As generative AI grows in popularity, it is evolving into a more complex and advanced form known as agentic AI. Unlike traditional generative AI models that provide a response to prompts in a simple one-step process, agentic AI engages in complex, multi-step processes and interacts with various systems to achieve desired outcomes. For instance, an AI-powered help desk may autonomously reset passwords, install software updates, and elevate tickets to human staff when necessary. By 2028, Gartner predicts that at least 15% of day-to-day work decisions will be made autonomously by agentic AI, marking a significant leap from 0% in 2024.
While the promise of improved efficiency and cost savings is compelling, deploying agentic AI presents several challenges that must be addressed to ensure system trustworthiness and security. The complexity and autonomy of agentic AI necessitate a thorough understanding of its underlying mechanisms, proper integration and monitoring, as well as stringent security measures. Only with these considerations can businesses fully leverage its potential while mitigating associated risks.
Model Logic and Critical Thinking
Agentic AI typically involves a planner model that orchestrates the actions of multiple agents. The critical-thinker model provides feedback on the outputs of the planner and agents, enabling the system to learn and improve. The more feedback the model receives, the better the resulting outputs. To facilitate this, the critical-thinker model should be trained on data that is closely grounded in reality. This requires extensive training with specific goals, plans, actions, results, and feedback, potentially involving hundreds or thousands of iterations.
Training the critical-thinker model on realistic data ensures that the AI can make informed decisions. This process involves setting specific goals and plans, executing actions, and analyzing the results. Feedback loops are crucial for continuous improvement, and the model must undergo numerous iterations to refine its decision-making capabilities. This iterative process helps the AI system to better understand the context and nuances of various tasks, leading to more accurate and reliable outcomes.
To effectively train the critical-thinker model, companies must invest in high-quality, diverse datasets that reflect real-world scenarios. The use of synthetic data generated through simulations can be an effective supplement to this training. However, it is essential that synthetic data closely mirrors real-life scenarios to ensure accuracy. Careful attention to this aspect of training will result in a more robust and adaptive AI system capable of handling the unpredictable nature of real-world tasks effectively.
Reliability and Predictability
Traditional interactions with computers are predictable, with engineers providing step-by-step instructions. However, agentic AI operates differently—it leads with the desired outcome, allowing the agent to determine the steps to achieve it. This degree of autonomy introduces randomness in the outputs. The experience with ChatGPT and other large language model (LLM)-based generative AI systems, which showed significant improvements in output consistency over time through fine-tuning and human feedback loops, indicates that similar efforts are needed to minimize randomness in agentic AI to enhance reliability and predictability.
To achieve this, developers must implement rigorous testing and validation processes. By continuously monitoring the AI’s performance and making necessary adjustments, they can reduce the variability in outputs. Additionally, incorporating human feedback loops can help fine-tune the system, ensuring that it consistently delivers reliable and predictable results. This approach not only enhances the AI’s performance but also builds trust among users, who can rely on the system to make accurate decisions.
Building predictability into agentic AI systems requires the establishment of comprehensive guidelines and frameworks that dictate how the AI should respond to various scenarios. This might involve creating rule-based systems alongside more complex machine learning algorithms to ensure that outcomes are not only consistent but also aligned with organizational objectives. By providing this scaffolding, businesses can ensure that agentic AI systems behave within acceptable parameters, enhancing both their effectiveness and user confidence.
Data Privacy and Security
Data privacy and security are key concerns when using agentic AI. Unlike static generative AI models, agentic AI systems often access multiple data sources with high levels of autonomy, increasing the risk of private data exposure. Measures such as data anonymization—obscuring user identities and stripping personally identifiable information from prompts—are essential before sending data to the model. Companies should start with small-scale deployments, containerizing the data to limit exposure and ensuring it remains within the necessary internal domain.
Different types of agentic AI systems—consumer agentic AI, employee agentic AI, and customer-facing agentic AI—present varying security implications. Consumer agentic AI involves an internal user interface interacting with an external AI model, where control over the AI rests solely on managing the data and prompts. Employee agentic AI is built internally for internal use but still risks exposing sensitive information to unqualified users. Customer-facing agentic AI, designed to serve customers, must have robust segmentation to avoid exposing private customer data.
