: hermes
• Model: nvidia/nemotron-3-super-120b-a12b (or your preferred model)
• Inference provider: NVIDIA prod (or your configured provider)

nemoclaw onboard --name data-pipeline
nemoclaw onboard --name reporter

2. Apply network policies

# data-pipeline: allow only NVIDIA inference + local gateway
openshell policy set --policy policy/demo-pipeline-restricted.yaml data-pipeline --wait

# reporter: zero network (completely blocked)
openshell policy set --policy policy/reporter-restricted.yaml reporter --wait

3. Upload files to data-pipeline sandbox

# Data, scripts, agent config, requirements
for f in data/raw/telco-churn.csv scripts/prepare.py scripts/train.py \
         scripts/render_report.py AGENTS.md requirements.txt; do
  openshell sandbox upload data-pipeline "$f" "/sandbox/$f"
done

# Agent skills (preprocessor, architect, trainer)
for role in preprocessor architect trainer; do
  openshell sandbox upload data-pipeline \
    "skills/$role/SKILL.md" "/sandbox/skills/$role/SKILL.md"
done

4. Upload files to reporter sandbox

for f in scripts/render_report.py AGENTS.md requirements.txt; do
  openshell sandbox upload reporter "$f" "/sandbox/$f"
done
openshell sandbox upload reporter \
  "skills/reporter/SKILL.md" "/sandbox/skills/reporter/SKILL.md"

5. Install Python dependencies inside sandboxes

openshell sandbox exec -n data-pipeline \
  bash -c 'python3 -m venv /sandbox/.venv && /sandbox/.venv/bin/pip install -r /sandbox/requirements.txt'

openshell sandbox exec -n reporter \
  bash -c 'python3 -m venv /sandbox/.venv && /sandbox/.venv/bin/pip install -r /sandbox/requirements.txt'

6. Create local Python venv

For running the pipeline locally (outside sandboxes):

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

7. Set up Hermes profiles

Creates profiles for each agent role (preprocessor, architect, trainer, reporter), inheriting your default model configuration:

bash scripts/setup_profiles.sh

8. Verify

nemoclaw list
openshell policy get data-pipeline
openshell policy get reporter

Running the Pipeline

Full run (all four stages)

source .venv/bin/activate
bash scripts/run_pipeline.sh data/raw/telco-churn.csv Churn

Step by step (recommended for demos)

source .venv/bin/activate

# 1. Preprocess — clean data and generate profile
hermes -p preprocessor chat -t terminal,file \
  -q "Process data/raw/telco-churn.csv with target=Churn" --yolo

# 2. Architect — queue 2-3 model configs based on profile
hermes -p architect chat -t file \
  -q "Read data/clean/profile.json and queue 2-3 configs" --yolo

# 3. Train — execute every queued config
hermes -p trainer chat -t terminal,file \
  -q "Drain runs/queue/" --yolo

# 4. Report — generate client-facing report
hermes -p reporter chat -t terminal,file \
  -q "Render the final report" --yolo

Security demo

bash scripts/demo-security.sh

Demonstrates network egress blocking, Landlock filesystem enforcement, process isolation, and data separation.

Expected Output

After a full run:

data/clean/clean.parquet       # Cleaned dataset (12,330 rows x 20 features)
data/clean/profile.json        # Data profile with target, dtypes, missingness
runs/results.tsv               # Append-only ledger (3 rows: LR, XGB, LGBM)
runs/live/best.json            # Winning run summary
models/.pkl            # Trained model artifacts
reports/final.md               # Client-facing report with executive summary,
                               # model comparison, SHAP feature importance,
                               # and caveats

Restart from scratch

rm -rf data/clean/ runs/ models/ reports/

Then re-run the pipeline stages. data/raw/ is untouched.

Troubleshooting