Agentic AI: Transforming autonomous decision making

ARTICLE | May 15, 2025

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models (LLMs), it is more complex than LLMs since those models rely on heavy amounts of inputted data that may be outdated and not designed to learn on their own.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:

Retrieval

AI-powered agents retrieve and synthesize data from trusted data sources, providing users with accurate responses, summarization and reasoning to support decision making.

Actions

Agents can go beyond retrieval by executing tasks, automating workflows and handling repetitive processes, increasing efficiency and reducing manual workload.

Automations

Advanced AI agents operate independently, dynamically planning tasks, orchestrating multiple agents learning from interactions and escalating when necessary to drive autonomous decision making.

“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 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.

Agentic AI examples by industry

Many industries already have the potential to utilize agentic AI tools and applications to transform critical business processes. Examples include:

• Health care: AI agents can provide autonomous appointment scheduling, patient account management and personalized treatment plans, and even assist in diagnosing conditions by analyzing medical data.
• Customer service: AI-driven chat agents can handle complex inquiries and personalize interactions. These agents can provide 24/7 support, resolve issues and offer personalized recommendations based on customer history.
• Logistics and supply chain: Companies can leverage agentic AI to develop self-adaptive systems for inventory management and route optimization. AI agents can predict demand, optimize supply chains and manage inventory levels to reduce costs and improve efficiency.
• Finance: AI agents can conduct credit scoring, fraud detection and investment management. These systems can analyze financial data, detect fraudulent activities and provide investment advice based on market trends.
• Human resources: Agentic AI systems can enhance recruiting, performance evaluation and employee engagement efforts. AI agents can screen resumes, conduct initial interviews and monitor employee performance to improve HR processes.
• IT service desk agent: Agents can create an intelligent service desk solution that analyzes issues, suggests solutions and routes or elevates more significant concerns to the appropriate parties. It can quickly support lower-priority requests while humans can focus directly on more critical tasks.

Buy versus build?

Companies looking to implement an agentic AI approach have the same pivotal question as they do with other AI and generative AI solutions: buy or build? The decision point revolves around whether a company should just buy the agent capability that an existing software provider says it has or build an application bespoke to the organization.

Buying a solution can certainly be effective, but the company will have less control and customization ability. On the other hand, building a solution inherently provides much more flexibility.

In addition, a successful agentic AI build strategy can increase cost optimization efforts as the infrastructure can be replicated and expanded into different business areas and scaled into more use cases. Expanding agentic AI is also possible in a buy scenario, but that may require multiple purchases from different software vendors for different areas of the business and workflows that may not fully align. That environment could result in higher software spending and a bigger maintenance burden over time.

What is the future of agentic AI?

Agentic AI represents a significant leap forward in AI technology, offering exciting opportunities, but also introducing formidable challenges. By understanding their mechanisms, applications and implications, your company can better prepare for the transformative impact of these autonomous systems.

The future of agentic AI looks promising, with ongoing research and development aimed at enhancing its capabilities and addressing current challenges. In fact, by 2028, Gartner predicts that 33% of enterprise software applications are expected to include agentic AI and 15% of day-to-day work decisions are projected to be made by agentic AI. This integration will drive efficiency and innovation across various sectors and play a crucial role in strategic decision-making processes.

How to get started with agentic AI

Like other recent AI developments, agentic AI is evolving rapidly and delivering significant benefits to companies that have leveraged thoughtful implementation strategies. While utilizing AI agents may introduce additional risk, they can also provide additional efficiency, productivity and cost optimization to help your business build a competitive advantage.

Ready to get started? Contact our team to learn more about how agentic AI can transform your key business operations.

Source: RSM US LLP.
Reprinted with permission from RSM US LLP.
© 2024 RSM US LLP. All rights reserved. https://rsmus.com/insights/services/digital-transformation/agentic-ai-transforming-autonomous-decision-making.html

RSM US LLP is a limited liability partnership and the U.S. member firm of RSM International, a global network of independent assurance, tax and consulting firms. The member firms of RSM International collaborate to provide services to global clients, but are separate and distinct legal entities that cannot obligate each other. Each member firm is responsible only for its own acts and omissions, and not those of any other party. Visit rsmus.com/about for more information regarding RSM US LLP and RSM International.