Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 24·5 min read
Representation Affects Retrieval: A Case Study of Skill Discovery and Routing in a Multimodal Agent Harness
TL;DR
Tinycloud, a multimodal video agent harness, demonstrates that skill representation significantly impacts task selection. Key findings reveal that full autoloading of skills ensures optimal task execution, while partial exposure can hinder correct skill selection.
✦ Why It Matters
Evaluate your skill exposure strategy to optimize task execution in multimodal agent systems.
Key Takeaways
- Implement full autoloading of skills for optimal task execution.
- Avoid partial skill exposure to reduce lexical competition.
- Test different skill exposure formats for your agent harness.
- Monitor task routing to identify misrouting issues.
How It Works
Tinycloud's skill representation involves two types: tool-skills, which are simple wrappers for APIs, and workflow-skills, which combine multiple tool-skills to achieve a specific outcome. The system prompt displays these skills in two ways: autoloaded skills are fully detailed, while on-demand skills are listed briefly.
This dual representation allows the agent to select the most appropriate skill based on the task context.