Agent-based artificial intelligence (AI) is a major advance in this field, allowing systems to operate independently without the need for continuous human intervention.
Unlike traditional AI, which relies on predefined instructions, agent AI can make decisions, take actions, and learn in real time. This autonomy allows you to adapt to changing conditions, optimize workflows, and achieve complex objectives with minimal oversight.
Agent AI operates through a cycle of perception, inference, action, and learning. It collects data from the environment, interprets the context, and proactively acts based on predefined goals. Another distinguishing feature is reinforcement learning. This allows the agent AI to improve its behavior through feedback. Additionally, advanced language capabilities allow you to manage multi-step tasks with minimal guidance, making them suitable for roles such as virtual assistants and automated customer support.
This dynamic adaptability makes it ideal for highly dynamic environments and effective in scenarios where static, rule-based AI systems are inadequate. For example, supply chain management can monitor real-time demand fluctuations, adjust inventory levels, and autonomously reroute shipments. In finance, you can assess market conditions, execute trades, and reduce risks autonomously. Its key differentiator is its ability to act independently while continuously optimizing performance.
How agent AI solves problems
Agent AI follows four stages.
Perception stage: Collecting and interpreting data from various sources such as sensors, databases, and digital systems to understand the environment. Inference stage: Leverage Large Language Models (LLM) to analyze tasks, generate solutions, and tune AI models for specific functions such as content generation, visual processing, and recommendations. Techniques such as search augmentation generation (RAG) ensure accurate and context-relevant output. Action stage: Executed through an application programming interface (API). This allows you to perform tasks efficiently while following pre-established guidelines. For example, customer service AI can automatically process claims within a certain threshold and flag larger claims for human approval. Learning phase: Use feedback loops, also known as the “data flywheel,” to analyze interactions and outcomes, refine decision models and strategies, and increase their effectiveness over time.
This self-optimization capability makes agent AI a powerful tool for businesses looking to improve decision-making and operational efficiency.
Differences between agent AI and traditional automation
Enterprise automation is already transforming industries by streamlining workflows and increasing efficiency. However, traditional automation relies on fixed rules and structured processes, which limit flexibility. These systems often fail or require human intervention when dealing with inconsistencies or unexpected issues.
Agentic AI overcomes these limitations by simulating human-like judgment and adaptability. For example, while traditional automation may struggle to process invoices with missing data or formatting issues, agent AI can recognize discrepancies, infer missing information, and resolve issues autonomously.
Although agentic AI is different from narrowly autonomous systems or the still-theoretical concept of artificial general intelligence (AGI), which aims to replicate human-like intelligence, many experts speculate that it may become a reality in the 23rd century. And while autonomous AI, such as self-driving cars and robotic assistants, can operate independently, they are typically designed for specific, narrow applications beyond which they cannot adapt.
Agent AI therefore occupies a pragmatic middle ground, offering greater autonomy and adaptability than traditional automation while focusing on pragmatic, goal-oriented uses rather than broadly replicating human cognition.
Benefits of agent AI
In today’s fast-paced business environment, companies face increasing challenges, from rising costs and intense competition to constant pressure to innovate. While traditional generative AI has streamlined certain processes, it has stopped short of providing a fully autonomous end-to-end enterprise solution. Agentic AI bridges this gap by managing complex workflows with greater autonomy and adaptability.
Therefore, by integrating agent AI, organizations can scale operations more effectively, respond quickly to dynamic situations, and free up employees to focus on high-value tasks. This change not only improves productivity, but also fosters innovation and enables companies to achieve long-term success in an increasingly competitive environment.
Dr. Mark Nasila, Chief Data and Analytics Officer, FNB Chief Risk Office
