Getting the Source Right, Not Just the Fact: Source-Aware Verification for MCP Agents
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huggingface.co
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Context
A fact can appear in an AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition →'s evidence yet be attributed to the wrong record. Multiverse Computing's account of ProvenanceGuard describes checking that connection after an agent answers. The system preserves tool-output identities, splits an answer into claims, checks each against a relevant source and compares that source with the answer's attribution. In the team's held-out evalA repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.Full definition → of 361 claims from 40 agent answers, it caught 138 of 139 claims that experts said should not pass, while also blocking or sending for review 67 supported claims. The reported configuration therefore exchanges extra review of valid claims for fewer unsupported claims passing through.
Terms in this piece · Glossary
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
MCP — The Model Context Protocol — an open standard that lets any AI assistant plug into any tool or data source without custom integration code.
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