If ChatGPT, Smart vacuum cleaners and Alexa are all generally referred to as AI, why can one only answer your questions while the other can smartly clean, and the third one can perform a wide range of tasks such as scheduling meetings or playing a song?
ChatGPT is the AI many use today for generating answers and insights. Smart vacuum cleaners, however, are the AI agents that know how to perform cleaning tasks for you. And Alexa combines many such agents designed to perform different tasks to form a system called agentic AI.
As futuristic as it sounds, Agentic AI makes safety, privacy, and control more complicated. This is due to their advanced autonomy, integration with external systems, and proactive decision-making capabilities. This “two sides of the same coin” nature of agentic AI demands a meticulous strategy to maximize its benefits while mitigating the potential risks.
So, the major concerns that we face with all of the following in no particular order and being as important as the other are:
Agentic AI systems often operate autonomously and may collect and process vast amounts of sensitive data (e.g., behavioral patterns, health records, location data). This raises concerns about:
· Unintentional data exposure
· Unauthorized access or misuse of data
For example, when an AI personal assistant books appointments or sends emails, it gets access to calendars, contacts, and even message content, which raises privacy issues if not properly safeguarded.
Agentic AI’s complex decision-making processes operate as black boxes, meaning that they are opaque and difficult to scrutinize. obscure how decisions are made, making it hard to audit or hold someone responsible.
Unlike simple AIs (e.g., ChatGPT), where users directly control inputs and outputs, agentic AI’s autonomy and multi-agent orchestration create accountability gaps.
For example, if an autonomous AI system in finance makes a risky trade leading to losses, is the developer, the user, or the AI provider accountable?
Agentic AI’s ability to act autonomously means biases can translate into real-world consequences, not just informational outputs.
For instance, if an agentic system prioritizes luxury hotels due to biased training data, it may exclude affordable options, disadvantaging budget-conscious users or a system used in hiring might prioritize resumes from a certain demographic due to biased training data, leading to unequal opportunities.
Agentic systems learn from user interactions, potentially reinforcing biases. For example, if Operator schedules meetings based on past user preferences that favor certain time zones, it may marginalize remote team members in other regions.
To build trustworthy agentic AI systems, developers must embed ethical data practices throughout the development lifecycle. Here are a few methods you can leverage to ensure privacy:
· Incorporate privacy safeguards throughout the AI development lifecycle. Techniques like data anonymization and differential privacy help protect user identities and prevent data misuse [2].
· Enforce strict adherence to global regulations such as the General Data Protection Regulation (GDPR), which mandates ethical data collection, processing, and accountability.
· Adopt models like federated learning, which enable AI to be trained directly on user devices. This reduces reliance on centralized data storage, thereby minimizing the risk of data breaches.
· Empower users with clear, accessible information about how their data is used, and provide easy-to-understand consent mechanisms to ensure they remain in control of their personal data.
To address the critical challenges of transparency and accountability in Agentic AI, Explainable AI (XAI) is playing a pivotal role by making complex systems more understandable and traceable. Key strategies include:
· Build AI systems that offer clear, human-understandable explanations for their decisions, allowing users to follow the reasoning and logic behind outputs.
· Providing tools and training to end-users and decision-makers so they can interpret AI results confidently, encouraging informed interaction and greater trust in the system [2].
In addition to technical strategies, strong governance and accountability mechanisms are essential:
· Implement clear legal and operational guidelines that define the roles and responsibilities of developers, deployers, and operators involved in the AI lifecycle.
· Embed advanced logging and tracking systems to maintain audit trails of AI decisions, ensuring that actions can be reviewed, explained, and justified when needed [7].
Encouraging the development of insurance mechanisms tailored to autonomous systems, which help distribute risk and enhance accountability in case of failures or harm.
To ensure Agentic AI systems are just and socially responsible, the following strategies should be prioritized:
· Continuously review datasets to ensure they are diverse, representative, and free from bias.
· Maintain clear documentation and interpretability to expose and address bias in AI decision-making.
· Integrate fairness metrics during development and deployment to prevent discrimination [2]. Test AI decision-making in diverse simulated settings to refine moral reasoning.
· Implement feedback loops to detect and correct emerging biases in real time.
· Engage technologists, policymakers, social scientists, and end users in building ethically sound AI frameworks.
NIST (National Institute of Standards and Technology – USA) promotes privacy and trust in AI through:
· AI Risk Management Framework (AI RMF) [4]: Encourages the design of trustworthy AI by addressing data privacy, transparency, fairness, and accountability.
· Privacy Framework [5]: Provides guidelines for embedding privacy-by-design into AI systems and aligns with international standards.
· Explainability and Transparency Guidance [6]: Supports the development of explainable AI models and promotes user understanding.
The EU (Europian Union) AI Act [3] is a pioneering legislative framework focused on trustworthy AI:
In June 2024, the EU adopted the world’s first rules on AI.
· Risk-Based Classification: Categorizes AI systems by risk (unacceptable, high, limited, minimal) and imposes stricter requirements for higher-risk systems.
· Mandatory Impact Assessments: High-risk AI must undergo conformity assessments including privacy, transparency, and human oversight checks before released in the market.
· User Rights and Transparency Obligations: Requires disclosure when users interact with AI systems and ensures access to explanation and data usage transparency.
· Alignment with GDPR: Enforces data minimization, consent, and individual rights aligned with the GDPR.
Mayo Clinic is a pioneer in responsibly integrating digital health technologies and artificial intelligence to advance precision medicine and improve patient outcomes. They recognized that in‑house developed AI‑enabled digital health technologies (DHTs) require robust safety, effectiveness, and ethical oversight throughout their lifecycle [7].
Steps Toward Ethical & Agentic AI:
1. Building Internal Skills & Expertise: They invested in training and hiring domain experts—including practicing physicians, regulatory specialists, and AI professionals—to enhance internal capacity.
2. Establishing a Centralized SaMD Review Board: In 2022, Mayo set up a Software as a Medical Device (SaMD) Review Board, an independent multidisciplinary panel. It triages AI projects, classifies regulatory risk, advises on FDA applicability, and recommends controls for safety, clinical standards, ethics, and regulatory compliance.
3. Aligning with Regulations & Best Practices: The Board aligns workflows with the FDA’s Digital Health Policy Navigator, FDA guidance, and international quality standards. It assesses projects dynamically across the lifecycle—from initial intake through deployment.
4. Ensuring Ongoing Oversight & Feedback: Hundreds of internal digital health teams use the Board's guidance. Its outputs inform governance groups, IT, legal, and executive leadership. Mayo continues improving the Board by expanding representation (e.g., nursing, allied health), and refining practices alongside evolving regulations.
Takeaway:
By embedding internal accountability, through expertise development, a centralized AI review board, and regulation-informed workflows, Mayo Clinic advances more ethical, agentic AI. This approach enabled clinicians to steer AI adoption responsibly, enhancing care quality and setting up a governance blueprint for other institutions.
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1. Ethical Implications of Agentic AI: Opportunities and Challenges [2025] - DigitalDefynd
2. The Ethical Dilemmas of Agentic AI - RPATech
3. EU AI Act: first regulation on artificial intelligence | Topics | European Parliament
4. AI Risk Management Framework | NIST
5. Privacy Framework | NIST
6. Artificial Intelligence Risk Management Framework (AI RMF 1.0)
7. The evolving ethics and governance landscape of agentic AI | IBM
8. Embedding Internal Accountability Into Health Care Institutions for Safe, Effective, and Ethical Implementation of Artificial Intelligence Into Medical Practice: A Mayo Clinic Case Study - ScienceDirect