An AI agent fleet treats software development like a staffing problem: each role in a typical engineering team gets an AI counterpart with a defined scope of work. A developer agent branches, implements, and opens pull requests. A reviewer agent reads the diff and approves or requests changes. A release manager agent handles the merge gate and deployment signals.
Coordinating multiple agents introduces challenges absent from single-agent setups: sequencing (review can't start before the PR exists), conflict avoidance (two developer agents shouldn't touch the same file), budget control (each agent can rack up significant token cost), and auditability (you need to know which agent made which decision and why).
Effective fleet management requires an event system for handoffs, role definitions that prevent overlap, risk controls, and a shared audit trail. Without these, a fleet degrades into uncoordinated agents stepping on each other's work.