AI Development Workflow Consulting

What does an AI development workflow consultant actually set up?

Four things: repo context an agent can read, a verification harness that runs automatically, a review loop with an explicit human gate, and custom commands for work your team repeats. Tool configuration is the smallest part. The harness decides whether the speed is real or borrowed.

A good CLAUDE.md is not a style guide — it is a map of what an agent cannot infer from the code: which directory owns what, which patterns are deliberate versus legacy, the commands that verify a change, and the few rules that must never break. Keep it under a page. Most of the value sits in what it forbids.

The harness is the part teams skip and then regret. This site runs the pattern it recommends: TypeScript strict, ESLint at zero errors, and scripted gates that machine-check meta tags, structured data, internal links, images, and a banned-claims list against rendered HTML, plus Playwright smoke tests. An agent cannot argue with a failing script.

How do you stop AI-written code reaching production unverified?

By making verification cheaper than review. Automated gates catch everything mechanically checkable, so human attention goes only where judgment is required — authorization logic, data modeling, money movement, and anything touching customer records. Reviewers stop skimming diffs for typos and start reading the files that matter.

The review loop needs a written rule, not a vibe. The version I recommend: an agent may open a pull request and never merge one; a failing gate is fixed, never overridden; the agent may not edit a test and the code it covers in the same commit; and any change touching authentication, authorization, payments, or personal data is read line by line by a human who knows the domain. Every automated change stays attributable, so a breakage six weeks later can be reconstructed rather than guessed at.

When does AI assistance cost more than it saves?

When verification is expensive and the answer is plausible. Agents excel where a test or a compiler can immediately say no. They are dangerous in security-critical logic and novel architecture, because wrong output there looks exactly like right output and the feedback loop is measured in incidents.

The honest framing is that AI assistance changes where effort goes, not how much you need. You spend less time typing and more time specifying and verifying. Teams that plan for that shift get faster; teams expecting the old review process to absorb three times the diff volume get slower and blame the tool. Anyone quoting you a fixed percentage improvement is guessing.

What is the receipt for this, and what is not claimed?

The receipt is an ongoing client delivery where Claude Code is the primary tool — full-stack work plus n8n agent workflows, run through the harness this page describes rather than argued for in the abstract. What is still not claimed is a team rollout: no engineering organization has hired me to configure their tooling and review loop across a group of developers, so enablement remains a service on offer rather than a shipped reference.

Those two things fail differently, which is why the distinction is kept. Delivering client work with an agent as the primary tool tests the harness against real deadlines and a real reviewer. Rolling a workflow out across someone else's team tests something else entirely — whether other engineers keep the review rule when it slows them down, and whether the gates survive contact with an existing codebase's habits. I have the first. I do not have the second, and anyone selling you the second on the strength of the first is guessing.

This site is the second demonstration, built and maintained the same way across 21 published articles and 66 indexable pages, and the outcome is inspectable rather than asserted: a live Lighthouse of 94 performance and 100 across accessibility, best practices, and SEO, on a site whose copy is script-checked for stale claims before it can go live. That last gate is the interesting one — a factual regression in prose fails the build the same way a type error does.

The engineering standard behind the recommendations is documented separately in client work: 248 passing tests and zero production bugs on an event-sourced fintech backend, 74 test files across 79 migrations on an AI SEO platform, and a HIPAA practice management system that passed an independent security assessment. Those prove the review discipline, not the rollout engagement.