Bayesian Uncertainty Propagation for Agentic RAG Pipelines: A Proof-of-Concept Study on Multi-Hop Question Answering
arXiv:2607.00972v1 Announce Type: new Abstract: Trustworthy deployment of Agentic Retrieval-Augmented Generation (RAG) systems requires mechanisms for estimating when multi-stage reasoning pipelines may fail. This paper presents an uncertainty-aware Agentic Retrieval-Augmented Generation (RAG) framework in which planner, evaluator and generator stages produce uncertainty signals derived from semantic divergence and generator self-evaluation. These signals are propagated through a Bayesian Network (BN) to estimate system-level uncertainty and provide node-level indicators of potential failure p
The increasing complexity and deployment of agentic AI systems necessitate robust methods for identifying and mitigating potential failure points, particularly in high-stakes applications.
Ensuring the trustworthiness and reliability of AI agents is crucial for their widespread adoption and for preventing systemic risks inherent in autonomous decision-making pipelines.
The ability to propagate and estimate uncertainty within agentic RAG pipelines provides a critical mechanism for enhancing their transparency, debuggability, and overall safety.
- · AI developers
- · Enterprises deploying AI
- · AI safety researchers
- · AI-driven industries
- · Black box AI systems
- · Organizations with opaque AI deployments
Improved reliability and explainability of agentic AI systems for complex tasks like multi-hop question answering.
Accelerated adoption of AI agents in sensitive domains requiring high assurance and auditable decision-making.
Potential for new regulatory frameworks incorporating uncertainty quantification and propagation as a standard for AI system approval.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI