As Artificial Intelligence (AI) evolves, Agentic AI has emerged as a gamechanger across industries. A Salesforce survey revealed that 69% of Australian and New Zealand C-suite executives prioritise Agentic AI for innovation1. Unlike Generative AI, which produces content, Agentic AI actively makes strategic decisions, akin to how Tesla’s self-driving technology adapts to real-time traffic data. This distinction is crucial for businesses looking to harness the full potential of AI technologies.
What is Agentic AI?
Agentic AI systems autonomously perform tasks, making decisions based on data analysis and environmental interactions. Using machine learning, natural language processing, and reinforcement learning, they operate independently with minimal human intervention.
What are the key characteristics of Agentic AI
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Autonomy: Operates independently, adapting to its environment without continuous supervision.
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Decision-Making: Evaluates scenarios to determine the best course of action autonomously.
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Interactivity: Engages actively with external conditions, gathering information to refine strategies on-the-fly.
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Problem Solving: Capable of solving complex problems and executing multi-step strategies effectively.
Why is Agentic AI a buzzword in 2025?
The term "AI agents," "agentic AI," and "agent-based AI" has been swirling around the tech space since the early 2000s, when advancements in machine learning and robotics began to pave the way for autonomous systems. NVIDIA CEO Jensen Huang describes Agentic AI as "one of the most important things that’s happening in the world of enterprise2. It gained more popularity since companies like Tesla and Waymo used Agentic AI in their autonomous vehicles to enhance navigation and safety3. The result? A significant reduction in accidents and improved efficiency in transportation systems.
Agentic AI has diverse applications across various industries:
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Healthcare: Companies like Propeller Health use Agentic AI to automate patient care processes through smart inhalers that monitor medication usage and provide real-time insights for better health outcomes4.
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Logistics: Amazon is heavily investing in robotics and AI to automate its warehouse operations. In its Shreveport fulfillment center, automation has led to a 25% reduction in operational costs, showcasing how AI-driven systems can autonomously manage supply chains and enhance efficiency5.
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Customer Service: Comcast has implemented an "Ask Me Anything" feature that integrates large language models to assist customer service agents in real-time. This AI-driven tool provides accurate responses during live interactions, reducing handling time by approximately 10% per conversation and improving overall customer satisfaction efficiency6.
According to the MIT AI Agent Index, the evolution of Agentic AI marks a shift towards more advanced autonomous agents that can adaptively collaborate with humans, improving operational efficiencies across sectors7.
What’s inside Agentic AI?
Building on its core capabilities, Agentic AI leverages advanced technologies to achieve autonomy such as, among others:
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Machine Learning Architectures: Deep learning enables agents to analyse vast amounts of data, facilitating informed decision-making.
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Reinforcement Learning: Agents learn optimal actions through trial and error in dynamic environments, enhancing their decision-making capabilities over time. For instance, customer service AI agents use reinforcement learning to refine interactions, improving user experiences dynamically.
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Large Language Models (LLMs): These models serve as the cognitive engine, processing complex information and generating sophisticated reasoning strategies. LLMs are integral in understanding natural language and generating context-specific responses.
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Natural Language Processing (NLP): NLP enables agents to understand user input and process it effectively, facilitating smooth communication between users and the platform.
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Retrieval-Augmented Generation (RAG): This technique enhances AI models by combining their reasoning abilities with real-time data retrieval, ensuring that agents provide up-to-date and accurate information.
Agentic AI vs Generative AI: What’s the difference?
Artificial intelligence has evolved into distinct branches, each serving unique purposes in the technological landscape. Two pivotal branches are Generative AI and Agentic AI. Generative AI is renowned for its ability to create new content such as text, images, and videos based on learned patterns, while Agentic AI focuses on autonomous decision-making and task execution, enabling systems to operate independently with minimal human intervention. Understanding the differences between these two AI types is crucial for leveraging their strengths effectively across various applications. The following table highlights the key distinctions between Generative AI and Agentic AI in terms of their focus, operation style, adaptability, and primary functions.
