Automating responsible AI principles with agentic AI in digital triplets

How digital triplet agents ensure responsible AI deployment

Over the past three years, we have seen how the previously niche technologies of artificial intelligence (AI) and machine learning (ML) revolutionized various sectors with the rapidly evolving capabilities and more accessibility of GenAI and agentic solutions. Now, with greater access to these technologies, we are beginning to experience the unprecedented advancements and efficiencies that these technologies bring. As they continue to evolve rapidly, and adoption increases, we need to be mindful that with greater capabilities come a greater responsibility.

Ensuring the responsible use of AI is paramount to prevent errors, security or privacy weaknesses or misuse, and unintended risks and consequences – and ultimately to achieve value from AI investments. Agentic AI in digital triplets provides a promising solution to autonomously enforce these principles, promoting automation and consistency of ethical AI deployment across industries.

Understanding digital triplets and agentic AI

Digital triplets evolved with the introduction of generative AI to bring information and data closer to businesses and decision-makers. The concept of digital triplets takes digital modeling further by adding AI-driven real-time interaction between three connected parts: the physical world, the virtual model and potential future outcomes.

Digital triplets embody a form of agentic AI due to the ability to trigger multi-purpose and context-based AI agents to act autonomously in completing a task based on a user’s request or prompt. Think of making a request to an expert team of assistants where data collection, analysis and option analyses are needed to support a complex decision.

Agentic AI allows a user to trigger an orchestrated set of actions that fulfill the user’s need for information and insights. These actions are completed by agents that decide on data sources, assess information quality and patterns from disparate data and sources, interact with their environment and multiple data stores in real-time, and engage users through text, speech, images and charts.

Bringing together a collaboration of “responsible use” agents

Imagine a generative AI (GenAI) solution designed to provide personalized health recommendations based on user data. In this scenario, multiple agentic AI agents work together to enhance the system’s reliability, security and privacy while fulfilling user requests, authentication and information access requirements. The responsible use agents work in parallel with the information retrieval, calculation and generation agents to help ensure the query response meets compliance and responsible use guidance—giving more assurance to the end user that the response from the AI system is trustworthy and the information is secured.

In this scenario, the agents initiated may include:

• Orchestration agent: coordinates and manages the interaction between the human request and different AI agents, ensuring smooth and efficient operation. This agent handles the scheduling and execution of tasks, optimizing resource usage, such as compute power, and minimizing conflicts among agents.
• Retriever agent: efficiently searches and retrieves relevant data from multiple sources. This agent uses advanced search algorithms to gather precise and pertinent information, ensuring that the GenAI solution has access to the most relevant data from the most appropriate source.
• Data prep agent: processes complex numerical data and performs sophisticated calculations or analyses as needed. This agent ensures that all quantitative analyses are accurate and timely, supporting the GenAI solution's decision-making processes.
• Quality agents: continuously monitors the data inputs and outputs ensuring that the data used by the GenAI solution is accurate and updated. This agent employs anomaly detection algorithms to identify and flag any inconsistencies, errors, omissions or redundancy in the data, automatically correcting minor issues or escalating them to human overseers for resolution. On its own, this agent helps improve data quality at the source and in the data pipelines.
• Bias detection agent: ensures that the recommendations by the GenAI solution are fair and unbiased. This agent continuously analyzes the decisions made by AI, identifying and addressing any biases that may arise. It employs fairness algorithms ensuring that the system remains impartial and transparent, and that the information sources are relevant and representative of the population the request is addressing.
• Privacy compliance agent: enforces privacy-preserving techniques such as differential privacy and federated learning. For example, this agent ensures that sensitive user data is anonymized or redacted before it is used for training the GenAI models. It autonomously manages, monitors and tracks data access and usage, ensuring that all operations comply with localized privacy regulations and user consent.
• Security monitoring agent: safeguards the system against potential threats and reinforces compliance requirements such as solution and data sovereignty. This agent employs advanced cybersecurity techniques to detect and mitigate variances in information flows as well as detect potential threats in real-time. In addition to ensuring any responses support data access and authentication rules for users, it continuously scans for vulnerabilities, applies security patches and monitors for any unauthorized access attempts, ensuring the GenAI solution remains secure against both external and internal threats.
• Generation agent: creates coherent and contextually appropriate text and images based on user queries. This agent uses advanced generative models to produce high quality content, enhancing the overall user experience.

In this example, agentic AI agents collaborate to create a transparent robust, secure and privacy-compliant GenAI solution, demonstrating the power of digital triplets in automating and integrating responsible AI principles into complex human-initiated action.

Lessons learned for implementing agentic AI in digital triplets

We also can leverage lessons learned from early adopters to help ensure successful adoption and value creation from the digital triplet agent ecosystem:

Benefits of agentic AI in digital triplets

The following benefits are being achieved through the application of agentic solutions within digital triplet models:

Enhanced efficiency and effectiveness
By autonomously enforcing responsible AI principles, agentic AI in digital triplets enhances the efficiency and effectiveness of AI investments. These systems can quickly identify, report and address issues, reducing the need for manual intervention and streamlining operations.

Improved trust and adoption of innovation
Automating the responsible use of AI principles and removing the need to manually validate AI output builds trust and acceptance among stakeholders. Agentic AI in digital triplets promotes transparency, fairness and accountability, fostering a positive perception of AI systems and encouraging wider adoption.

Scalability and adaptability
Digital triplets offer scalability and adaptability, allowing organizations to deploy AI systems across various domains and environments. By leveraging the predictive and prescriptive capabilities of digital triplets, organizations can proactively address challenges and optimize performance—starting small, getting familiar and then scaling solution for increased value.

Preparing today for the future of agentic AI in digital triplets
Agentic AI in digital triplets represents a significant advancement in the autonomous enforcement of responsible AI principles. By integrating predictive and prescriptive AI models with physical systems and digital twins, these systems can proactively address ethical, legal and operational challenges. While there are complexities and considerations, the benefits of enhanced efficiency, improved trust and scalability make agentic AI in digital triplets a promising solution for responsible AI deployment.

As organizations continue to explore and implement AI technologies, they must prioritize ethical considerations, invest in AI governance and establish robust frameworks to ensure the responsible and sustainable use of AI. Learn more about CGI’s AI capabilities, success stories and responsible AI framework. Also, reach out to me to engage in a conversation about how we can help your organization achieve trusted AI outcomes.