When the Database Fails: Prompting LLM Dialogue Agents for Safe Recovery in Task-Oriented Dialogue
arXiv:2606.31307v1 Announce Type: new Abstract: Large language models used in task-oriented dialogue often produce fluent but unsafe responses when backend database calls fail, return empty results, or surface mismatched information, inventing venues, confirmations, or booking details not grounded in the database. We study a lightweight prompting-based recovery approach that improves robustness without retraining or additional model calls. We compare three response strategies, including a guided recovery prompt conditioned on structured database status, across six open-weight model families (D
This research addresses a critical and immediate challenge as large language models are increasingly deployed in real-world task-oriented dialogue systems, making robustness to backend failures paramount.
Improved robustness in LLM-driven task-oriented dialogue will reduce reputational risk and increase user trust, accelerating the adoption and capability of AI agents in commercial applications.
The ability of LLMs to safely recover from backend database errors through lightweight prompting, rather than extensive retraining, significantly enhances their deployability and reliability.
- · AI software developers
- · Customer service automation
- · SaaS platforms
- · Enterprise AI adopters
- · Companies with brittle AI deployments
- · Traditional customer service roles (over long term)
Task-oriented AI agents become more reliable and less prone to 'hallucinations' when backend systems are unstable.
Increased user confidence in AI systems leads to faster integration of AI into complex, customer-facing operations.
The reduced need for extensive retraining could democratize advanced AI agent development, lowering entry barriers for new AI applications.
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Read at arXiv cs.CL