Artificial intelligence (AI) is rapidly transforming the business landscape, and the conversation is quickly shifting from generative AI to agentic AI. While generative AI tools like ChatGPT and DALL-E have captured the public’s imagination with their ability to create text, images, and other content, agentic AI represents a significant step further – a move towards AI systems that can not only generate outputs but also act autonomously to achieve goals.

This shift presents both unprecedented opportunities and significant risks for organizations, demanding a new approach to AI governance and risk management. This critical topic will be the focus of a key session at the upcoming #RISK Digital North America event on April 24th, titled ”Agentic AI: Navigating the Risks and Rewards of Autonomous Systems” (13:10 - 13:50  EST / 10:10 AM - 10:50 AM PST).

What is Agentic AI?

The key difference between generative AI and agentic AI lies in their level of autonomy and agency.

Generative AI: These systems are designed to generate new content based on the data they’ve been trained on. They respond to prompts and create outputs, but they don’t have goals beyond completing the specific task they’re given. Think of them as sophisticated tools that require human direction. Examples include:

  • Large Language Models (LLMs) like ChatGPT, Bard, and Claude.
  • Image generators like DALL-E, Midjourney, and Stable Diffusion.
  • Code generators like GitHub Copilot.

Agentic AI: These systems, often built upon generative AI models, are designed to act in the world to achieve specific goals. They can:

  • Plan and execute multi-step tasks: They can break down complex goals into smaller steps and execute them autonomously.
  • Interact with their environment: They can interact with software, APIs, databases, and even the physical world (through robotics).
  • Adapt to changing circumstances: They can adjust their plans and actions based on new information or feedback.
  • Learn and improve over time: They can learn from their experiences and improve their performance.
  • Make decisions: Based on the data they have, they can make calls without the input of a human.

Example: The Automated Customer Service Agent

Imagine a customer service chatbot powered by generative AI. It can answer simple questions, provide information, and even generate personalized responses. However, it’s fundamentally reactive; it responds to specific prompts from the user.

Now, imagine an agentic AI system designed to manage customer service. This system wouldn’t just answer questions; it would:

  1. Proactively monitor customer interactions across multiple channels (email, chat, social media).
  2. Identify customers who are likely to be dissatisfied (based on sentiment analysis, purchase history, etc.).
  3. Develop a plan to address the customer’s needs (e.g., offer a discount, schedule a call with a human agent, proactively send a helpful resource).
  4. Execute that plan autonomously, interacting with various systems (CRM, email, ticketing system) to achieve the goal of resolving the customer’s issue and preventing churn.
  5. Learn from the outcome and adjust its strategies for future interactions.

This agentic system is not just responding to prompts; it’s acting independently to achieve a defined objective (customer satisfaction/retention).

The Rewards of Agentic AI:

The potential benefits of agentic AI are enormous:

  • Increased Efficiency: Automating complex, multi-step tasks can free up human employees to focus on higher-value work.
  • Improved Decision-Making: AI agents can analyze vast amounts of data and make decisions faster and more accurately than humans in many situations.
  • Enhanced Productivity: Automating workflows and optimizing processes can lead to significant productivity gains.
  • New Product and Service Development: Agentic AI can enable the creation of entirely new products and services that were previously impossible.
  • Personalized Experiences: AI agents can tailor interactions and recommendations to individual users, improving customer satisfaction.

The Risks of Agentic AI:

However, the increased autonomy and agency of these systems also introduce significant risks:

  • Unpredictable Behavior: Complex AI systems can behave in unexpected ways, particularly in situations they haven’t been explicitly trained for.
  • Lack of Control: It can be challenging to control and monitor the actions of autonomous agents, especially as they become more sophisticated.
  • Ethical Dilemmas: Agentic AI raises ethical questions about accountability, responsibility, and the potential for unintended consequences. Who is responsible when an autonomous agent makes a mistake?
  • Security Vulnerabilities: AI agents can be targets for cyberattacks, and vulnerabilities in their design or implementation could have serious consequences.
  • Job Displacement: The automation capabilities of agentic AI could lead to significant job displacement in certain sectors.
  • Bias and Discrimination: If AI agents are trained on biased data, they can perpetuate or amplify existing inequalities.
  • Data Privacy The more power and autonomy an AI has, the more data it will need.

Strategies for Governing and Controlling Agentic AI:

Managing the risks of agentic AI requires a proactive and comprehensive approach to governance and control:

  • Robust Testing and Validation: Thoroughly test AI systems before deployment, and continuously monitor their performance to identify and address any issues.
  • Human Oversight: Implement “human-in-the-loop” systems, where humans review and approve critical decisions made by AI agents.
  • Explainable AI (XAI): Develop AI systems that are transparent and explainable, so that humans can understand why they are making certain decisions.
  • Ethical Guidelines: Establish clear ethical guidelines for the development and deployment of AI systems.
  • Data Governance: Implement strong data governance policies to ensure the quality, security, and privacy of the data used to train and operate AI agents.
  • Cybersecurity: Protect AI systems from cyberattacks and data breaches.
  • Regulatory Compliance: Stay informed about evolving regulations related to AI and ensure compliance.
  • Continuous Monitoring: Implement systems for ongoing monitoring of AI agent behavior and performance.

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#RISK Digital North America: Expert Guidance

The session ”Agentic AI: Navigating the Risks and Rewards of Autonomous Systems” at #RISK Digital North America on April 24th (1:10 PM - 1:50 PM EST / 10:10 AM - 10:50 AM PST) will provide a deeper exploration of these critical issues.

Featuring:

  • Speaker: Stan Yakoff, RegTech Adviser & Law Professor, Fordham Law School.

Attendees will gain valuable insights into:

  • The current state of agentic AI technology and its applications.
  • The specific risks associated with autonomous AI systems.
  • Practical strategies for governing and controlling agentic AI.
  • The legal and ethical considerations of deploying these technologies.
  • Real-world examples and case studies.

Agentic AI is rapidly changing the world, and organizations must be prepared to manage the associated risks. This session at #RISK Digital North America is an essential opportunity to gain the knowledge and tools you need to navigate this new frontier responsibly and successfully.

Register for #RISK Digital North America today and secure your place at this critical session.

Secure your place today!

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