Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
Data Science
Towards Data Science·Jul 31·5 min read
The 3× Token Bill We Didn’t See Coming
TL;DR
A shift to a multi-agent architecture unexpectedly tripled the costs associated with large language models (LLMs). The issue was resolved through strategic adjustments in the architecture.
✦ Why It Matters
Evaluate your architecture choices to avoid unexpected cost increases in your AI projects.
Key Takeaways
- Assess the cost implications of orchestration in multi-agent systems.
- Implement task complexity-based routing for agent model selection.
- Trim unnecessary context during agent handoffs to reduce token costs.
- Enable parallel execution of independent tasks to minimize latency.
How It Works
The architecture uses a supervisor agent to manage multiple sub-agents, each responsible for specific tasks. By routing tasks based on their complexity, the system assigns appropriate models, reducing costs.
Context trimming ensures that agents only receive necessary information, while parallel execution allows independent tasks to run simultaneously, minimizing the impact of failures.