# PROV-AGENT: Unified Provenance for Tracking AI Agent Interactions in Agentic Workflows

arXiv: https://arxiv.org/abs/2508.02866 · HTML: https://mlsanity.com/paper/2508.02866/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2025.

## Built on by (most cited first, 30 of 31)

- [LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions](https://mlsanity.com/paper/2509.18970/) (2025)
- [From Agent Traces to Trust: A Survey of Evidence Tracing and Execution Provenance in LLM Agents](https://mlsanity.com/paper/2606.04990/) (2026)
- [A Survey on Long-Term Memory Security in LLM Agents: Attacks, Defenses, and Governance Across the Memory Lifecycle](https://mlsanity.com/paper/2604.16548/) (2026)
- [ARGUS: Defending LLM Agents Against Context-Aware Prompt Injection](https://mlsanity.com/paper/2605.03378/) (2026)
- [LLM Agents for Interactive Workflow Provenance: Reference Architecture and Evaluation Methodology](https://mlsanity.com/paper/2509.13978/) (2025)
- [Agent libOS: A Runtime Substrate for Capability-Controlled Self-Evolving LLM Agents](https://mlsanity.com/paper/2606.03895/) (2026)
- [Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap](https://mlsanity.com/paper/2608.05179/) (2026)
- [TDFlow: Agentic Workflows for Test Driven Development](https://mlsanity.com/paper/2510.23761/) (2025)
- [Towards Security-Auditable LLM Agents: A Unified Graph Representation](https://mlsanity.com/paper/2605.06812/) (2026)
- [LEDGER: Claim-to-Evidence Trace Graphs for Auditing LLM Agents](https://mlsanity.com/paper/2608.18398/) (2026)
- [TessPay: Verify-then-Pay Infrastructure for Trusted Agentic Commerce](https://mlsanity.com/paper/2602.00213/) (2026)
- [AI-NativeBench: An Open-Source White-Box Agentic Benchmark Suite for AI-Native Systems](https://mlsanity.com/paper/2601.09393/) (2026)
- [Fantastic Scientific Agents and How to Build Them: AgentBuild for Rietveld Refinement](https://mlsanity.com/paper/2606.12834/) (2026)
- [Beyond Memory Majority: Latent-Source Reasoning for Multi-Agent Memory Arbitration](https://mlsanity.com/paper/2608.19701/) (2026)
- [AgentTrails: Towards Trust and Reuse for Agentic Tasks](https://mlsanity.com/paper/2607.18816/) (2026)
- [Fine-Grained Traceability for Transparent ML Pipelines](https://mlsanity.com/paper/2601.14971/) (2026)
- [Designing Agent-Ready Websites for AI Web Agents: A Framework for Machine Readability, Actionability, and Decision Reliability](https://mlsanity.com/paper/2607.12056/) (2026)
- [F(AI)2R: Who Did What, and Who Checked? Verifiable AI Provenance as an Executable Skill](https://mlsanity.com/paper/2607.25637/) (2026)
- [Causal-Temporal Event Graphs: A Formal Model for Recursive Agent Execution Traces](https://mlsanity.com/paper/2604.17557/) (2026)
- [KYA: A Framework-Agnostic Trust Layer for Autonomous Systems with Verifiable Provenance and Hierarchical Policy Composition](https://mlsanity.com/paper/2605.25376/) (2026)
- [Clarus: Coordinating Autonomous Research Agents toward Web-Scale Scientific Collaboration](https://mlsanity.com/paper/2606.30246/) (2026)
- [Workflow as Knowledge: Semantic Persistence for LLM-Mediated Workflows](https://mlsanity.com/paper/2607.08740/) (2026)
- [Toward Trustworthy Autonomous Science: A Two-Year Community Roadmap](https://mlsanity.com/paper/2607.12113/) (2026)
- [Operational Identity: A Finite Audit of Declared and Implemented Rules of Sameness](https://mlsanity.com/paper/2607.20729/) (2026)
- [Grading the Narrators: An Isnad-Rijal Framework for Claim-Level Provenance in Multi-Agent Knowledge Systems](https://mlsanity.com/paper/2607.24117/) (2026)
- [Workflow Cards: Structured Summaries of Workflow Executions Using Provenance Data](https://mlsanity.com/paper/2608.11022/) (2026)
- [TessIndex: Capability Verified Identity System for the Agent Economy](https://mlsanity.com/paper/2608.21942/) (2026)
- [Stochastic Semantic Evidence Graphs: Uncertainty Propagation and Governance for Agentic AI](https://mlsanity.com/paper/2609.29703/) (2026)
- [Towards Mitigating Fabricated Consensus: The Active Provenance Gate for Multi-Agent Debate Synthesis](https://mlsanity.com/paper/2609.31422/) (2026)
- [K-Dense BYOK: An Open-Source AI Research Assistant That Runs Locally and Keeps a Hash-Chained Lab Notebook](https://mlsanity.com/paper/2610.00074/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) and other foundation models are increasingly used as the core of AI agents. In agentic workflows, these agents plan tasks, interact with humans and peers, and influence scientific outcomes across federated and heterogeneous environments. However, agents can hallucinate or reason incorrectly, propagating errors when one agent's output becomes another's input. Thus, assuring that agents' actions are transparent, traceable, reproducible, and reliable is critical to assess hallucination risks and mitigate their workflow impacts. While provenance techniques have long supported these principles, existing methods fail to capture and relate agent-centric metadata such as prompts, responses, and decisions with the broader workflow context and downstream outcomes. In this paper, we introduce PROV-AGENT, a provenance model that extends W3C PROV and leverages the Model Context Protocol (MCP) and data observability to integrate agent interactions into end-to-end workflow provenance. Our contributions include: (1) a provenance model tailored for agentic workflows, (2) a near real-time, open-source system for capturing agentic provenance, and (3) a cross-facility evaluation spanning edge, cloud, and HPC environments, demonstrating support for critical provenance queries and agent reliability analysis.