Introduction  

Generative AI undoubtedly emerged as a major focal point, attracting considerable interest from players across industries. As scientists, analysts, and economists diligently evaluate the potential impact and uses of GenAI, practitioners explore its adoption with caution, mindful of the inherent risks and limitations. In this article, we explain agents, propose principles and reference architecture to start up smoothly and offer our projections regarding the rise of Agentic AI.

AI agents have gained traction based on the Intelligent Agent concept. Originating from economic theory and enhanced by Chain-of-Thought research, this involves external communication to complete tasks satisfactorily. Combining these principles has led to multi-agent AI frameworks, automating minor and complex tasks. Agents are now used for data collection, document, and report generation in finance, banking, public sector, and supply chain, indicating a move from simple agents to complex multi-agent systems.

1. What is an AI agent?

AI agents are reasoning engines that understand context, plan workflows, connect to external tools and data, and execute actions to achieve specified goals. They replicate human qualities such as language processing, planning, reasoning, reflection, and using tools and data. Their ability to  learn, remember, and avoid mistakes allows for continual improvement.

In the business, AI agents resemble human workers. Both need careful selection, thorough training, and proper tools to perform effectively. Strategic deployment and consistent management are crucial to ensure efficiency and value addition. Integrating AI agents with human-like cognitive skills helps businesses improve productivity, streamline operations, and achieve greater success.

Last year, many simple agents were explored and released. This year, the trend has shifted from simple agents to complex multi-agent systems and ecosystems, enhancing communication with models and task execution.

DIAGRAM 1: Agent capabilities

2. Six ways to implement Agentic AI in your organization

As AI agents increasingly mimic human-centric skills more effectively than older systems, our recommended AI agent design and management principles align with organizational and HR standards. Follow these six key considerations for a successful AI agent deployment:

1. Role-based design: Assign agents to perform roles by grouping similar activities, reducing confusion, overlaps, and complexity. This enables reuse across systems and domains.
2. Domain tailoring: Tailor agents to specific business areas with unique processes and data. While some agents can handle multiple domains, most should be specialized.
3. Balance responsibilities: Balance the scope of AI agents' responsibilities to avoid cost increase and governance issues with too many agents, and bottlenecks with too few agents.
4. Controlled access: Limit agents to essential tools, data, and skills to reduce risk and improve output. Split responsibilities if an agent needs more than five tools.
5. Prioritize feedback: Design agents for self-reflection and feedback from other agents and humans to ensure continuous learning and adherence to standards.
6. Embrace reinvention: Allow multi-agent AI systems to transform enterprise architecture by addressing emerging needs innovatively, not just automating tasks.

3. Maximizing the potential of AI agents

The key is to view a multi-agent AI system as an ecosystem of capabilities, not isolated solutions, and create a reference architecture that supports both business and technical processes

This enables rapidly scale, expansion, and reuse of AI agents and frameworks across various use cases while streamlining governance, monitoring, operation, and improvement of outputs. Each layer is loosely coupled and independent, allowing for adaptation, connection, and application of best-fit solutions for any use case.

DIAGRAM 2: Layers for reference architecture

Multi-agent ecosystems are highly adaptable, opening up numerous use cases across your organization. Popular and efficient use cases in the market recently include:

• Finance reporting: Automating data collection, documentation, and report generation in finance, banking, public sector, and supply chain to ensure accuracy and timeliness in financial analysis and reporting.
• Human resources: Providing real-time employee support, responding to benefits questions, assisting with general queries, and aiding in recruitment processes like answering candidate questions, scheduling interviews, and managing onboarding paperwork.
• Information technology: Dynamically addressing current issues, integrating data from various IT management systems, resolving IT tickets faster and more accurately, and automating repetitive tasks like password resets and software access.

DIAGRAM 3: Reference architecture applied to IT helpdesk use case

To improve IT operations, consider optimizing the support for a business software application. Traditionally, this process involves multiple interactions between the support team and the business users. The new AI-enabled process uses a four-layer architecture to streamline this workflow.

Users get continuous updates with less involvement, while support personnel focus on monitoring, reviewing, and approving solutions rather than handling everything themselves. This way, human support can address more complex and critical issues, allowing business users to concentrate on  generating enterprise value.

4. Future expectations

The fast evolution of multi-agent AI systems is transforming how organizations address challenges and streamline processes. The commercial availability of language models, advanced agent ecosystems, and supportive frameworks are driving rapid advancements in Agentic AI.

Organizations that systematically design and manage these systems will be able to scale effectively. By applying AI across different use cases and domains, instead of restricting them to isolated processes, they can exponentially enhance its impact.

In the future, we foresee substantial advancements in automation and innovation with larger, more sophisticated agents and networks of agents working together. These agents will be more accurate, sophisticated, and accessible, potentially having their own marketplaces and info exchange systems.

Unlike some LLM applications, agentic AI is still being explored and requires more study on security, scalability, and performance risks. Organizations should consider these factors but also begin to benefit from agentic AI.

Using core principles and robust reference architectures helps organizations maximize the potential of multi-agent AI systems. This boosts AI investments and gives them a competitive edge in technological.