What is agentic AI, and how can you capitalize on the emerging innovation?
Agentic AI refers to artificial intelligence (AI) systems capable of independent decision making, planning and adaptive execution to complete processes and achieve specific objectives. While many organizations have already taken advantage of AI and generative AI's immense potential to optimize and elevate operations, agentic AI systems represent the next evolution of AI technology. With agentic AI systems, companies can leverage sophisticated solutions that solve complex problems by acting and adapting autonomously, with limited human intervention.
While agentic AI benefits greatly from large language models (LLMs), it is more complex than LLMs since those models rely on heavy amounts of inputted data that may be outdated and not designed to learn on their own. On the other hand, agentic AI leverages reasoning and planning capabilities to solve complex, multistep problems, transforming how humans interact with machines.
AI agents can break down complex problems into sequential tasks—each task having its own context, tackling them one by one while still learning from prior conclusions, actions and logic. AI agents stretch beyond their inputted data training environment. They can learn in real-time from countless sources across the internet or within the data platform in which they are deployed.
The spectrum of AI agents
When considering an agentic AI strategy, companies have a spectrum of options, from simpler, more straightforward information retrieval to fully automating a step or process. The complexity of an AI agent depends on the user and the goals of the specific process. Options include:
"For customer service, for example, with agentic AI, you can implement end-to-end automation from the customer interaction point," says RSM Director Robbie Beyer. "The automation encompasses and streamlines how customers interact with the model, the system and the record they're integrated with, how they retrieve information, how they update records within the organization and then start an action for the customer support team to follow up with the customer or resolve their query."
What are the benefits of agentic AI?
Ultimately, agentic AI can revolutionize various business functions, and extensive research has already been applied to several industries. Potential benefits are vast and include:
What are the challenges and risks of agentic AI?
Despite its potential, agentic AI poses several challenges, some more significant than popular LLMs. With its increased complexity and process transformation ability, the related risks of AI agents include:
- Security risks: Autonomous systems may be vulnerable to attacks, leading to detrimental actions. This risk is heightened as little human interaction can mean attacks may be detected later than usual without constant supervision. Establishing robust security measures is crucial to protect AI systems from malicious activities.
- Unpredictable behavior: Relying completely on an AI agent with a lack of human intervention can also lead to biased, unexpected or strictly incorrect results. AI agents must be designed thoughtfully to handle unforeseen situations and make decisions that align with ethical standards. Agents may also need to be trained to use trustworthy and unbiased data in their research and autonomous learning.
- Ethical concerns: Potential issues such as bias, job displacement and accountability need to be addressed. Developing ethical guidelines, frameworks and controls is essential to mitigate these concerns.
- Resource utilization: Complex systems can be computationally intensive, creating environmental and operational expense issues. Efficient resource management and sustainable practices are necessary to minimize the environmental impact.
The increased level of potential risk underscores the importance of leveraging an effective AI governance framework — such as RSM's AI Governance Framework — built to manage the complexity of agentic AI solutions and strategies.
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