#agentic AI

[weandthecolor]

Is Adobe’s Agentic AI What Your Creativity and Productivity Needs?

The conversation around AI is rapidly evolving, with a growing focus on Agentic AI—a technology that goes beyond following commands to understand context, take initiative, and solve complex problems. Rather than feeling like science fiction, it represents a shift toward AI as a collaborative partner.

Adobe, known for empowering creatives, is embracing Agentic AI with a clear vision: it's not about replacing human creativity but enhancing it. Like with their generative AI tool Firefly, Adobe sees Agentic AI as a co-pilot—freeing users from repetitive tasks so they can focus on strategic thinking, creativity, and collaboration. It’s about unlocking human potential, not replacing it.

More here.

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[govindhtech]

MediaTek Kompanio Ultra 910 for best Chromebook Performance

MediaTek Ultra 910

Maximising Chromebook Performance with Agentic AI

The MediaTek Kompanio Ultra redefines Chromebook Plus laptops with all-day battery life and the greatest Chromebooks ever. By automating procedures, optimising workflows, and allowing efficient, secure, and customised computing, agentic AI redefines on-device intelligence.

MediaTek Kompanio Ultra delivers unrivalled performance whether you're multitasking, generating content, playing raytraced games and streaming, or enjoying immersive entertainment.

Features of MediaTek Kompanio Ultra

An industry-leading all-big core architecture delivers flagship Chromebooks unmatched performance.

Arm Cortex-X925 with 3.62 GHz max.

Eight-core Cortex-X925, X4, and A720 processors

Single-threaded Arm Chromebooks with the best performance

Highest Power Efficiency

Large on-chip caches boost performance and power efficiency by storing more data near the CPU.

The fastest Chromebook memory: The powerful CPU, GPU, and NPU get more data rapidly with LPDDR5X-8533 memory support.

ChromeOS UX: We optimised speed to respond fast to switching applications during a virtual conference, following social media feeds, and making milliseconds count in in-game battle. Nowhere is better for you.

Because of its strong collaboration with Arm, MediaTek can provide the latest architectural developments to foreign markets first, and the MediaTek Kompanio Ultra processor delivers the latest Armv9.2 CPU advantage.

MediaTek's latest Armv9.2 architecture provides power efficiency, security, and faster computing.

Best in Class Power Efficiency: The Kompanio Ultra combines the 2nd generation TSMC 3nm technology with large on-chip caches and MediaTek's industry-leading power management to deliver better performance per milliwatt. The spectacular experiences of top Chromebooks are enhanced.

Best Lightweight and Thin Designs: MediaTek's brand partners can easily construct lightweight, thin, fanless, silent, and cool designs.

Leading NPU Performance: MediaTek's 8th-generation NPU gives the Kompanio Ultra an edge in industry-standard AI and generative AI benchmarks.

Prepared for AI agents

Superior on-device photo and video production

Maximum 50 TOPS AI results

ETHZ v6 leadership, Gen-AI models

CPU/GPU tasks are offloaded via NPU, speeding processing and saving energy.

Next-gen Generative AI technologies: MediaTek's investments in AI technologies and ecosystems ensure that Chromebooks running the MediaTek Kompanio Ultra provide the latest apps, services, and experiences.

Extended content support

Better LLM speculative speed help

Complete SLM+LLM AI model support

Assistance in several modes

11-core graphics processing unit: Arm's 5th-generation G925 GPU, used by the powerful 11-core graphics engine, improves traditional and raytraced graphics performance while using less power, producing better visual effects, and maintaining peak gameplay speeds longer.

The G925 GPU matches desktop PC-grade raytracing with increased opacity micromaps (OMM) to increase scene depths with subtle layering effects.

OMM-supported games' benefits:

Reduced geometry rendering

Visual enhancements without increasing model complexity

Natural-looking feathers, hair, and plants

4K Displays & Dedicated Audio: Multiple displays focus attention and streamline procedures, increasing efficiency. Task-specific displays simplify multitasking and reduce clutter. With support for up to three 4K monitors (internal and external), professionals have huge screen space for difficult tasks, while gamers and content makers have extra windows for chat, streaming, and real-time interactions.

DP MST supports two 4K external screens.

