Agentic engineering at full speed. Release confidence you can defend.

I get inside the codebase, delivery process, team, and commercial pressure fast, then separate real risk from noise.

Every risk and opportunity is plotted on value versus effort, read through your release-confidence signals rather than a generic grid, so the sequence is obvious and defensible.

The priorities become a phased route: quick wins in the first 30 days, the heavier structural work across 60 and 90, so momentum starts immediately.

Automated tests, from the smallest piece up to whole user journeys, that prove the experience still works before anything changes, paired with visual regression that catches the style breakage those tests can’t see.

Formatting, code checks, tests, security scans, and complexity warnings that run automatically on every change, so the quality floor holds itself without anyone policing it.

The standards and conventions your team works to, written down so people and the agents follow the same playbook, with context tooling like Graphify that keeps AI usage efficient from day one.

Tighten the review cycle itself and extract the patterns worth repeating, so quality compounds instead of depending on individual heroics.

The right model, the right harness, and the right optimisation tools, working as one. An agent-integrated IDE for hands-on work, a powerful agent harness for the heavy lifting, and the tooling that keeps both fast and in context. The exact tools vary by model; the shape stays the same.

Mix models so you are never without capacity. Plan with a premium model, execute with a cheaper, high-quality one, and keep an always-available model in reserve. Premium usage runs out fast, and the workflow has to keep moving when it does.

The layer that makes agents reliable instead of wasteful. The tools are built as hard code, not agents, and wired into your systems (n8n, Zapier, or your own), so an agent check is one trustworthy call instead of twenty. A library of shared skills means the team solves a predictable task once, not three times: consistency, compounding knowledge, fewer tokens burned.

The agents work inside the same pipeline your team ships through, and across how work is planned, reviewed, and released, with an AI reviewer built from your team’s own best patterns and your review style, so changes arrive review-ready. Delivery gets reimagined around agents, not bolted on.

Direct the work from your phone or a chat thread. Kick off a change on the train, review it over coffee. Senior people steer from anywhere, not just at a desk.

Persistent agents that work while you sleep: overnight audits, dependency checks, failure triage, changes drafted from the backlog and ready for review. A small team ships like a much bigger one, and the system’s knowledge compounds instead of resetting every sprint.