Agentic ML Modeling Loop Simulator & Guardrail Workbench

Target: Held-out MAE < 4.28m | Latency budget < 45ms

Agent Hyper-Strategy GRIFFIN RUNNER

Exploration Policy

Deterministic Guardrails

Temporal Point-in-Time Check Prevents joining delivery completion timestamps onto order dispatch features

Eval Split Isolation Strict separation between modeling agent & held-out evaluation harness

Seed-Variance Battery Rejects lucky one-off seeds lacking 5-fold cross-seed validation

Latency Envelope Budget Disallows bulky ensembles exceeding 45ms P99 routing SLA

Human-in-the-Loop Steering

Optimization Progression Curve Phase 3: Smaller Compound Gains

Accepted Trials 4 26 rejected / 30

Current Best Metric 4.12 min -3.74% vs baseline

Guardrail Incidents 1 Temporal leak caught

Inference Latency 28.4 ms < 45ms budget

Best-So-Far Frontier

Accepted Breakthrough

Rejected Plateau

Guardrail Flagged / Leak

Hypothesis Execution Stream (Click trial node or row to inspect) Showing 30 trials

Trial Inspector Trial #28 (Accepted)

Tested Hypothesis

Combine Huber loss (delta=1.2) with recency weighting and 5-seed model averaging to dampen long-tail delivery outliers and improve stability.

Agent Code Delta / Griffin Config

model_config = {
  "family": "lightgbm",
  "objective": "huber",
  "huber_delta": 1.2,
  "recency_decay_halflife_days": 14,
  "num_leaves": 48,
  "min_child_samples": 35,
  "seed_averaging_count": 5
}
            

Validation Evidence & Guardrail Check

Instacart MLE Interpretation

Early on, increasing leaf capacity caused overfitting. Once seed averaging and recency weighting stabilized variance, additional capacity safely pushed error down.

Structured Agentic Modeling Ledger Production Ready

Diffusion of agentic knowledge capturing winning architecture, rejected dead-ends, and safety violations.

Winning: LightGBM + Huber Loss (delta=1.2) + Recency Weighting + 5-Seed Averaging

Key Insights across 30 Experiments: Moving from the histogram baseline to LightGBM yielded the initial step change (4.28 → 4.22 MAE). A separate tuned-MLP branch with learned embeddings achieved competitive error but approached the 45ms latency ceiling. The winning configuration compound gains arrived through recency weighting (14-day halflife), Huber loss thresholding, and seed averaging across 5 random seeds, delivering a 3.74% reduction in held-out MAE (4.12m). One severe temporal join leakage bug was caught deterministically in trial #14 before evaluation contamination occurred.