arXiv:2606.04645v1 Announce Type: new Abstract: Language models acting as agents over knowledge graphs generate Cypher queries that fail structurally (crashing at the database) or semantically (executing but returning wrong results). We place a pre-execution gate between query generation and a production Neo4j database. The gate validates structure through a four-backend chain culminating in execution against a mirror graph at 5.6 ms median latency. Structurally broken queries are routed to a corrector that iterates structured error feedback through a language model. On seven CypherBench schem
The proliferation of language models acting as agents interacting with complex data structures like knowledge graphs necessitates robust pre-execution validation to ensure reliability and prevent system failures in live environments.
This development addresses critical challenges in deploying AI agents by improving the reliability and efficiency of their interactions with production databases, which is crucial for scalable agentic applications.
The introduction of pre-execution gating and automated correction mechanisms for AI-generated queries drastically reduces query failure rates and improves system stability for agent-driven operations on knowledge graphs.
- · AI Agent developers
- · Knowledge Graph database providers
- · Enterprise AI deployments
- · SaaS platforms leveraging AI agents
- · Systems unprepared for AI-driven query error handling
Increased trust and adoption of AI agents for complex data manipulation tasks in real-world settings.
Reduced operational overhead and development cycles for integrating AI with structured databases through more robust error handling.
Acceleration of 'AI agents' narrative as a viable and reliable technology, leading to new categories of automated B2B services.
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