Custom processing optimises power use and improves audio quality. Low-power standby detects wake-up keywords, improving voice assistant response. This performance-energy efficiency balance improves smart device battery life, audio quality, and functionality.

Hi-Fi Audio DSP for low-power standby and sound effects

Support for up to Wi-Fi 7 and Bluetooth 6.0 provides extreme wireless speeds and signal range for the most efficient anyplace computing.

Wi-Fi 7 can reach 7.3Gbps.

Two-engine Bluetooth 6.0

[datapeakbyfactr]

AI’s Role in Business Process Automation

Automation has come a long way from simply replacing manual tasks with machines. With AI stepping into the scene, business process automation is no longer just about cutting costs or speeding up workflows—it’s about making smarter, more adaptive decisions that continuously evolve. AI isn't just doing what we tell it; it’s learning, predicting, and innovating in ways that redefine how businesses operate. 

From hyperautomation to AI-powered chatbots and intelligent document processing, the world of automation is rapidly expanding. But what does the future hold?

What is Business Process Automation? 

Business Process Automation (BPA) refers to the use of technology to streamline and automate repetitive, rule-based tasks within an organization. The goal is to improve efficiency, reduce errors, cut costs, and free up human workers for higher-value activities. BPA covers a wide range of functions, from automating simple data entry tasks to orchestrating complex workflows across multiple departments. 

Traditional BPA solutions rely on predefined rules and scripts to automate tasks such as invoicing, payroll processing, customer service inquiries, and supply chain management. However, as businesses deal with increasing amounts of data and more complex decision-making requirements, AI is playing an increasingly critical role in enhancing BPA capabilities. 

AI’s Role in Business Process Automation 

AI is revolutionizing business process automation by introducing cognitive capabilities that allow systems to learn, adapt, and make intelligent decisions. Unlike traditional automation, which follows a strict set of rules, AI-driven BPA leverages machine learning, natural language processing (NLP), and computer vision to understand patterns, process unstructured data, and provide predictive insights. 

Here are some of the key ways AI is enhancing BPA: 

Self-Learning Systems: AI-powered BPA can analyze past workflows and optimize them dynamically without human intervention. 

Advanced Data Processing: AI-driven tools can extract information from documents, emails, and customer interactions, enabling businesses to process data faster and more accurately. 

Predictive Analytics: AI helps businesses forecast trends, detect anomalies, and make proactive decisions based on real-time insights. 

Enhanced Customer Interactions: AI-powered chatbots and virtual assistants provide 24/7 support, improving customer service efficiency and satisfaction. 

Automation of Complex Workflows: AI enables the automation of multi-step, decision-heavy processes, such as fraud detection, regulatory compliance, and personalized marketing campaigns. 

As organizations seek more efficient ways to handle increasing data volumes and complex processes, AI-driven BPA is becoming a strategic priority. The ability of AI to analyze patterns, predict outcomes, and make intelligent decisions is transforming industries such as finance, healthcare, retail, and manufacturing. 

“At the leading edge of automation, AI transforms routine workflows into smart, adaptive systems that think ahead. It’s not about merely accelerating tasks—it’s about creating an evolving framework that continuously optimizes operations for future challenges.”

— Emma Reynolds, CTO of QuantumOps

Trends in AI-Driven Business Process Automation 

1. Hyperautomation 

Hyperautomation, a term coined by Gartner, refers to the combination of AI, robotic process automation (RPA), and other advanced technologies to automate as many business processes as possible. By leveraging AI-powered bots and predictive analytics, companies can automate end-to-end processes, reducing operational costs and improving decision-making. 

Hyperautomation enables organizations to move beyond simple task automation to more complex workflows, incorporating AI-driven insights to optimize efficiency continuously. This trend is expected to accelerate as businesses adopt AI-first strategies to stay competitive. 

2. AI-Powered Chatbots and Virtual Assistants 

Chatbots and virtual assistants are becoming increasingly sophisticated, enabling seamless interactions with customers and employees. AI-driven conversational interfaces are revolutionizing customer service, HR operations, and IT support by providing real-time assistance, answering queries, and resolving issues without human intervention. 

The integration of AI with natural language processing (NLP) and sentiment analysis allows chatbots to understand context, emotions, and intent, providing more personalized responses. Future advancements in AI will enhance their capabilities, making them more intuitive and capable of handling complex tasks. 

