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With the release of ChatGPT and its API in late 2022, the technological world was shown the fascinating properties of AI and how it can multiply productivity for businesses. Since then, business applications and tools have increasingly used AI and its generative capabilities, but the further evolved next step is Agentic AI.
Agentic AI is emerging as a transformative technology because it is the first time AI is being used to form completely autonomous ‘intelligent’ minds. Agentic AI does not work like generative AI (Like ChatGPT), which takes commands and generates answers, rather, it can take independent action and decisions, and adapt to changing business conditions to do tasks autonomously.
Agentic AI refers to a type of artificial intelligence model that can make decisions, take actions, and plan its own course of action without the need for human guidance. It has an unparalleled ability to adapt to changing conditions in the pursuit of its goals and can perform complex sequences of activities.
While typical and current AI models use input prompts to generate answers and are limited by the ‘rules’ set for them by humans, Agentic AI can think for itself, take actions, make decisions, analyze problems, and do human-like work. Unlike traditional AI models, Agentic AI is not dependent on human prompts and can pursue its goal independently after being deployed.
For example, a customer service agentic AI can be made to pursue better customer satisfaction scores, and to achieve this, it can do human-like response-based communication, much like a digital human.
Agentic AI uses a complex array of tools such as Natural Language Processing (NLP), Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG).
Agentic AI first collects and perceives information from various sources such as business documents, digital interfaces, and sensors, then the LLM works on the data perceived and makes reasonings and analyses to arrive at a conclusion to act upon.
After making its decisions, Agentic AI can then execute the decisions through interfaces programmed into various business applications. Another great fact about Agentic AI is that it continuously uses its reasoning and analyses to learn and grow into a better decision-maker.
Agentic AI can integrate with enterprise data sources (with some of the best Agentic AI companies also providing compliance and security measures) to continuously update itself on the condition of the business and make decisions accordingly. This autonomous decision-making and self-directed learning is the biggest strength of Agentic AI.
Agentic AI can seriously transform many business functions- the possibilities are endless, but some early applications that have been discovered are in customer service, sales, manufacturing, supply chain, etc.
While we talk about the effects of Agentic AI on work, we have talk about Augmentation vs Automation-
Agentic AI is capable of automating business tasks, which means completely taking over the function and not needing any human input to carry it forward. These AI systems are designed to execute repetitive, predefined tasks at scale, delivering speed and efficiency without creative or contextual input from humans. Examples of Agentic AI automation include content generation, schedule management, etc.
Augmentation, on the other hand, refers to using the capabilities of Agentic AI to assist humans in doing their tasks, examples of which include AI-assisted medical diagnosis, AI-assisted plan designing, etc. This kind of usage greatly streamlines these functions and increases efficiency, without removing human supervision and creative input.
Agentic AI fundamentally changes how humans can use AI for businesses, making it so that AI tools are not only generators of ideas or content, but actual employees of the company working towards the fulfillment of goals.
Agentic AI is a huge leap in the prowess of AI technology. Apart from being autonomous and self-learning, Agentic AI tools have advanced capabilities for reasoning and making decisions, which is where their ‘human-like’ aspect shines.
Here are some of the features of Agentic AI applications:
Agentic AI workflows are designed to solve problems proactively, without any human interaction. Combined with their high-level problem-solving skills and continuous learning, it makes them excellent resources for handling business functions independently. For example, AI Sales Agents using Agentic AI technology can understand consumer emotions, respond in the correct tone and context, send the right assets, book meetings, and drive sales.
Another great thing about Agentic AI frameworks is that they are aware of situational changes and context- for example, if an Agentic AI sales agent encounters a rude and angry customer on a sales call, it can appropriately respond and/or transfer the call to a human agent for intervention.
This ability to perceive situational and environmental changes makes them more independent and suitable for autonomous tasks.
Agentic AI is designed to learn from every interaction and every new situation, making it improve its decision-making skills and reasoning. This adaptive learning ensures that Agentic AI agents become better over time at much faster than human learning and training standards. This means that businesses that deploy Agentic AI technologies can experience AI agents that grow accustomed to their particular needs.
As mentioned before, Agentic AI agents are not dependent on input prompts to decide their next steps or activities. They can make their own decisions based on their own reasoning regarding the best steps to take to reach the goals they are working towards.
