Single-agent coding has a natural ceiling: one agent context window limits how much of a large codebase it can reason about at once, and one agent cannot review its own code with genuine independence. Multi-agent coding distributes the work so that each agent specializes. A developer agent focuses on implementation; a separate reviewer agent approaches the PR without the implementation bias that produced it.
Coordination between agents can be synchronous (agent A waits for agent B's response before proceeding) or asynchronous (agent A publishes a result to a shared bus and moves on; agent B picks it up when ready). Asynchronous coordination scales better because agents do not block each other, though it requires more careful event schema design.
Effective multi-agent coding requires more than just running several agents in parallel. Agents need shared context (which branch, which issue, which ticket), conflict detection (two agents should not edit the same file simultaneously), and a reconciliation strategy when agents disagree — for example, when a reviewer requests changes the developer agent later reverts.