3. Process Mining and AI-Driven Insights 

Process mining leverages AI to analyze business workflows, identify bottlenecks, and suggest improvements. By collecting data from enterprise systems, AI can provide actionable insights into process inefficiencies, allowing companies to optimize operations dynamically. 

AI-powered process mining tools help businesses understand workflow deviations, uncover hidden inefficiencies, and implement data-driven solutions. This trend is expected to grow as organizations seek more visibility and control over their automated processes. 

4. AI and Predictive Analytics for Decision-Making 

AI-driven predictive analytics plays a crucial role in business process automation by forecasting trends, detecting anomalies, and making data-backed decisions. Companies are increasingly using AI to analyze customer behaviour, market trends, and operational risks, enabling them to make proactive decisions. 

For example, in supply chain management, AI can predict demand fluctuations, optimize inventory levels, and prevent disruptions. In finance, AI-powered fraud detection systems analyze transaction patterns in real-time to prevent fraudulent activities. The future of BPA will heavily rely on AI-driven predictive capabilities to drive smarter business decisions. 

5. AI-Enabled Document Processing and Intelligent OCR 

Document-heavy industries such as legal, healthcare, and banking are benefiting from AI-powered Optical Character Recognition (OCR) and document processing solutions. AI can extract, classify, and process unstructured data from invoices, contracts, and forms, reducing manual effort and improving accuracy. 

Intelligent document processing (IDP) combines AI, machine learning, and NLP to understand the context of documents, automate data entry, and integrate with existing enterprise systems. As AI models continue to improve, document processing automation will become more accurate and efficient. 

Going Beyond Automation

The future of AI-driven BPA will go beyond automation—it will redefine how businesses function at their core. Here are some key predictions for the next decade: 

Autonomous Decision-Making: AI systems will move beyond assisting human decisions to making autonomous decisions in areas such as finance, supply chain logistics, and healthcare management. 

AI-Driven Creativity: AI will not just automate processes but also assist in creative and strategic business decisions, helping companies design products, create marketing strategies, and personalize customer experiences. 

Human-AI Collaboration: AI will become an integral part of the workforce, working alongside employees as an intelligent assistant, boosting productivity and innovation. 

Decentralized AI Systems: AI will become more distributed, with businesses using edge AI and blockchain-based automation to improve security, efficiency, and transparency in operations. 

Industry-Specific AI Solutions: We will see more tailored AI automation solutions designed for specific industries, such as AI-driven legal research tools, medical diagnostics automation, and AI-powered financial advisory services. 

AI is no longer a futuristic concept—it’s here, and it’s already transforming the way businesses operate. What’s exciting is that we’re still just scratching the surface. As AI continues to evolve, businesses will find new ways to automate, innovate, and create efficiencies that we can’t yet fully imagine. 

But while AI is streamlining processes and making work more efficient, it’s also reshaping what it means to be human in the workplace. As automation takes over repetitive tasks, employees will have more opportunities to focus on creativity, strategy, and problem-solving. The future of AI in business process automation isn’t just about doing things faster—it’s about rethinking how we work all together.

Learn more about DataPeak:

[peterbordes]

Nvidia’s Jensen Huang says AI agents are ‘a multi-trillion-dollar opportunity’ at last nights CES AI keynote. Let that “multi Trillion” sink in, because this global movement is just getting started.

[procurement-insights]

What do you believe is AI's role in procurement and supply chain, and what is the procurement professional's role using AI?

[oraczenai0]

Agentic AI: The Next Evolution of Enterprise AI Solutions

The Rise of Agentic AI in Enterprises

As artificial intelligence continues to evolve, enterprises are shifting from traditional AI models to more advanced, autonomous, and adaptable AI agents. This shift is driven by the need for AI solutions that not only process data but also make intelligent decisions, self-improve, and seamlessly integrate into business operations. This is where Agentic AI comes in.

Agentic AI represents a new generation of AI systems that operate with autonomy, contextual awareness, and continuous learning capabilities. Unlike conventional AI models that rely on predefined rules, Agentic AI can adapt, learn from interactions, and optimize workflows dynamically.

