Peer agents are a multi-agent system with no boss. Several agents talk to each other directly, share state, and coordinate through protocols rather than a central orchestrator. It's the pattern people imagine when they hear "multi-agent AI." It's also the pattern that's hardest to debug and easiest to make too complex. For most production use cases, orchestrator-worker wins. Peer agents are for specific problems where no single agent has the full picture.
In orchestrator-worker, information flows up-and-down. The orchestrator knows everything; workers know only their slice. In peer systems, agents talk sideways. Agent A sends a message to agent B. Agent B replies to A, or routes to C, or writes to a shared blackboard others read. No agent is the authoritative decider.
Peer systems pay a real cost:
Task: "Should we build feature X?" You spin up three peer agents: a "PM persona" agent (user value), an "engineer persona" agent (feasibility, cost), and a "CFO persona" agent (revenue impact). They exchange arguments for 3 rounds, each building on what the others said. A fourth "facilitator" agent summarizes the debate and surfaces the key tradeoffs.
No single agent could do this well because no single agent has the right mental model. The interaction surfaces tradeoffs none would have identified alone.
Task: "Summarize recent news about Apple from the web, our internal brief, and a financial database."
Tempting to make this peer agents. Don't. This is orchestrator-worker: one orchestrator, three workers, parallel fetches, synthesis step. No agent needs to talk to any other agent. Making it peer adds complexity without benefit.
Start with orchestrator-worker. Graduate to peer agents only when the task genuinely benefits from agent-to-agent interaction, and you can accept the debugging cost. 90% of production multi-agent systems are orchestrator-worker in peer-agent clothing. Simplify.