What is agentic AI?

Agentic AI, the latest evolution of artificial intelligence (AI), represents a profound shift in machine capabilities. Agentic AI is capable of autonomously acting and making decisions in line with a defined goal.

At its core, AI agents can perform automated tasks independently and respond to conditions in real-time.

Agentic AI vs. generative AI

Generative AI, epitomized by large language models (LLMs) such as ChatGPT, showcases an astonishing ability to create novel content, including text, images and code, based on training data. While incredibly powerful for content generation and creative tasks, generative AI primarily functions reactively, awaiting a prompt to produce output.

In contrast, agentic AI imbues AI systems with a sense of “agency”. Agentic AI is focused on decisions rather than solely creating content and it does not rely exclusively on human prompts. As a result, agentic AI can pursue complex tasks independently and break down a high-level goal into a series of sub-tasks. It continuously learns from its interactions with the environment and the system’s proactive nature allows it to gather data and adjust in real-time.

How does agentic AI function?

The way agentic AI operates fundamentally lies in a continuous self-improving feedback loop, often described as a “data flywheel”.

• Perception: The AI gathers real-time data from diverse sources such as APIs, databases, or even sensors in the physical world, ensuring it has up-to-date information.
• Reasoning: It processes this data using advanced techniques like natural language processing and machine learning to understand context, identify patterns and interpret queries.
• Goal setting: Based on predefined objectives or user inputs, the AI sets specific goals and develops a strategy to achieve them.
• Decision-making: The AI evaluates multiple possible actions and selects the optimal one, considering factors like efficiency, accuracy and predicted outcomes.
• Execution: The AI carries out the chosen action, which might involve interacting with external systems (e.g., APIs, data platforms, or even robotic systems) or providing responses to users.
• Learning and adaptation: After executing an action, the AI evaluates the outcome and gathers feedback.
• Orchestration: Agentic AI can orchestrate multiple individual AI agents or systems to work collaboratively towards a larger goal.

Applications of agentic AI

Agentic AI has been integrated across various industries to make significant advancements in efficiency and decision-making.

1. Healthcare: Agentic AI has already made a big impact in the quality of care being offered to patients. Agentic AI can assist patients in a variety of ways, including patient monitoring, diagnoses and personalized treatment plans.
2. Customer service: Agentic AI systems have recently been developed to autonomously handle customer inquiries and provide personalized responses. They can also solve issues in real-time, meaning that a global customer base can access support 24/7, leading to improved customer satisfaction rates.
3. Manufacturing and supply chain efficiency: Predicting demand fluctuations and managing inventory levels is one of the major ways the supply chain is using agentic AI.
4. Enterprise workflows and business process automation: Agentic AI can automate routine and complex tasks by processing pooled data. What’s more, it can handle workflows with minimum human intervention from start to finish.
5. Retail: Using agentic AI, companies can adjust pricing in response to real-time variables like demand signals and competitor pricing.

The challenges of agentic AI

The implementation of agentic AI must be met with consideration of the following factors:

1. Unstructured data: While agentic AI can significantly improve efficiency by automating individual tasks, it is not focused on defining unstructured data. Also, it cannot manage more complex enterprise operations. Agentic AI “excels in task execution but may not be the ideal starting point for enterprises looking for a more holistic solution,” said Doug Shannon, global IA/generative AI thought leader and Gartner Peer Ambassador said in PEX Network’s intelligent automation report. 
2. Data privacy: To ensure the protection of sensitive data, robust security measures and compliance with data protection regulations is required. Depending on the specific use, agentic AI can require a lot of sensitive data, including financial records and healthcare databases.
3. Integration with existing systems: An agentic I system must be compatible with existing workflows and infrastructure to maximize its benefits.

Agentic AI governance

Organizations must develop comprehensive strategies to reasonably deploy agentic AI.

Human-in-the-Loop (HITL) strategies is a popular method of safely deploying and using agentic AI. As the name suggests, it involves keeping human involved at critical junctures in the AI life cycle to correct any errors and fine-tune the models. There are predefined thresholds to help decide when to escalate to a human, which prioritize uncertain and high-risk cases.

It is also important to establish ethical guidelines surrounding its use, which include notes in human rights, privacy and accountability. Clear governance frameworks and compliance measures are essential to define roles and responsibilities for all stakeholders involved in the development and us of agentic AI systems.

Without strong foundations in governance and data management, positions like the chief AI officer could be ineffective, said Gladwin Mendez, a recognized Forbes Tech 100 global leader in AI governance at PEX Network’s Agentic AI APAC Deep Dive.

The future of agentic AI

Whilst the AI systems have wide-reaching capabilities, it’s important to note that any vendors are contributing to an agentic AI hype. Many vendor are engaging in “agent washing” where they rebrand existing products (such as AI assistants) that don’t have agentic capabilities, under this label. Only about 130 of the thousands of agentic AI vendors are real, Gartner's research estimates.

That being said, the future of agentic AI is exciting and to stay ahead, businesses should take  a proactive approach to agentic AI adoption. Start by identifying the areas that agentic AI automation could yield the most value.

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