Four ways I help engineering teams ship better.
Agentic Engineering Adoption
I coach engineering teams through the shift from writing code to specifying intent. Claude Code & Cursor rollouts, spec-first workflows, and supervisory protocols — so your seniors stop drowning in machine-generated code.
Custom Agent Infrastructure
embry0-style autonomous pipelines integrated into your dev workflow. Sandboxed Docker, no customer code retention, GitHub-issue-to-PR with triage / developer / reviewer / QA agents and human-in-the-loop checkpoints.
See embry0 →Specification & Test Rigor
I turn fuzzy PRDs into AI-ready specs: state machines, decision tables, hallucination-proof test suites. The same standards safety-critical avionics teams apply to flight hardware — applied to AI-generated code.
Senior Engineering Work
Hands-on architecture and development. When you need a senior who can ship the thing, not just talk about how to ship it. Backend systems, agent infrastructure, secure networking, full-stack.
embry0 — autonomous agent orchestration
for GitHub-driven teams
embry0 is a multi-agent orchestration engine I built that autonomously resolves GitHub issues — from triage to pull request — inside isolated Docker sandboxes.
A triage agent analyzes each issue and configures the pipeline. A developer agent owns the full lifecycle inside its sandbox: code changes, git operations, and PR creation. A reviewer agent gates every change, and an optional QA agent boots the real app to validate it end to end before it ships. Humans stay in the loop via a React control-plane dashboard, Telegram, or GitHub comments.
Architecture choices that matter: no customer code retention (every container is destroyed after the job), credential-injection proxies (API keys never enter the sandbox), and dynamic network isolation (agents get internet only when researching). embry0 ships as an on-prem Docker image — I deploy and configure it inside your infrastructure.
embry0 pipeline — issue → triage → developer → reviewer → QA → PR
Typing code is no longer the job.
AI hasn't made engineering easier — it's moved the bottleneck upstream. Code is disposable; specifications are the product. And the supervisor who doesn't understand what the system is doing is the one who crashes the plane.
Who I work with.
Engineering teams of 5–30 at companies that ship software as a core part of the business.
Teams already using or seriously evaluating Claude Code, Cursor, or similar AI coding tools.
Senior engineers who feel like they're drowning in machine-generated code.
If that sounds like you, we should talk.
Behind Eastwood AI.
I spent my career in domains where software failure isn't an inconvenience: avionics communication platforms, embedded firmware for industrial control systems, and secure network infrastructure for high-assurance environments.
The through-line: every system I've shipped has been one where "kind of working" is unacceptable. Safety-critical hardware. Regulated environments. Networks carrying traffic nobody can afford to leak. AI-generated code in production needs the same standard.
Eastwood AI is how I help engineering teams hold that line while moving faster than ever — through coaching, custom agent infrastructure, or hands-on engineering work.
Let's talk.
Free 30-minute intro call. No pitch deck, no sales pressure — we figure out together whether we're a fit.