Responsible Agentic AI Requires Explicit Provenance

2026-05-19 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Compliance & Risk Management · Depth: Expert, extended

Summary

The proliferation of agentic AI systems, which are autonomous and operate with tools, memory, and multi-agent coordination, has outpaced the development of robust responsibility frameworks, leading to a trust deficit and adoption bottleneck. This paper argues that explicit provenance is essential for making responsibility computable and actionable in agentic AI, moving beyond component-level audits to address emergent harms from system-level compositions. The authors propose a framework based on four axes: identifying responsibility gaps across sociotechnical dimensions, formalizing provenance through a causal attribution function $\kappa(p,\omega,\tau)$ and a responsibility tensor $\mathbf{R}$, detailing how provenance can be made computable across design, engineering, deployment, and experience layers, and demonstrating responsibility assignment in a concrete incident. Preliminary experiments using neuro-symbolic monitors show that online provenance signals are estimable and support intervention before irreversible harm occurs, with AUPRC results across benchmarks like WebArena and TAU2Bench exceeding random and zero-shot LLM baselines.

Key takeaway

For CTOs and VPs of Engineering deploying agentic AI, prioritizing explicit provenance is not optional; it is a structural necessity for building trust and enabling responsible scaling. You should integrate provenance infrastructure across the agentic lifecycle, from design-time causal modeling to real-time monitoring and post-deployment accountability. This will allow your teams to quantify causal contributions, trace harmful trajectories, and intervene before irreversible consequences accumulate, thereby closing critical responsibility gaps and accelerating adoption.

Key insights

Explicit provenance is crucial for assigning computable and actionable responsibility in complex agentic AI systems.

Principles

• Responsibility requires quantifiable causal attribution.
• Traceability grounds responsibility in verified execution records.
• Interventionability demands continuous provenance for real-time interception.

Method

A neuro-symbolic monitoring architecture converts raw agent traces into failure-aligned event abstractions, scored by monitors to estimate trajectory risk online, enabling early intervention and causal attribution.

In practice

• Design systems with explicit causal dependency graphs.
• Implement real-time trajectory monitoring for risk estimation.
• Document responsibility envelopes and intervention boundaries.

Topics

• Agentic AI Responsibility
• Explicit Provenance
• Causal Attribution Function
• Responsibility Tensor
• Neuro-Symbolic Monitoring

Code references

• openclaw/clawhub

Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, AI Architect, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.