THE AUTHOR
George Philip
Sr. Vice President (Data Analytics and Emerging Technologies)
What is Agentic AI?
The future of artificial intelligence lies in agentic AI, which combines advanced reasoning and iterative planning to address intricate, multi-step challenges autonomously. This innovation is poised to revolutionize productivity and operational efficiency across various sectors.
Agentic AI, powered by foundation models like large language models (LLMs), can reason, analyze, and adapt to complex workflows. Unlike LLMs alone, it gains decision-making and task execution abilities when augmented with complementary tools.
Key to its power is autonomy: Agentic AI can independently plan and execute tasks, leveraging reasoning tokens and chain-of-thought models to tackle complex challenges. It processes multimodal data—videos, images, text, and more—for deeper task understanding.
Agentic AI also uses tools and systems, including enterprise apps and the internet, to perform tasks efficiently. In multiagent environments, it collaborates with other AI agents to enhance task completion. By incorporating retrieval mechanisms and memory, agentic AI overcomes LLMs’ stateless nature, retaining context and learning from past experiences to improve performance and deliver better results over time.
Why Agentic AI?
By 2025, Deloitte projects 25% of enterprises using generative AI will adopt AI agents, growing to 50% by 2027. Gartner also highlights agentic AI as a key trend for 2025. By 2028, 15% of routine work decisions will be autonomously handled by agentic AI, up from almost none in 2024.
With over 1.25 billion knowledge workers globally, U.S. productivity growth has been slow—just 0.8% since 1987. AI agents are set to boost productivity significantly.
Unlike traditional systems like expert systems and RPA, which struggle with complexity, agentic AI powered by LLMs offers greater adaptability and can handle a wider range of tasks.
What Sets Agentic AI Apart from Generative AI
In contrast, agentic AI emphasizes goal-driven behavior and adaptive decision-making. It leverages technologies such as machine learning, natural language processing (NLP), and large language models (LLM) to achieve its objectives. While agentic AI vs generative AI highlights their unique strengths, agentic AI remains a developing technology not yet fully realized.
Agentic AI vs Generative AI: Core Differences
| Feature | Generative AI | Agentic AI |
|---|---|---|
|
Function
|
Creating content such as text, code, images, and more. |
Goal-driven actions and autonomous decision-making. |
|
Human Intervention |
Needs prompts and input. |
Requires little intervention after setup, operates autonomously while learning and adapting in real-time. |
|
Learning |
Driven by existing data. |
Adaptable through experiences. |
|
Use Case Examples |
Writing tools like ChatGPT for customer responses. |
In an intricate logistics process, overseeing resource management is crucial for efficiency. |
Various Agent Types in an Agentic AI Architecture
Agentic AI agents can be classified into four distinct types:
- Content Generation and Information Retrieval Agents: Agents designed to provide knowledge in less-regulated environments or topics.
- Advisory Knowledge Agent: Agents designed to provide knowledge in highly regulated environments or topics.
- Adaptive Workflow Agents: Agents responsible for executing tasks and taking actions.
- User Assistant Agents: User assistant agents provide direct support to individuals with their everyday tasks.
How Does Agentic AI Function?
Agentic AI follows a four-step approach to problem-solving:
- Perceive: AI agents gather and analyze data from various sources, focusing on key features and relevant entities.
- Reason: A large language model generates solutions and coordinates tasks, using techniques like RAG for accurate results.
- Act: Agentic AI integrates with tools via APIs to execute tasks, with safeguards for accuracy, like human approval for high-value claims.
- Learn: Through continuous feedback, agentic AI refines models using interaction-generated data, improving decision-making and operational performance over time.
Agentic AI: Real-world Use Cases
The possibilities for agentic AI examples are limitless, restricted only by innovation and skill. Whether it’s handling straightforward tasks like content creation and distribution or more intricate applications such as managing enterprise software, agentic AI examples are revolutionizing various industries.
Cybersecurity: A significant lack of human professionals as threat actors is driving up AI-based attacks. Emerging agentic cybersecurity systems can autonomously detect attacks and generate reports, improving system security and reducing the workload of human experts by up to 90 percent.
Agentic AI can also help software development teams detect vulnerabilities in new code. It can conduct tests and directly interact with developers to provide detailed solutions for fixing issues, a task currently performed manually by human engineers.
