arXiv:2602.04935v3 Announce Type: replace-cross Abstract: Adapting LLM agents to domain-specific tool calling remains notably brittle under evolving interfaces. Prompt and schema engineering is easy to deploy but often fragile under distribution shift and strict parsers, while continual parameter-efficient fine-tuning improves reliability at the cost of training, maintenance, and potential forgetting. We identify a critical Lazy Agent failure mode where tool necessity is nearly perfectly decodable from mid-layer activations, yet the model remains conservative in entering tool mode, revealing a
The accelerating development of AI agents necessitates robust and adaptable methods for tool integration, especially as interfaces evolve rapidly.
Improving the reliability and adaptability of AI agents in using external tools is critical for their practical deployment and expanded capabilities across various domains.
This research proposes a method for agent representation engineering that is independent of backbone training, potentially making tool-calling more robust and less prone to 'lazy agent' failure modes.
- · AI Agent developers
- · SaaS companies integrating with AI
- · Industries relying on AI automation
- · Companies with brittle tool integration solutions
- · Legacy prompt engineering methods
AI agents become more reliable and adaptable in leveraging external tools for complex tasks.
Increased adoption of AI agents in business processes as their failure rate decreases and robustness improves.
New services and platforms emerge to manage and optimize tool-calling for vastly more capable AI agents, leading to novel forms of automation.
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Read at arXiv cs.AI