All terms
Agents & tools
AI team topology
Also known as: multi-model workflow, AI team, model role separation
The pattern of using multiple AI models for different roles in a single workflow — typically architect, builder, reviewer — instead of one model doing everything.
What it means
AI team topology is the 2026 default for serious AI-assisted work: instead of relying on a single model for every step, you use different models for different jobs. The most common shape is three roles — an architect that plans, a builder that implements, and a reviewer that critiques — each played by a model picked for that role's strengths.
The architect role rewards careful reasoning over speed. The builder role rewards speed and tool integration. The reviewer role rewards skepticism and a fresh perspective. No single model is best at all three, which is why teams have started splitting them.
This is closer to how human engineering teams work than the older "ChatGPT for everything" pattern. The cost is more orchestration complexity. The benefit is fewer hallucinations slipping through and better-quality output, because each role gets a model suited to it. Common examples in 2026: a planning model writes a spec, a coding agent implements it, and a different model reviews the diff with no priors about how it was built.
Example
A founder uses one model to draft an investor update, a second model to fact-check the numbers, and a third model to critique the framing. Each output goes through the next model with explicit instructions about its role.
Why it matters
Single-model workflows have a blind-spot problem: the same model that wrote something is biased toward thinking it's good. Bringing in a second model breaks that loop. As 2026 progresses, knowing which model to use for which role is becoming as important as knowing which model is "best" overall.