Capitalize on the AI Agent Opportunity

In a market with fierce competition and rapidly emerging technologies — each with their own risks and benefits — digital leaders are under constant pressure to adjust and adapt the business model.

Business model innovation aims to discover new ways to meet customer needs, differentiate from competitors and increase efficiency and profitability by optimizing or transforming the existing business model.

Organizations looking to innovate their business models should consider leveraging customer journey mapping. By addressing specific pain points along this journey, they can enhance the experience for customers, streamline operations and uncover new products, services or revenue streams. AI agents can be a key solution for organizations looking to mitigate customer pain points and capitalize on emerging opportunities.

AI agents are not a one-size-fits-all solution and come in a variety of shapes and sizes. Organizations should carefully consider each AI-agent solution and select the technology that makes the most sense for their organization and application.

• Types: There are six types of AI agents: reflex agents, goal-based agents, learning-based agents, utility-based agents, hierarchical agents and collaborative agents. Each type is suited to different scenarios and applications.

• Applications: AI agents are ideal for a variety of situations that require automation, decision making and intelligent interaction with the environment. 

• Interaction models: AI agents can function with different levels of human involvement, such as human-in-the-loop versus human-out-of-the-loop.

• Multiagent systems: Multiple agents can address complex tasks that individual agents cannot, resulting in more adaptable, scalable and robust solutions.

• Embedded AI techniques: AI agents use AI techniques to sense, navigate and adapt to their environment. Techniques include optimization, natural language processing, and knowledge representation.

• Risks: These agents are designed to act autonomously and proactively within an environment, often learning and adapting as they operate.

Organizations looking for AI agents that are focused on planning, reasoning and processing should consider those that use large language models (LLMs) to drive execution of tasks and processes. LLM-based AI agents can provide a useful and accessible alternative to more established types of AI agents.

LLM-based AI agents use both programmed and prompted behaviors that require careful design, evaluation and monitoring to ensure the desired outcome. These agents should be built using a modular and composable approach to the software architecture.