# Summary: 2026-07-20_22-28-16Z_EngineeringTrustworthyAgenticAIforCriticalSystems.md
Saved: 2026-07-24 00:27
Source: 2026-07-20_22-28-16Z_EngineeringTrustworthyAgenticAIforCriticalSystems.md
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## Summary  
The paper proposes a trustworthiness framework for agentic artificial intelligence in critical engineering systems, treating trust as an engineering property rather than merely a task‑capability metric. It maps five cross‑cutting dimensions—safety, robustness, transparency, accountability, and privacy/security—to an end‑to‑end assurance workflow that spans perception through audit. The authors survey existing architectures, threat models, trust mechanisms, and quantitative metrics, then apply the model to four domains: power systems, autonomous vehicles/UAVs, high‑performance computing, and communication networks. By synthesizing these analyses they outline a reusable cross‑domain assurance framework analogous to graded certification regimes used in mature safety‑critical engineering.

## Key Contributions  
- A comprehensive trustworthiness model with five dimensions (safety, robustness, transparency, accountability, privacy/security) mapped onto an end‑to‑end assurance workflow.  
- Systematic identification of recurring design patterns, shared failure modes, and domain‑specific gaps across power systems, autonomous vehicles/UAVs, high‑performance computing, and communication networks.  
- Proposal of a reusable cross‑domain assurance framework that mirrors mature certification regimes for critical engineering.

## Methodology  
The authors conducted a literature survey combined with expert interviews to construct the trust model and its associated assurance workflow. They compiled a taxonomy of agentic system architectures, threat models, trust mechanisms, and quantitative metrics, then performed comparative case studies in four constraint‑bound domains to reveal patterns and gaps. This mixed‑methods approach allowed both qualitative insight into design practices and quantitative evaluation of trustworthiness.

## Results  
The framework identified the five trust dimensions and their corresponding technical requirements, showing that safety and constraint satisfaction dominate power systems, while privacy is paramount for communication networks. Cross‑domain analysis highlighted shared failure modes such as sensor drift and model drift, and domain‑specific gaps like real‑time certification for autonomous vehicles. Quantitative metrics—e.g., an auditability score derived from traceability of perception, planning, tool use, and action steps—were introduced to evaluate trustworthiness objectively.

## Significance  
By treating trustworthiness as a measurable engineering property with an assurance workflow, the work bridges AI capability and safety‑critical reliability. The reusable framework enables systematic design, verification, and certification of agentic systems in high‑stakes applications where failure is unacceptable, aligning with existing graded certification regimes and fostering stakeholder confidence.

## Related Concepts  
- Trustworthiness  
- Assurance  
- Verification  
- Auditability  
- Safety‑critical engineering  
- Graded certification  
- Perception  
- Planning  
- Tool use  
- Multi‑step action  
- Cross‑domain analysis  
- Design patterns  
- Failure modes  
- Privacy  
- Security