AUTOMATION
Agentic Workflow Distillation for Cost Reduction
Distill complex multi-step agentic workflows into model weights for 100x inference cost reduction
Updated: 5/23/2026
Difficulty
hard
Time
120m
Use Case
Cost-efficient agent deployment at scale
Popularity
0 views
About this automation
Research-backed technique to distill full agentic workflows (multi-step LLM calls, tool invocations, scratchpads, decisions) into model weights, achieving 100x lower inference cost while preserving near-frontier task quality.
How to implement
1
Document agentic workflow steps
2
Collect training data from workflow execution
3
Fine-tune model on workflow patterns
4
Validate task quality preservation
5
Measure inference cost reduction
6
Deploy distilled model