The right prompt can direct today’s AI systems to generate content, execute challenging operational routines, solve and predict outcomes for complex scenarios, and much more. But imagine if the prompt wasn’t needed all the time, and the AI can autonomously make the right choice to drive processes, generate outcomes, and power complex tasks on its own with minimal human intervention. This is exactly what Agentic AI workflows can establish in the coming years.
Agentic AI can proactively learn scenarios, set targets, and evolve from feedback generated by its execution workflows. In short, it is a self-executing, decision-making entity that can take calls on the value it creates for users without human guidance. The adoption of Agentic AI is peaking. A major factor influencing the growth is its ability to provide significant benefits in everyday customer and operational scenarios through autonomous interactions.
Gartner says that 80% of all customer service issues can be dealt with by Agentic AI agents by as early as 2029. While more sectors embrace the tech, it is equally important for leaders to understand how exactly Agentic AI workflows manage the barrage of unstructured data that comes their way from daily interactions. And most importantly, how does it handle ambiguity?
The basics of an Agentic AI workflow
Traditional AI models work by executing a series of instructions based on logic derived from their training ecosystem. When it comes to an agentic workflow, the AI model executes several tasks in parallel, with AI agents taking care of individual subroutines within a bigger complex problem. For example, we are used to seeing generative AI models build a range of outputs like images, text, etc. In an agentic AI system, an entire book can be made by the system with multiple parallel AI agents working on images, text, structure, etc. This implies that agentic AI workflows can handle nonlinear tasks that involve complex decision-making.
How can Agentic AI workflows handle ambiguity and unstructured data?
At the heart of every agentic AI is a set of powerful AI agents connected to a strategic data pipeline. These agents are trained through a series of established decision-making algorithms and machine learning and can adopt a range of learning approaches, like rule-based, reinforcement, optimization, and deep learning.
Taming the data
Agentic AI systems operate with a powerful integrated data strategy, which ensures a strategic data pipeline to supply the right insights in the right format to the right agent. This involves preprocessing of the data to filter out inaccuracies, and validate for correctness, integrity, and veracity of the data.
The pipeline also ensures that necessary statistical and exploratory analysis is performed on the data to break down complex data streams and transform them into the right data structure needed for algorithmic computation and processing.
The pipeline is also responsible for the output that the agents produce. It combines, transforms, and translates into text, media, reports, or any other visual representations, such as dashboards.
The Brain behind the magic
Agentic AI models leverage a wider scope of AI capabilities to operate and execute complex workflows. It combines natural language processing, analytics, algorithm-driven machine learning, and autonomous reasoning abilities to decipher even the most sophisticated queries and contexts encountered. From an operational perspective, an Agentic AI system works by leveraging 5 primary agents to handle different use cases:
Reactive agents
These are like traditional AI agents, which are programmed to respond to a specific stimulus, such as a user query or an event triggered by a change in the operational state of the system.
Deliberative agents
They form an internal functioning body that encompasses an operating model of its own with resources needed to analyze a situation, plan a response according to strategically trained knowledge and insights it has access to within its ecosystem, and make accurate decisions based on the plan.
Learning agents
They are responsible for studying feedback as well as insights from scenarios where the agentic AI is used and then use the feedback to improve future interactions and generation capabilities, like better recommendations or personalized results.
Multi-agent systems
They comprise 2 or more agents that can synchronize with each other and collaborate to execute complex tasks together to achieve accurate results. The book production example covered earlier is the perfect example.
Hybrid agents
They can combine the capabilities of 2 or more of the above agents to handle very complex scenarios at scale, like in the case of a self-driving vehicle, wherein capabilities like reactive, deliberative, learning, etc., are all needed in a random yet coordinated fashion.
Towards infinite possibilities
Agentic AI workflows are self-reliant ecosystems that can autonomously analyze and solve complex problems on their own. As evidenced above, they can understand even unstructured data and ambiguity through powerful reasoning and by leveraging a range of data processing and NLP-driven techniques.
The possibilities and use cases for such a radical technology are nearly limitless. From banking to autonomous driving and smart factories with zero human intervention, the next decade of AI innovation will likely be spearheaded by agentic AI. As an enterprise, it is critical to stay ahead in the race to build capabilities in such amazing technologies and become a leader in the market. This is where collaborating with a knowledgeable technology partner like Wissen can make a huge difference. Get in touch with us to know more.
