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.
Fleet management is the operational discipline around that collection. It covers the full lifecycle — registering an agent so the org knows it exists, starting and stopping it, monitoring what it does, governing what it is allowed to do, requiring human approval at high-risk steps, and auditing every decision after the fact — applied consistently across teams, repositories, tools, and environments rather than ad hoc on individual laptops.
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 (a runaway agent can burn hours of run time), 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.