Agentic AI
Carmen Beissner
CEO TALOOB ? N26, DeliveryHero, Autodoc alumna. CXO. Executive Coach
Agentic AI represents a significant evolution in artificial intelligence, moving beyond narrow task-specific applications to systems capable of autonomous decision-making, complex reasoning, and adaptive behavior. This new paradigm of AI is poised to revolutionize how businesses operate and how we interact with technology.
Agentic AI differs from traditional AI in several key ways when it comes to decision-making
1. Autonomy: Agentic AI can make decisions and take actions autonomously with minimal human oversight, while traditional AI typically requires more direct human guidance and intervention. Agentic AI can set its own goals and determine how to achieve them.
2. Contextual reasoning: Agentic AI has more advanced reasoning capabilities that allow it to make contextual decisions, weigh tradeoffs, and exercise judgment. Traditional AI is often limited to predefined decision trees or rules.
3. Adaptability: Agentic AI can dynamically adjust its goals, plans and decisions based on changing conditions and new information. Traditional AI tends to be more rigid in its decision-making processes.
4. Language understanding: Agentic AI leverages advanced natural language processing to comprehend nuanced instructions and context, enabling more sophisticated decision-making based on human language inputs.
5. Multi-step planning: Agentic AI can break down complex goals into subtasks and fluidly move between them to optimize workflows. Traditional AI is typically focused on individual, discrete tasks.
6. Proactivity: Agentic AI can proactively make decisions and take actions towards goals, rather than just reacting to specific inputs like traditional AI.
7. Broader context: Agentic AI aims to understand the wider context and implications of decisions, similar to human-like reasoning. Traditional AI often lacks this big picture view.
8. Continuous learning: Agentic AI can learn from outcomes and adapt its decision-making over time, while traditional AI often has a more static decision model.
In essence, agentic AI represents a shift towards more autonomous, flexible and contextually-aware decision-making that can handle complex, multi-step processes with less human intervention. This allows it to tackle more open-ended problems and workflows compared to the narrower, task-specific decision-making of traditional AI systems.
At its core, agentic AI refers to AI systems designed to pursue complex goals and workflows with minimal human supervision
Unlike conventional AI that is programmed for specific tasks, agentic AI can understand nuanced context, set its own goals, and make decisions based on changing conditions. This allows it to operate more like a human employee, comprehending natural language instructions and adapting its actions as needed
These features enable agentic AI to handle complex tasks across various domains, from data cleanup and software development to document processing and customer service
The emergence of agentic AI has been driven by several key innovations:
- Large language models (LLMs) that can understand and generate human-like text
- Increased computing power to train complex models
- Access to massive datasets for deep learning
- The ability to connect and interact with other systems
Compared to traditional AI approaches, agentic AI offers significant advantages. While custom software is limited to specific predefined tasks and robotic process automation (RPA) follows rigid scripts, agentic AI can learn, adapt, and handle unpredictable scenarios. This flexibility makes it particularly valuable for businesses dealing with dynamic environments and complex workflows.
The potential applications of agentic AI in the enterprise are vast. It could transform how businesses handle data management, software development, customer service, and more. For example, an agentic AI system could autonomously clean and organize large datasets, write and test code, or process and extract information from numerous documents.
Customer Service Transformation
One of the most impactful applications of agentic AI is in customer service. Traditional AI chatbots and automated systems often fall short because they are designed to follow scripted interactions, lacking the flexibility and depth to handle complex customer inquiries.
Agentic AI: Focusing on Resolving Customer Questions, Not Just Answering Them
领英推荐
These examples illustrate the diverse applications of agentic AI, showcasing its ability to enhance efficiency, adaptability, and decision-making across different sectors. As the technology continues to evolve, its integration into everyday workflows is expected to become even more pervasive, driving innovation and transforming industries.
Key differences and characteristics of agentic process automation, robotic process automation, and traditional AI
Citations:
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Yours, Carmen
CX Transformation Wizard | Tech Integration Guru | M&A Maestro Powering Customer Delight with AI & Innovation | Global Problem-Solver
3 个月Interesting read Carmen Beissner I like the clarification provided in this article. I've found that proper training of AI agents is paramount to their success, requiring a significant initial investment of time and resources. The payoff, however, comes in the form of consistent, scalable service that improves over time with continuous learning. Is very important to conduct frequent security audits and penetration tests on Agentic AI systems to identify and address potential vulnerabilities. This approach will help safeguard against potential exploits that could compromise data security or IP rights.
Optimizing logistics and transportation with a passion for excellence | Building Ecosystem for Logistics Industry | Analytics-driven Logistics
3 个月How can companies strike a balance between scripted AI chatbots and Agentic AI to provide efficient and personalized customer service?