Announcing MCP Toolbox Java SDK v1.0: Agentic data access for the enterprise
cloud.google.com·6d ago
Cloud & Infrastructure
The New Stack·21d ago·5 min read
Your AI agent is burning tokens on choices that don’t need words
TL;DR
A new family of decision models called Kev, based on Qwen 3.5, eliminates unnecessary text generation in AI agents, significantly reducing token usage. This approach allows for faster decision-making without the overhead of generating and parsing text responses.
✦ Why It Matters
Consider adopting Kev models to reduce token costs in your AI applications today.
Key Takeaways
- Implement Kev to reduce unnecessary text generation in AI agents.
- Utilize the prefill-only model for faster decision-making.
- Evaluate multiple decisions in a single forward pass for efficiency.
- Access and modify Kev's open-source code for tailored applications.
How It Works
Kev processes state and questions in one forward pass, using a pointer head to return probabilities for given candidates without generating text. This method allows for efficient decision-making by evaluating only the options provided, thus avoiding irrelevant choices.
⚠ The Catch
While Kev can score candidates effectively, its probability outputs may not always reflect true confidence, leading to potential misjudgments in decision-making.