A team of AI agents, each with a different role, collaborating, challenging each other's ideas and reviewing each other's work. Pick a project and a collaboration pattern, press Run simulation, and watch a turn-by-turn transcript stream in while messages fly between agents in 3D. Drag to orbit, scroll to zoom.
Each agent gets its own system prompt, tools and context window. A "Critic" told only to find flaws finds more flaws than a generalist asked to "also check your work" — the same model behaves differently under a narrow role. Separate contexts also stop one long task from polluting another (context isolation). Cost: you pay tokens per agent, and information must be explicitly passed between them.
Proposer–critic loops measurably reduce sloppy first drafts: the critic's job is adversarial, so errors that survive self-review get caught. Tradeoffs: loops can oscillate (endless nitpick cycles) or collapse into sycophancy where the critic starts agreeing. Production systems cap rounds (2–3) and require the critic to cite concrete, checkable objections.
One lead agent decomposes the task, dispatches workers in parallel, then merges results — this is how Claude Code subagents and most agent frameworks structure delegation. Strength: parallelism plus a single point of coherence. Weakness: the orchestrator is a bottleneck and a single point of failure — a bad decomposition means every worker does the wrong job. Pipelines are simpler but serial: latency stacks up per hop.