We are experimenting with a different way of building multi-model AI systems.
Traditional agent architectures usually assign:
one model for planning
one model for execution
one model for validation
But we wanted to test a different idea.
Instead of role-based distribution...
What if we don’t assign models to entire phases at all?
Instead, we distribute cognitive responsibilities within each phase across multiple models.
For example, instead of “one model does planning,” we break planning into specialized contributions:
Model A → builds the core plan structure Model B → probes edge cases and missing branches Model C → validates logical coherence of the plan
Then during execution, the roles shift but the collaboration pattern stays:
Model A → checks system-level consistency during implementation Model B → writes the core implementation Model C → verifies correctness and catches errors
This way, models are no longer locked into static roles like planner or executor. Instead, each model continuously contributes based on its strength within every phase, creating a more flexible and overlapping intelligence system.
Routing Layer Architecture
Traditional Multi-Agent Architecture
One model.
One identity.
Most orchestration systems permanently assign a role to each model. Planning belongs to one brain. Execution belongs to another. Validation belongs somewhere else.
Model 1
Assigned Role
Planning
Model 2
Assigned Role
Execution
Model 3
Assigned Role
Validation
Ensemble Cognitive Routing
Distribute cognition.
Not roles.
Instead of assigning entire identities to models, we route individual cognitive operations dynamically across the ensemble.
Why this is interesting
Traditional multi-agent systems create rigid boundaries:
- planner
- executor
- validator
But cognition in humans is not isolated that way.
Reasoning overlaps.
Validation influences planning.
Execution influences reasoning.
cognition is collaborative instead of role-isolated