Forward Deployed AI Researcher · Agentic AI Engineer

_{Building verifiable, long-horizon agent environments · Enterprise AI & GTM}

Gradia · Snorkel AI · Axiom Consulting

I’m a forward deployed AI researcher and agentic AI engineer building systems that make autonomous behavior observable, replayable, and independently verifiable.

My work sits where agent infrastructure meets experimental science: record what happened, challenge the evaluator, preserve the evidence, and make every important claim reproducible by someone else.

My upstream work is discovered automatically across public repositories outside my account. A project enters when GitHub proves accepted work through an authored merged PR on any upstream branch or an authored commit on the default branch. GitHub-listed contributor is the highest attribution tier and is applied automatically when GitHub's Contributors API catches up. Stars and forks describe repository reach, not personal credit.

Owned public source is a separate signal: GitHub lists me as the repository owner. These projects are discovered automatically from my public, non-fork, non-archived repositories, then ranked by stars, recent activity, and GitHub-attributed commits. Ownership is not counted as upstream contributor credit.

• ProofSeal: public owner repository with 13 GitHub-indexed commits. Repository reach: 2 stars and 0 forks; last pushed 2026-06-13.
• Gradia Guard: public owner repository with 10 GitHub-indexed commits. Repository reach: 1 star and 0 forks; last pushed 2026-10-02.
• TestLore: public owner repository with 99 GitHub-indexed commits. Repository reach: 1 star and 0 forks; last pushed 2026-10-01.
• scrapmind: public owner repository with 8 GitHub-indexed commits. Repository reach: 0 stars and 0 forks; last pushed 2026-10-03.
• ghostpart: public owner repository with 13 GitHub-indexed commits. Repository reach: 0 stars and 0 forks; last pushed 2026-10-01.
• nerve: public owner repository with 3 GitHub-indexed commits. Repository reach: 0 stars and 0 forks; last pushed 2026-09-28.
• gradia-wind-tunnel: public owner repository with 83 GitHub-indexed commits. Repository reach: 0 stars and 0 forks; last pushed 2026-09-03.
• gradia-universes-work-sample: public owner repository with 34 GitHub-indexed commits. Repository reach: 0 stars and 0 forks; last pushed 2026-09-03.

_{Showing 8 of 20 qualifying owned public repositories · browse every public source repository · complete evidence retained in machine-readable data}

_{Last verified 2026-10-06 UTC · visual + evidence refreshed every 30 minutes by GitHub Actions · machine-readable evidence}

Agentic Power asks a practical question: how much accepted skilled work can one hour of human direction command?

I design for that multiplier, but I won’t publish one without exposing its scope, evidence, assumptions, and uncertainty. My operating model is to delegate meaningful responsibility, compose the right tools and context, verify the result, then use the evidence to improve the next run.

AP ≈ 110.9× (operator-calibrated provisional estimate): approximately 15,948.6 skilled Human-Equivalent Hours, or 398.7 engineer-weeks, divided by 143.8 operator-estimated human-direction hours. The transparent scenario range is 70.7× to 177.4×.

The evidence base since January 2025 combines 884 merged upstream PRs with 6,532 authored, non-merge default-branch commits across 109 currently accessible repositories: personal, private, employer, and open-source alike. Work such as Gradia is included only as a redacted aggregate: no repository names, commit messages, code, links, employer, or client details are published.

The conservative GitHub activity proxy produces 2,251.3 direction hours because it assigns attention to individual commits and repository-months. Operator recall is 1.5–2.0 active direction hours per week; across 82.2 weeks, that calibrates the denominator to 123.3–164.4 hours, with 143.8 as the midpoint.

Direction includes active briefing, steering, reviewing, correcting, and coordinating. It excludes agent runtime and waiting. The calibration is operator-estimated rather than reconstructed from time logs, so this remains a transparent scenario, not a completed Full Evidence Audit.

_{Framework and formula · calculation evidence · public upstream evidence and visuals refreshed every 30 minutes; redacted repository snapshot retained until a private read credential is available}

• Direct: goals, constraints, acceptance criteria, and escalation rules stay explicit.
• Orchestrate: models, specialist agents, reusable skills, tools, memory, and loops become one working system.
• Verify: Gradia Guard, ProofSeal, and replayable evaluations separate completed work from plausible-looking activity.
• Improve: Wind Tunnel and Reward Loop turn measured failure into a better next run.

_{Framework concept by Dr. Mark Allen / HeroForge.AI. Visual and operating-model adaptation are original to this profile.}

Can we trust the record?      → proof-bound execution evidence
Can we trust the score?       → adversarial evaluator stress testing
Can we reproduce the result?  → deterministic replay + committed artifacts
Can an agent remember?        → regression memory before commit

The thread through all of it is simple: an AI system should be able to show its work without asking you to trust the AI system.

• Gradia Universes — proof-carrying, interruption-capable synthetic agent worlds with deterministic replay.
• Gradia Reward Loop — oracle-witnessed reward-hacking experiments and a replay-verified paired-GRPO diagnostic.

I build communities as deliberately as systems. I lead AI Tinkerers Charlotte, a demo-first room for people shipping real AI systems, and serve as Regional Ambassador for the Agentics Foundation, connecting builders working on practical agentic AI.