Regulatory Compliance: Companies across industries, including financial services and healthcare, are required to conduct periodic regulatory compliance reviews. Agentic AI can analyze regulations and corporate documents and quickly determine whether the company is compliant. The agent can cite specific regulations and proactively provide analysis and advice to human regulatory professionals.
Software Development: AI agents are enhancing developer efficiency by automating routine coding tasks. By 2030, it’s expected that AI could automate as much as 30 percent of work hours, allowing developers to dedicate more time to complex problems and foster innovation.
Content Development: Agentic AI can efficiently produce tailored, high-quality marketing content. Generative AI agents help marketers save up to three hours per piece, enabling them to focus on strategy and creativity. By optimizing content creation, you can boost customer engagement and maintain a competitive edge.
Customer Service: AI agents are improving customer support by improving self-service options and automating routine interactions. More than half of service professionals note substantial improvements in customer experiences, leading to quicker response times and higher satisfaction.
The rise of digital humans, AI-driven agents representing a company’s brand. It also helps sales teams by providing lifelike, real-time engagement to address customer inquiries or resolve issues during high call volumes.
Video Data Analysis: Organizations across industries are harnessing the power of video analytics AI agents to enhance workforce capabilities. These agents analyze vast volumes of live or archived video, using data analytics techniques to perform tasks like video search, summarization, and visual question-answering. With the ability to generate anomaly alerts, draft incident reports, and improve quality control through visual inspection, they are revolutionizing operational efficiency. Predictive maintenance is another key benefit, as these agents assist in monitoring equipment performance and preventing failures through analytics.
Healthcare: AI agents assist doctors by processing large volumes of medical as well as patient information, extracting essential data to improve decision-making. By automating administrative duties and recording clinical notes during patient visits, AI reduces time spent on these tasks, enabling doctors to prioritize patient relationships.
Additionally, AI agents offer round-the-clock support, providing details on medication usage, managing appointments, and sending reminders to help patients stay on track with their treatment plans.
Enablers for Agentic AI
Microsoft Copilot: With its Copilot Vision, autonomous AI capabilities come to life. The Copilot Studio empowers users to design tailored agents, which are anticipated to independently perform tasks across sales, service, finance, and supply chain operations.
OpenAI’s Operator: It extends OpenAI’s capabilities beyond text generation, enabling it to perform tasks like software development and travel bookings via a web interface. Available as a research preview, Operator aims to make AI agents versatile tools for daily use.
Nvidia’s Eureka Agent: It leverages GPT-4 to autonomously teach robots advanced skills. From performing pen-spinning tricks to manipulating scissors, Eureka exemplifies AI-driven learning. It introduces groundbreaking capabilities for robotics, simplifying the training of complex tasks.
Oracle’s Miracle Agent: Launching with Fusion Cloud updates in 2025 as a key addition to its suite of over 50 AI agents seamlessly integrated into the Fusion Cloud.
Salesforce Agentforce 2.0: Its full release is scheduled for February 2025, with some features available earlier. The platform gives ways to customize their agents, making it easy to adapt them to different roles and industries using tools like Flows, Prompts, Apex, and MuleSoft APIs.
Anthropic’s Claude 3.5: The latest version of Claude includes a “computer use” feature, enabling AI to perform human-like operations such as navigating software, clicking buttons, and browsing the web. This human-mimicking functionality opens avenues for automating repetitive desktop tasks. Enterprise access is expected in early 2025.
Google’s Vertex: Companies can use no-code tools to create agents for specific tasks, such as building marketing collateral based on previous marketing campaigns.
LangChain: It uses open-source technology to help companies construct multiagentic systems.
Why Korcomptenz?
As a Microsoft Certified Solution, AI Cloud & Fabric Partner, we specialize in AI, enterprise analytics, and modern data cloud solutions. Our “AI-First” approach helps businesses create measurable value and gain a competitive edge. By integrating AI copilots into business analytics, we scale self-service generative BI capabilities. With our specialization in Azure Analytics, advanced data modeling, expert design, and thorough system assessments, we ensure seamless execution and maintain data accuracy, reliability, and integrity throughout the project lifecycle.
Wrapping Up
Ready to transform your business? Agentic AI brings a new era of AI-human collaboration. These intelligent agents solve problems, make decisions, and continuously learn. By focusing on transparency, ethics, and human oversight, businesses can unlock productivity and innovation. Let Korcomptenz integrate AI into your systems—schedule a discussion today!
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