Every team is being asked to use more AI for their coding, but more doesn't mean better. The right amount of AI use depends on the constraints the team (and organization) is under. Use more AI than your constraints support and you're just shipping defects faster. Use less and you're leaving velocity on the table. The expensive failures are mismatches.

In this talk, we show how to match your team to one of three modes of agentic coding, where AI agents write, test, and change code with growing autonomy. Individual mode lets each developer choose their own harness or workflow, at the risk that learning stays with the individual. Shared mode checks domain context files, guidelines and standards, and tools and skills into a shared repo, where improvements compound across the team. Orchestrated mode grows that shared infrastructure into a workflow agents can run semi-autonomously, with humans at defined checkpoints. Which mode you can sustain comes down to six constraints, from whether domain knowledge lives in heads or written down, to whether you must inspect every line or can verify outcomes. The upside of a higher mode is throughput; the cost is infrastructure and verification built first. We walk through three real teams, one per mode, all successful in their own situation, including one mid-transition from individual to shared. For teams deciding how far to push, you leave able to de-risk the decision with a clear idea of what needs to change.