AI agents: the next phase of artificial intelligence in business

It’s Monday morning, an efficiency enabler has already prioritized your emails, scheduled meetings, and drafted responses to your clients while you made your first coffee. The scenario is not a futuristic vision; but a potential reality with agentic AI. And the new efficiency enabler in your team could be an AI agent.

Over the past 18 months or so, AI has rapidly become the cornerstone of innovation. Indeed, 77% of enterprises in the U.S. report having AI projects in production or evaluation. But as businesses strive to leverage AI’s potential, a paradigm shift is emerging.

By merging autonomy — aka independent acting — with intelligence, agentic AI is redefining how organizations work, by empowering newly achievable levels of efficiency.  

How does agentic AI differ from generative AI? What exactly are AI agents?  

AI agents are software entities that not only leverage generative AI ability but are able to observe fluid data to plan and complete actions to reach specific goals. They range from simple assistants to fully autonomous agents, and are also specifically designed to specialize in certain tasks and areas. They extend beyond static algorithms to interact dynamically with both other systems and users.

Unlike traditional AI systems designed primarily to analyze data and offer insights, or generative AI which produces various forms of output based on input data and learned patterns, agentic AI is able to perceive an environment, reason strategically, make decisions and independently take action, and learn from its actions to achieve desired goals with improvement. 

Agentic AI encompasses defining capabilities: 

• Autonomy: Unlike traditional systems, agentic AI doesn’t merely suggest actions but executes them independently. In a financial context, for example, an AI agent can reconcile accounts or identify fraud without step-by-step prompting. 

• Goal-oriented behavior: AI agents have defined objectives and actively strive to achieve them through reason and planning.     

• Proactive interaction: AI agents actively perceive and interact with their environment, initiating actions and adapting to changing circumstances.      

• Learning and adaptation: AI agents are designed to learn from their experiences and adapt their behavior over time to improve performance.  

Agentic architecture unlocks the power of AI agents 

One of the most impressive uses of AI agents is self-driving autonomous cars. Such a use case is achieved with various AI agents that are housed within an agentic software architecture. Each one is specifically designed to focus on individual areas. In this case that includes perception, path planning, motion control, and risk assessment. The AI agents act in unison within a dynamic and complex task environment — not unlike the multifaceted space that enterprise business processes inhabit.

And while personal uses of agentic AI are incredibly captivating, its impact on businesses is unfolding and about to become just as significant. To fully harness such potential of AI agents, businesses need the right architecture — a flexible but controlled environment for the development, implementation, and operation of customizable AI agents across an organization.

An agentic architecture is essential to realize the benefits of AI agents and to overcome the limitations imposed by legacy systems or rigid, off-the-shelf solutions. An agentic architecture achieves this by enabling various important business critical abilities. 

Customizable scalability

An agentic architecture enables organizations to design AI systems tailored to their specific workflows, ensuring flexibility and scalability as business needs evolve. This customization fosters greater adaptability than rigid, off-the-shelf offerings or individual point solutions. 

Integration across systems

Agentic systems are built to integrate effortlessly across platforms, ensuring AI agents can work cohesively with tools such as CRM, ERP, and supply chain applications. This in-built interoperability removes the need to rebuild connectivity in each individual agent.  

Enhanced operational reliability

Centralized architectures ensure that AI agents operate consistently, reliably, and cost-effectively. By reducing inefficiencies caused by fragmented systems, businesses can achieve uninterrupted workflows and superior performance. 

Robust security and governance

A controlled, agentic architecture allows organizations to define agent entitlements and implement stringent security protocols. This framework safeguards sensitive data and ensures compliance with evolving regulatory standards. 

Future-proof flexibility

Built with open frameworks, agentic architectures accommodate emerging AI technologies and innovations, enabling businesses to remain competitive without costly infrastructure overhauls. 

What industries stand to benefit most from AI agents? 

Agentic AI is set to enable newly achievable efficiency for multiple industries by optimizing processes, enhancing decision-making, and unlocking intelligent collaboration. Some best positioned to capitalize on AI agent business use cases include: 

• Financial services: AI agents can automate fraud detection, streamline risk assessments, and enhance compliance processes, reducing human error and increasing operational security. 

• Healthcare: From triaging patients to managing administrative workflows and even assisting in diagnostics, AI agents are transforming how medical professionals deliver care efficiently and accurately. 

• Manufacturing and supply chain: Predictive maintenance powered by AI agents can help reduce downtime, optimize logistics, and improve operational efficiency across complex supply environments. 

• Customer engagement: AI agents can further enhance customer engagement and satisfaction via automated chat, personalized recommendations, and 1-1 support. 

• Legal: AI-driven agents can support legal teams in areas such as automated contract reviews, constantly monitored regulatory changes, and ongoing compliance with industry standards. 

As AI agents continue to evolve, organizations that embrace their capabilities today will lead the way. Early adopters will streamline efficiency and gain a significant competitive edge in an increasingly automated marketplace.