Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 15·5 min read
Correct Is Not Governed: Provenance Integrity in Agentic Workflows
TL;DR
Matrix is a deterministic causal-state layer designed to enhance the integrity of agentic workflows by ensuring that decisions and outcomes are supported by verifiable provenance. It consistently preserves governing evidence and limits recovery to dependent tasks, making agentic work auditable.
✦ Why It Matters
Implement Matrix to enhance the integrity and auditability of your agentic workflows today.
Key Takeaways
- Adopt Matrix to ensure verifiable provenance in workflows.
- Evaluate authority and completion claims in agentic tasks.
- Limit recovery actions to only affected dependent tasks.
- Be cautious of overly strict completeness contracts in workflows.
How It Works
Matrix operates by recording authority and fact dependencies, which allows it to verify completion evidence and selectively invalidate work that is affected by changes. This structured approach ensures that all decisions made during the workflow are backed by inspectable provenance, enhancing the overall integrity of the process.