02 · Agentic Engineering

AI-augmented development with statistical gates

Personal systems + applied at HeadHuntersHQ · AI-assisted workflows, orchestration, verification

3-5wk → <1wk feature release cycle

-30% production incidents

2,511 hypotheses tested

~126 isolated as false positives

The problem

Most teams "use AI" as autocomplete. That produces fast code and slow systems — the bottleneck moves to verification. My position: treat agents as junior engineers with a strict CI gate, and treat their output as a hypothesis to be tested, never a result to be trusted.

What I built

Multi-agent delegation: implementation agents work in isolated contexts with file-ownership contracts; a lead agent audits contracts, builds, lint, and browser-level verification before anything merges.
A validation discipline for quantitative work: permutation tests, Bonferroni correction, effect-size and mutual-information thresholds, out-of-sample splits — run before any pattern is believed.
Applied at work: this workflow is the mechanism behind the release-cycle and incident improvements in case 01.

Principle	Why
Agents get contracts, not prompts	Verifiable outputs, not vibes
Every claim needs a test	2,511 tests → ~126 false positives without correction. Uncorrected "discoveries" are noise.
Human judges tradeoffs, agents do mechanics	Judgment stays senior; execution parallelizes

Brief + contractspec, file ownership

Agent workersisolated contexts

Verification gatebuild · lint · runtime

Statistical gatepermutation · Bonferroni

Mergehuman sign-off

Outcome: releases that took 3-5 weeks now ship in under a week with fewer incidents — because AI accelerated execution and the gates caught what AI gets wrong.