For example, suppose an Agentic AI tool is working as a real estate agent. In that case, it can recommend properties based on the customers' needs on its own, and drive the interaction towards a property showing or deal. ContactSwing’s AI Real Estate Agents are full-serve AI agents that can independently drive sales.
Now that we have covered the basics of how Agentic AI works and what features it has, we can comprehensively understand some of the applications of Agentic AI technology for various business functions.
● Customer Support: Virtual AI assistants, like ContactSwing’s AI Customer Service Agents, can proactively respond to customer queries, offer personalized resolution, and escalate critical queries to human agents, while learning from each interaction and handling customer survive in multiple languages while working 24/7.
● Production: Agentic AI agents can also optimize and streamline production and supply chain processes, suggesting improvements by analyzing current metrics on their own. Similarly, they can monitor inventory and do restocking on their own too. They improve quality control processes, reduce energy consumption, enhance monitoring and prediction over quality issues, etc.
● Sales Service: Agentic AI agents can proactively assist sales by making sales calls and scheduling appointments, handling sales inquiries, and helping in lead management and prioritization. ContactSwing’s AI agents can conduct sales calls with personalization 24/7, learning and becoming better with each interaction.
● Health and Social Care: Agentic AI agents can reduce the load on healthcare workers by managing patient communication, gaining intelligent insights using patient data, handling prescription refills, scheduling appointments and managing schedules, etc.
Agentic AI offers a range of advantages that significantly enhance business operations and strategic decision-making:
● Higher Expertise:
Agentic AI systems possess deep application domain knowledge, allowing them to efficiently handle complex tasks that traditionally require human input. By analyzing vast amounts of data in real-time and integrating with data sources, these systems can make accurate decisions and provide actionable insights. This expertise ensures better problem-solving and improved outcomes in fields such as finance, healthcare, and manufacturing.
● Innovation:
The autonomous and adaptive capabilities of agentic AI have fueled the development of new products and services for businesses. Handling tasks independently, running operations 24/7, having intelligent predictive insights, etc. are the norm with the introduction of Agentic AI.
● More Trustworthy:
Transparency in decision-making processes and consistent performance are key benefits of agentic AI. They provide complete reports of their actions with no chance for error. These systems can explain the rationale behind decisions, fostering trust among stakeholders. Additionally, their unbiased approach and ability to consistently follow ethical guidelines contribute to a more trustworthy digital environment.
Of course, as with the case of any other AI tech, Agentic AI faces criticisms and challenges mostly focused on the notion of them ‘not being human’ and providing a ‘human touch’. After all, it is artificial intelligence which can never technically be human.
Here are some of the challenges that are faced by Agentic AI in the context of an industrial digital landscape:
● Ethics and Bias: Agentic AI systems may inadvertently perpetuate biases present in training data, leading to discriminatory outcomes. If their decision-making prowess is based on historical data, it has the potential to perpetuate pre-existing biases. For example, AI hiring agents may favour the majority racial demographic while hiring.
● Threats on Data Privacy and Security: With agentic AI relying heavily on vast datasets, integration with data sources, and sensors for perceiving data, securing sensitive information becomes a critical challenge. Data breaches or unauthorized AI access can lead to reputational damage and regulatory penalties, along with harming the wishes of people who do not want their data captured.
● Managing Human-AI Collaboration Effectively: AI agents being able to manage business functions and run 24/7 raises the concern of human workers losing their jobs to a technology they can’t compete with. It has also been brought up that human workers may lose their expertise due to an over-reliance on AI technology.
There are some clear directives that are to be implemented to get the most out of Agentic AI tools. Some of these are:
Aligning AI implementation with Specific, Measurable, Achievable, Relevant, and Time-bound (SMART) goals is essential for driving meaningful business outcomes.
Why it Matters: Clear objectives help organizations stay focused and allow the AI Agent to pursue clear outcomes.
Example: Instead of a vague goal like "improve customer experience," a SMART goal would be "reduce customer query resolution time by 20% within 6 months using an AI-powered chatbot."
Building a cross-functional AI adoption team ensures the seamless integration of agentic AI into business operations.
Why it Matters: Diverse teams bring unique perspectives and expertise, helping mitigate biases and navigating different concerns, ensuring successful implementation.
Sample Team Composition:
● Data scientists for model development
● Domain experts for contextual insights
● IT professionals for system integration
● Change management experts for adoption strategies
Establishing governance and control mechanisms ensures AI decision-making remains ethical, transparent, and aligned with company values.
