Offer a structured team engagement that audits current AI coding workflows, creates repo-specific agent instructions, and runs hands-on practice labs where engineers alternate between AI-assisted implementation, manual reconstruction, code reading, and review. The service produces team playbooks for specification writing, agent prompting, generated-code review, and periodic no-AI practice days. Over time, the offer can productize into templates, assessment rubrics, manager dashboards, and recurring coaching for engineering teams.

LLM^? coding tools are now common enough that engineering managers face practical adoption problems, not theoretical ones. The signals show growing concern around skill atrophy, agent loops, code comprehension, and mentoring juniors in an AI-assisted environment.