AUTOMATION
Intelligent Model Routing and Token Optimization for Coding Agents
Route requests to optimal model/reasoning depth for 3x token savings
Updated: 6/6/2026
Difficulty
medium
Time
varies
Use Case
Reducing token spend and latency for coding agent workflows while maintaining quality
Popularity
0 views
About this automation
Nerfguard uses a fast classifier to route requests to the least expensive model and reasoning depth required. Combines with token efficiency techniques to achieve 3x usage for same spend. Enables faster agent responses and higher throughput.
How to implement
1
Train classifier on task complexity vs model requirements
2
Implement routing logic for model selection
3
Apply token optimization techniques (caching, prompt engineering)
4
Measure baseline token spend and latency
5
Deploy routing in agent workflow
6
Monitor savings and adjust classifier thresholds