Orchestrating Design Agent Teams

The decision this whole course hinges on: multi-agent setups carry a real coordination tax, and most tasks do not repay it. This module covers the signals that a task genuinely splits, the costs people underestimate, and the single-agent alternatives to try first.

• Name the coordination costs of multi-agent work: merge conflicts, divergent assumptions, review load.
• Identify the task properties that justify splitting: independent zones, parallel deadlines, distinct expertise.
• Exhaust the single-agent alternatives — better harness, longer runs, sequenced sessions — before splitting.

The three patterns that cover most real cases: fan-out for independent parallel work, pipelines for staged work with handoffs, and critic pairs where one agent produces and another reviews against the criteria.

• Match fan-out, pipeline, and critic-pair patterns to task shapes.
• Define boundaries, outputs, and merge rules before any worker starts.
• Run an orchestrator role — human or agent — that owns the brief, the merge log, and the stop conditions.

Single MCP connections are useful; chains are where orchestration earns its keep — research feeding a canvas, the canvas feeding code, the browser feeding screenshots back into review — with attention to credentials, scopes, and what happens when one link fails.

• Design a tool chain where each MCP connection has a defined role and scope.
• Handle credentials, permissions, and data boundaries across chained tools.
• Build chains that fail loudly and partially rather than silently and completely.

Multiple agents working a shared canvas or design file at the same time: zone ownership, naming and layout conventions that prevent collisions, and the review rhythm that keeps a fast-moving canvas coherent.

• Partition canvas work into zones with single ownership per zone.
• Set naming, layer, and component conventions that concurrent agents can follow.
• Run a review rhythm that catches divergence before it spreads across the canvas.

The full pipeline from approved canvas design to production code: stage gates, parity checks, token sync, and the handoffs where intent historically leaked — now run as an orchestrated workflow with evidence at every stage.

• Define the pipeline stages from canvas approval to merged production code.
• Place gates with owners and evidence requirements at each stage.
• Measure where defects enter the pipeline and tighten the responsible stage.

Making the capability durable: cost visibility and budgets, permissions and audit trails, review capacity that scales with output volume, onboarding people into the system, and the governance that keeps speed from outrunning accountability.

• Set up cost tracking and budgets per workflow rather than one opaque bill.
• Define permissions, audit trails, and escalation paths for agent-driven changes.
• Plan review capacity so human attention scales with the volume agents produce.