Team Design
Agent specialization, roles, personalities, and team composition. Team Design addresses how to structure AI agents for maximum effectiveness: moving beyond generalist chatbots to specialized collaborators with distinct capabilities and domains.
5 methods| ID | Method | Description |
|---|---|---|
| FG-0301 | Agent Specialization 3 | Design AI agents with distinct roles, domains, and capabilities rather than using generalist agents for all tasks. A single generalist agent produces generalist results: competent but unfocused. Specialized agents with defined domains produce deeper, more consistent output because their entire context window is dedicated to their specialty. This is the multi-agent design pattern, and at the system level it echoes mixture-of-experts (and, more directly, multi-agent routing): route each task to the specialist best suited to it. Specialization also reduces contradictory requirements: a testing agent does not need to balance quality advocacy with shipping pressure. |
| FG-0302 | Voice Differentiation 3 | Give each specialized agent a distinct communication style that reflects their domain and personality. Voice Differentiation is not cosmetic. It serves three operational purposes: it makes multi-agent output instantly attributable (you can tell who wrote what), it creates natural specialization boundaries (agents stay in character and therefore stay in domain), and it makes the collaboration more engaging for human participants. |
| FG-0303 | Calibrated Expressiveness | Adjust agent communication intensity based on their role and context. Not every agent should be equally dramatic or equally terse. Calibrated Expressiveness assigns each agent a communication intensity level that matches their function: creative agents can be expressive; testing agents should be direct; analytical agents should be precise. This prevents output fatigue and ensures that communication style serves the content. |
| FG-0304 | Team Synergy Mechanics | Design collaboration patterns that produce better results when multiple agents work together than when they work alone. Team Synergy Mechanics creates intentional interaction patterns between specialists: a designer and architect collaborating on implementation produces better results than either working solo. Define which agent combinations produce synergies, how handoffs work between them, and how to structure multi-agent work sessions. |
| FG-0305 | Multi-Contributor Workflow | Establish clear protocols for how multiple humans and AI agents collaborate on shared work products. Multi-Contributor Workflow addresses the coordination challenges that emerge when more than one human and more than one AI agent are contributing to the same project. It defines attribution, review chains, conflict resolution, and how to maintain coherence when many contributors are active simultaneously. |