sdlc-practices

Coordinate AI agent teams by enforcing SDLC discipline with dedicated feature branches and isolated worktrees. This prevents branch contamination and ensures scoped, gated development across project layers.

Enforce code review gates in a multi-agent workflow by mandating explicit prompt scopes and establishing repeatable peer-review cycles. This ensures alignment with design documents before gated merges occur.

Plan and dispatch work packages to AI agents by following a structured SDLC checklist. This involves decomposing tasks, defining explicit scope boundaries, and dispatching targeted agents for implementation scouting.

Yes, your team workflow needs design-to-implementation checkpoints for AI-generated code to prevent scope creep and disjoint coordination. These checkpoints bridge design docs with implementation through repeatable reviewer cycles.

Yes, you can use an SDLC process to manage scope creep when orchestrating multiple AI agents. Mandating explicit scope in prompts and enforcing task decomposition keeps agents aligned with assigned work packages.

Ad-hoc team workflows for AI agents lack branch isolation, explicit scoping, and review gates, leading to disjoint coordination and missing review cycles. Without a disciplined SDLC process, multi-agent development risks branch contamination and scope creep.