By Jyotsna Grover
Whether or not you’re immersed in the AI world, the term Agentic AI is all the rage nowadays. If you haven’t heard about it yet, well, today’s your lucky day. In keeping with how most things go, it’s already a polarizing topic and experts have chosen sides. Alright, I’m exaggerating, maybe not a polarizing topic quite yet but it’s definitely one that has people on opposite ends of the spectrum – on one hand there’s those who have embraced it, then there are those who are outright naysayers, and others who are skeptical.
While some industry analysts have told us they are bullish on the concept others view it as nothing more than the latest marketing buzzword.
What is Agentic AI?
Simply explained, the concept of Agentic AI refers to AI systems that possess a degree of autonomy and decision-making capabilities. Think of it like an “agent” acting independently within its environment. Unlike traditional AI models that function purely as tools or assistants, agentic AI can initiate actions based on its own assessments, goals, and understanding of its environment.
According to Gartner, Agentic workflows represent a paradigm shift from traditional, linear processes to dynamic, AI-driven systems where humans and machines collaborate seamlessly. Unlike autonomous AI systems, agentic workflows emphasize the complementary roles of humans and AI in achieving shared objectives, with AI serving as a cognitive catalyst and humans providing context, intuition and oversight.
Understanding Agentic AI
Agentic AI differs from conventional AI in its autonomy and intentionality. Traditional AI systems, such as machine learning models used in predictive analytics or natural language processing (NLP), operate within predefined boundaries and require explicit instructions or inputs from users. These systems excel in streamlining business processes like data processing, pattern recognition, and automating routine workflows but remain fundamentally reactive.
Agentic AI, on the other hand, is characterized by its ability to make decisions and take actions without direct human input. It can set and pursue goals, interact with its environment, and adapt its behavior based on feedback. This capability moves AI beyond the realm of tools and into a more complex, interactive role that can potentially alter the dynamics of human-computer interaction.
The Opportunities and Challenges of Agentic AI
The promise of agentic AI lies in its potential to revolutionize industries by enabling machines to perform complex tasks with minimal human oversight. For example, in sectors like logistics, healthcare, or financial services, agentic AI workflows could optimize operations by autonomously managing supply chains, diagnosing medical conditions, or making investment decisions. This could lead to significant efficiency gains, cost savings, and new business models.
However, the autonomy of agentic AI also introduces significant risks and ethical concerns. Autonomous systems could act unpredictably or in ways that are not aligned with human values or business goals. There is also the potential for misuse, where agentic AI could be deployed in ways that are harmful or unethical – some examples of this include self-driving cars, surveillance systems, autonomous weapons systems, analyzing voter data and target campaign messages, and many others. These risks necessitate a careful and thoughtful approach to the development and deployment of agentic AI.
The Hyperscience Perspective on Agentic AI
At Hyperscience, we’re focused on applying AI to automate, augment, and increase productivity of humans and the organizations where they work. We certainly recognize the potential of agentic AI to transform how businesses operate by enabling more autonomous decision-making and complex problem-solving capabilities. However, it’s crucial to feed Agentic AI workflows with extremely clean business data and ensure that quality controls for this key first step are airtight.
There has been plenty of literature recently about how most LLMs have reached a point of saturation and read all the public data out there. So much so, that there is no more data to train these models. My favorite piece on this topic is this article by Deepa Seetharaman at The Wall Street Journal. The article focuses on how organizations are now turning to synthetic data to train their LLMs and while we strongly believe that synthetic data has its place in the world, it’s not the only option left. A huge opportunity that many seem to be completely leaving out is the need to train LLMs using an enterprise’s own ground truth data. Several cross-industry studies show that on average, less than half of an organization’s structured data is actively used in making decisions—and less than 1% of its unstructured data is analyzed or used at all.
The saying ‘garbage in, garbage out,’ could not be more relevant when it comes to training models but using an organization’s own proprietary data ensures the models are tailored to their specific industry nuances and unique business needs, leading to more accurate and relevant insights compared to those derived from publicly available data.
Training LLMs aside, we recommend a ‘human in the loop’ approach for complex, decisioning tasks. Starting with a process where AI’s role is less complex allows for a smoother transition and a better understanding of its decision-making capabilities. For example, an AI system might initially make recommendations on whether to pay a claim. As it builds a track record and proves its reliability, it could then take on more autonomous decision-making responsibilities without requiring constant human oversight. This incremental approach not only improves efficiency and accuracy, but it also mitigates potential risk. Here are a few additional points for consideration when thinking of Agentic AI:
- Augmentation, Not Replacement: Fundamentally, we believe that AI should augment human capabilities, not replace them. Agentic AI should be designed to work alongside humans, enhancing their abilities in business process optimization, rather than making them obsolete. At Hyperscience, we champion “human-in-the-loop” technology. Our approach to AI prioritizes human review of decisions and data, ensuring fairness and accuracy. Whether it’s during input processing or as part of quality assurance, human oversight is crucial for explainability and transparency.
- Ethical and Transparent AI: As AI systems gain more autonomy, it becomes crucial to ensure they act in ways that are ethical and transparent. At Hyperscience, we are committed to developing AI that is aligned with human values and business objectives, with clear mechanisms for accountability and oversight. This includes building machine learning for business systems that are transparent in their decision-making processes, allowing users to understand and trust the actions of AI systems, and reporting on accuracy and Q/A to validate the performance of the models.
- User-Centric Design: For agentic AI to be effective and trusted, it must be designed with the end-user in mind. We advocate for a user-centric approach, where AI systems are intuitive, easy to interact with, and tailored to meet the specific needs of the business and its employees. This approach ensures that the AI’s actions are aligned with the user’s goals and that users feel in control of the technology.
- Iterative and Safe Deployment: The deployment of agentic AI should be iterative and carefully managed to ensure safety and reliability. We believe that testing and validating AI systems in controlled environments before widespread deployment is crucial. By taking a gradual approach, potential risks can be identified and mitigated, ensuring that the AI’s autonomy does not lead to unintended consequences.
- Collaboration and Regulation: Finally, we recognize the importance of collaboration between industry, academia, and regulators in shaping the future of Agentic AI. The development of standards, guidelines, and regulations is essential to ensure that agentic AI is used responsibly and benefits society as a whole. We are committed to contributing to this dialogue and working with stakeholders to create a sustainable and ethical AI ecosystem.
The Path Moving Forward
Agentic AI can provide a significant step forward in the evolution of artificial intelligence, offering the potential to revolutionize industries and redefine human-computer interaction.
As the field continues to advance and mature, we remain committed to leading the way with a thoughtful, ethical, and user-focused approach to Agentic AI.
We believe that the journey towards autonomous intelligence is just beginning and the future holds immense promise but there needs to be a balanced approach that delivers improvements in automation, productivity and business outcomes, while also maintaining trust and confidence of society at large.