Deploying robust encryption methods and implementing stringent access controls can significantly reduce the risk associated with agentic AI systems. Regular security audits and assessments can ensure that weaknesses in the system are identified and addressed promptly. Furthermore, having clear procedures for data handling and processing, as well as ensuring adherence to regulatory requirements such as GDPR or CCPA, can help mitigate privacy concerns. By taking these steps, companies can protect sensitive information while leveraging the benefits of agentic AI.
Data Quality and Relevancy
Ensuring the agentic AI model’s outputs are grounded in accurate, current, and relevant data is another significant challenge. Systems must access high-quality data from diverse platforms and sources. Data streaming platforms can be useful, enabling developers to use tools like Apache Kafka and Kafka Connect for integrating data from different sources and Apache Flink for model communication. The success of agentic AI hinges on its ability to access reliable, fresh data, which will help mitigate hallucinations and ensure accurate responses.
High-quality data is essential for the AI to make informed decisions. Developers must ensure that the data is not only accurate but also relevant to the tasks at hand. This involves integrating data from various sources and continuously updating the system with the latest information. By doing so, the AI can provide more accurate and relevant outputs, reducing the risk of errors and improving overall performance.
To maintain high data quality, continuous processes for data validation, cleaning, and updating are crucial. Machine learning and AI algorithms themselves can be used to detect anomalies in data, ensuring that only the most relevant and accurate information is used for decision-making processes. Moreover, collaborating with data providers to understand the provenance and reliability of their data can further enhance the efficacy of agentic AI systems. This end-to-end approach to data management ensures that all inputs are both high-quality and fit for purpose.
ROI and Talent
Entering the realm of AI involves substantial investments in hardware, GPUs, and new data infrastructures, including memory management for caching and storage. Building in-house inference models requires new talent with specialized AI skills or training existing workers, contributing to upfront costs. The return on investment (ROI) may take time, particularly for early adopters. Despite these hurdles, agentic AI is anticipated to spread through enterprises as generative AI has. For example, GitHub Copilot has transitioned from automating coding processes to acting in an agentic way to write and test code autonomously.
Another key aspect of ROI is the ongoing cost associated with the maintenance and updating of AI systems. Continual improvements and iterations based on user feedback and advancements in underlying AI technology will be necessary. Investing in a talented workforce that can manage and evolve these systems is crucial. Companies need to balance the cost of these investments against the potential efficiency gains, improved decision-making, and other benefits that agentic AI promises. Success in the domain will likely come from a strategic approach to both investments and scaling AI solutions.
Partnering with universities, engaging in collaborative projects, or facilitating apprenticeships in AI can help bridge the talent gap while promoting innovation. Companies can also leverage AI platforms and services that lower the barrier to entry, allowing teams to implement sophisticated AI solutions without needing to develop everything from scratch. This mixed strategy can help firms efficiently deploy agentic AI systems, fostering growth and operational excellence while managing the associated costs and resource requirements effectively.
Conclusion
As generative AI gains traction, it’s transforming into a more sophisticated and advanced form called agentic AI. Traditional generative AI models respond to prompts in a straightforward, one-step manner, while agentic AI undertakes complex, multi-step procedures and interacts with various systems to achieve desired results. For example, an AI-driven help desk might autonomously reset passwords, install software updates, and escalate tickets to human staff when necessary. By 2028, Gartner forecasts that at least 15% of daily work decisions will be autonomously made by agentic AI, a dramatic increase from 0% in 2024.
Despite the allure of improved efficiency and cost savings, deploying agentic AI comes with challenges that must be tackled to ensure system trustworthiness and security. The complexity and autonomy inherent in agentic AI require a deep comprehension of its core mechanisms, seamless integration, diligent monitoring, and robust security measures. Only by addressing these factors can businesses fully exploit its potential while mitigating associated risks.