Why it Matters: Prevents errors, maintains accountability, and fosters the consumers’ trust in AI systems.
Example: Implementing regular AI audits to monitor decision-making accuracy and fairness.
By setting SMART goals, assembling a strong team, and ensuring robust governance, organizations can unlock the full potential of agentic AI technology.
Agentic AI requires a large amount of data to analyze and form its thought patterns and model its workflows. To do this, both structured and unstructured data are used and can provide different benefits according to different applications.
Structured data, i.e., data from spreadsheets, databases, CRMs, and other integrated data sources, provides the necessary foundation for decision-making and predictive analytics for Agentic AI applications.
Unstructured data, i.e., data from customer interactions, emails, social signals, etc. is used to form the AI’s contextual understanding and give nuance to its decision-making. This is the data that makes Agentic AI applications capable of adapting to different scenarios,
The combination of structured and unstructured data makes Agentic AI technology capable of providing accurate predictive analytics about process optimizations, handling customer interactions without fault, and becoming an independent worker for businesses.
Here are some examples of Agentic AI being used by huge companies to improve their processes, making the resourcefulness of such systems evident:
● Healthcare (IBM Watson Health)
IBM Watson Health leverages agentic AI to assist doctors by analyzing vast datasets and recommending personalized treatment plans. It has significantly improved diagnostic accuracy and reduced time spent on research.
● Retail (Amazon)
Amazon uses agentic AI for dynamic inventory management and personalized product recommendations. The AI autonomously forecasts demand optimizes warehouse operations and enhances the customer experience with relevant suggestions.
● Finance (Betterment)
Betterment, a leading robo-advisor, employs agentic AI to autonomously manage investment portfolios. The AI system adjusts strategies based on market trends and client preferences, helping users achieve financial goals.
● Manufacturing (Siemens MindSphere)
Siemens uses agentic AI in its IoT platform, MindSphere, for predictive maintenance and process optimization. It autonomously monitors machine performance, predicts failures, and adjusts production parameters.
Agentic AI brings us closer to the next step of the long-awaited dream of humanity which is to have self-serving AI that can independently take care of business functions and run businesses autonomously and automatically.
Agentic shows promise as it is able to achieve this dream, although concerns still loom around the ethics and ability of agents being able to deliver truly human experiences. However, the truth is that the world will soon be using a lot more Agentic AI technology, just like the huge wave of AI products that were released following ChatGPT, and we have to be ready for it.
What are some real-world applications of AgenticAI?
Agentic AI is used in developing autonomous customer service agents, real estate agents, factory workers to optimize operations, patient care, health data analysis, etc.
How to build Agentic AI?
Building an Agentic AI system requires a lot of time, effort, and financial support. Large Language Models (LLMs) have to be built, trained with data, and tested. Self-learning mechanisms and feedback loops also have to be built.
What makes an AI agentic?
AI models that can independently take action, perform a sequence of actions, and learn and adapt to changing business conditions are known as Agentic AI, unlike traditional AI models which are dependent on human prompts.
Which companies are doing Agentic AI development?
Juna.ai makes factory worker AI Agents for process improvement in factories. ContactSwing.ai makes conversational AI agents for sales, real estate, customer service, and recruitment. Ema.ai also makes conversational AI employees.
What is an Agentic AI system?
An Agentic AI system is an AI model that is not dependent on human inputs and can independently pursue its goals, make its own decisions, and learn to adapt to different situations and contexts.
What are the applications of Agentic AI in Healthcare?
Agentic AI can be used to manage interactions with patients, analyze patient data and assist in diagnosis, manage prescription refills, manage hiring for travelling healthcare workers, etc. Visit ContactSwing’s Health Clinics page to learn more.
What are the top Agentic AI companies?
Leading companies in agentic AI include OpenAI (ChatGPT), DeepMind (AlphaGo), IBM Watson (healthcare AI), UiPath (RPA automation), C3 AI (predictive analytics), and NVIDIA (self-driving technology). These companies create AI solutions capable of autonomous decision-making, goal-setting, and learning from data to optimize processes across industries.
How does Agentic AI architecture work?
Agentic AI architecture involves data collection, model training, decision-making, goal management, and feedback loops. It uses machine learning and neural networks for dynamic problem-solving, adapts goals autonomously, and continuously improves through feedback. Ethical controls ensure transparency and compliance, making the AI intelligent, adaptive, and responsible.
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