SERVICES / AI ENGINEERING ENABLEMENT
Adopt AI coding agents without wrecking your codebase.
A deliberate rollout of Claude Code and multi-agent development workflows, with the review gates, hooks, and team conventions that keep the speed-up from turning into a quality problem.
01
WHO THIS IS FOR
- Engineering leads whose teams are already using AI coding agents informally and want it made deliberate, with consistent conventions and real review discipline.
- Teams evaluating whether to adopt agentic coding tools and want a realistic picture of what changes (review process, PR size, testing discipline) rather than a vendor pitch.
- Organizations where AI-assisted development has to coexist with strict requirements (security-sensitive codebases, regulated data, protected branches) without loosening them.
02
WHAT'S INCLUDED
- Hands-on rollout of Claude Code (or comparable agentic coding tools) on your actual codebase rather than a toy repo. The friction that matters only shows up on real code with real constraints.
- Custom automation: repo-specific skills, hooks (e.g., blocking direct commits to protected branches, enforcing pre-merge checks), and CLAUDE.md-style project context so the agent operates inside your team's actual invariants instead of guessing at them.
- Review process design for AI-authored or AI-assisted changes: what needs human review before merge, what can run through automated gates, and how to keep PR size and diff quality sane when generation is fast.
- Multi-agent workflow patterns for larger tasks: fanning out independent work into parallel agents/worktrees, and the coordination discipline that keeps parallel AI-assisted work from stepping on itself.
- Pairing sessions and documentation so the practice survives after I leave. This isn't a one-time demo.
03
ENGAGEMENT SHAPES
- Assessment (1 week): review current AI-tool usage (or lack of it) on your codebase and team process; deliver a concrete adoption plan scoped to your actual risk tolerance and stack.
- Sprint (2–4 weeks): implement the guardrails — hooks, skills, CI gates, project context files — and run the team through real usage on live work.
- Retainer: ongoing refinement as the tools and your codebase both evolve; this space moves fast enough that "set it up once" tends to go stale within a quarter.
04
FAQ
Q1Will this mean our team ships lower-quality code faster?
The explicit goal is the opposite: faster iteration with the same or better review bar, via automated gates (lint, typecheck, tests, protected-branch hooks) that catch what human review would otherwise have to catch manually every time.
Q2I'm not an engineer — is this for me?
This page is written for engineering teams, but there's a version of this work for founders building without one: I stand up the product foundation, configure the AI workflow around it, and hand it off so you keep building yourself. That offer lives at devstrike.us/founders, in plain English.
Q3Do we need to already be using Claude Code to work with you?
No. Engagements range from "we've never tried this" to "we're using it inconsistently and want it made deliberate." The assessment adapts to wherever you're starting from.
Q4Is this specific to Claude Code, or does it generalize to other AI coding tools?
My deepest day-to-day experience is with Claude Code specifically, including its multi-agent and hooks capabilities. The underlying principles (deliberate context files, automated review gates, scoped parallel work) transfer to comparable tools. Tell me what you're evaluating or using during the intro call.
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