Here’s a clearer comparison between the two:
| Details | Generative AI | Agentic AI |
|---|---|---|
| Focus | Content creation (text, images, videos) based on patterns | Decision-making and goal-driven actions |
| Operation Style | Reactive to prompts, generates outputs based on learned patterns | Proactive, autonomous decision-making and action |
| Adaptability | Limited to predefined patterns and training data | Highly adaptable through continuous learning and real-world feedback |
| Primary Function | Content generation (e.g., articles, images) | Autonomous task execution (e.g., logistics, robotics) |
| Learning Approach | Pattern recognition from training data | Dynamic learning from interactions and real-world feedback |
| Autonomy | Requires user input to function | Operates with minimal human intervention |
| Applications | Marketing, art, education, entertainment | Robotics, logistics, healthcare, autonomous systems |
| Examples | ChatGPT, DALL-E | AI-powered stock trading systems, autonomous vehicles |
Global Governance of Agentic AI: Standards and Best Practices
As organisations consider adopting Agentic AI, they must navigate the complexities of AI governance. Agentic AI's autonomous nature requires robust frameworks to ensure accountability, ethics, and compliance. The use of technical standards is crucial in this context, as it provides a structured approach to implementing effective AI governance. By adhering to these standards, organisations can ensure that their AI systems operate responsibly and securely, aligning with both regulatory requirements and organisational values.
Implementing Agentic AI effectively within an organisation demands adherence to several key technical standards that guide AI governance. These standards are essential for ensuring that AI systems operate with autonomy whilst maintaining accountability and ethical decision-making.
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ISO/IEC 42001 (AI Management System): This standard establishes governance frameworks for AI autonomy, enabling organisations to define protocols for oversight, accountability, and ethical decision-making. For instance, a healthcare company deploying AI-powered diagnostic tools can use ISO/IEC 42001 to ensure robust governance, ensuring that AI decisions are transparent and aligned with medical ethics.
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ISO/IEC 23894 (AI Risk Management): This standard guides risk mitigation for autonomous AI systems, ensuring transparency and ethical compliance. Financial institutions, for example, can use ISO/IEC 23894 to ensure that AI-driven credit scoring models are fair and compliant with regulatory requirements.
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NIST AI Risk Management Framework (AI RMF): This framework provides best practices for responsible autonomous AI deployment. An e-commerce platform implementing AI-driven recommendation engines can follow the NIST AI RMF to evaluate potential biases in customer profiling, ensuring fairness and compliance with consumer protection regulations.
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ISO/IEC 27001 (Information Security): This standard ensures secure and compliant data handling in autonomous AI systems. Banks deploying AI-driven fraud detection systems must comply with ISO/IEC 27001 to protect sensitive customer data from cyber threats, ensuring encrypted transactions and secure access controls.
By adopting these standards, organisations can ensure that their Agentic AI systems operate securely, ethically, and in compliance with regulatory requirements, fostering trust and confidence in AI-driven decision-making processes.
Ensuring Safe and Compliant AI Adoption with AIQURIS
While Agentic AI unlocks significant opportunities, it also brings governance, security, and compliance challenges. AIQURIS ensures its secure and compliant adoption by providing governance, risk assessment, and compliance solutions. Focusing on six key pillars—Safety, Security, Legal, Ethics, Performance, and Sustainability—AIQURIS offers structured risk profiling, AI maturity assessments, compliance automation, and real-time regulatory intelligence. This enables businesses to navigate evolving regulations, maintain transparency, and mitigate risks while deploying Agentic AI responsibly.
Conclusion
Understanding Agentic AI equips businesses with the knowledge necessary to leverage this transformative technology effectively. Recognising its unique features and applications will empower organisations to drive innovation, improve productivity, and maintain a competitive edge in their respective markets. With platforms like AIQURIS steering the way, businesses can confidently embrace the age of intelligent automation. To explore how AIQURIS can benefit your organisation, reach out today.
