Researchers at Hugging Face have introduced ProvenanceGuard, a verification system addressing a critical gap in AI agent reliability: the ability to catch when agents cite facts from the wrong sources. While traditional fact-checking systems verify whether claims are supported by available evidence, they don't track which specific tool output supports each claim or ensure proper attribution. In healthcare applications, this becomes particularly dangerous—a clinical detail from a patient's record could be presented as a finding from medical literature, technically true but dangerously misattributed. ProvenanceGuard operates as a verification layer on top of Model Context Protocol (MCP) agents, preserving source identity throughout the checking process rather than pooling all evidence together. The system breaks answers into specific claims, identifies the most relevant source for each, verifies the source actually supports it, and checks whether the stated attribution matches reality. In tests on 281 real traces from medical agents, ProvenanceGuard caught 138 of 139 problematic claims that human experts identified, demonstrating effectiveness in data-sensitive applications where incorrect attribution carries consequences as serious as factual errors. The verification system uses local models for processing, though the architecture can be adapted to cloud-based alternatives with proper recalibration. Blocked answers can be sent through repair processes and re-verified, with the system carefully validating specific values like numbers and dates. The research highlights the growing sophistication required for fact-checking in multi-tool agent systems, where simple evidence pooling no longer suffices.