Date: 2026-03-15 Author: Ravi Natarajan

Motivation

Modern AI systems often rely on multiple models with different capabilities, costs, and performance characteristics.

Selecting the appropriate model dynamically becomes an important architectural concern.

K9-AIF introduces the concept of a Model Router that routes requests to the most appropriate inference provider.

Architectural Context

Within the K9-AIF architecture the router sits above the inference layer and coordinates model selection.

Router → Orchestrator → Squads → Agents

The router enables policy-driven model selection while keeping agent implementations independent of specific model providers.

Design Goals

The K9 Model Router aims to support:

  • provider independence
  • cost-aware model selection
  • performance-aware routing
  • extensible routing policies
  • integration with enterprise governance controls

Default Router

K9-AIF provides a simple default router capable of routing requests based on configuration rules.

Advanced Routing

More advanced routers may implement strategies such as:

  • quality evaluation
  • model benchmarking
  • cost-performance trade-offs
  • dynamic routing policies

These advanced routers can be implemented as Solution Building Blocks (SBBs).

Next Steps

Future posts will demonstrate:

  • a default K9 router implementation
  • an example NotDiamond-style router SBB