Oraczen AI: Leading the Agentic AI Revolution

At Oraczen AI, we specialize in building custom AI solutions tailored for enterprise needs. Our Zen Platform is designed to harness the power of Agentic AI, offering businesses an end-to-end AI management solution that ensures efficiency, security, and seamless integration.

Key Features of Oraczen’s Zen Platform

The Zen Platform empowers enterprises by providing a suite of powerful AI tools:

Datazen – Integrates enterprise knowledge into AI models, improving output accuracy and relevance.

Memoryzen – Enables continuous learning, allowing AI agents to adapt to changing environments.

Agentzen – Customizes AI agents to align with specific business processes and goals.

Operatezen – Provides observability into AI agent performance for optimization and troubleshooting.

Securezen – Ensures enterprise-grade security and compliance in AI deployments.

Configzen – Simplifies AI agent configuration for quick and efficient implementation.

These features collectively allow enterprises to create intelligent, adaptable AI solutions that enhance productivity, automate workflows, and drive data-driven decision-making.

Real-World Applications of Agentic AI

Oraczen’s Zen Platform is already being leveraged across multiple industries, demonstrating the power of Agentic AI in real-world scenarios:

Finance: Automating invoice processing and financial analysis to reduce errors and improve efficiency.

Procurement: AI-driven spend analysis to optimize procurement costs and vendor management.

Consulting: Automating business intelligence reports through AI-driven data visualization.

Healthcare: Enhancing compliance checks and data processing for improved patient care and operational efficiency.

Manufacturing & Government: Streamlining regulatory compliance and optimizing production workflows.

The Future of Enterprise AI with Oraczen

The Agentic AI revolution is here, and enterprises that embrace it will gain a competitive edge. With Oraczen’s Zen Platform, businesses can deploy AI agents that are not only intelligent but also adaptable, secure, and fully customizable.

By integrating Agentic AI, enterprises can transform their operations, drive innovation, and future-proof their business processes. Join the AI evolution with Oraczen AI and experience the power of custom, enterprise-ready AI solutions.

For more details, visit Oraczen Ai and explore how the Zen Platform can elevate your AI strategy.

[blogchaindeveloper]

Agentic AI vs Generative AI: Key Differences You Need to Know

Artificial Intelligence (AI) has made significant strides in recent years, with two prominent branches emerging: Agentic AI and Generative AI. While both are subsets of AI, they serve distinct purposes and operate differently. This article delves into the key differences between Agentic AI and Generative AI, their applications, and their implications for professionals seeking certifications like the Certified Agentic AI Expert™, Certified Agentic AI Developer™, or those pursuing an AI Course, Gen AI Course, or Blockchain Certification.​

What is Agentic AI?

Agentic AI is an AI system that autonomously makes decisions and performs tasks to achieve specific goals without human intervention. These systems utilize natural language processing, machine learning, and reinforcement learning technologies to analyze data, plan actions, and execute tasks. Unlike traditional AI, which responds to inputs, Agentic AI proactively takes actions to accomplish objectives.​

Applications of Agentic AI:

Autonomous Vehicles: Self-driving cars that navigate and make decisions in real time.

Robotic Process Automation (RPA): AI agents handling repetitive tasks in business processes.

Healthcare Diagnostics: AI systems analyzing medical data to provide diagnostic recommendations.

Supply Chain Management: AI agents optimizing inventory and logistics operations.​

Professionals aiming for the Certified Agentic AI Expert™ or Certified Agentic AI Developer™ certifications will gain expertise in developing and deploying such autonomous systems.​

What is Generative AI?

Generative AI, or Gen AI, encompasses AI systems that create new content—text, images, music, or code—based on patterns learned from existing data. These systems, including large language models (LLMs) like GPT, are trained on vast datasets to generate human-like responses or creative outputs.​

Applications of Generative AI:

Content Creation: Generating articles, poetry, or marketing copy.

Image Generation: Creating visuals from textual descriptions (e.g., DALL·E).

Code Assistance: Providing code suggestions or writing snippets (e.g., GitHub Copilot).

Music Composition: Composing original music tracks.

For those pursuing a Gen AI Course, this area offers insights into building and fine-tuning generative models.​

Key Differences Between Agentic AI and Generative AI

FeatureAgentic AIGenerative AI

Primary Function: Autonomously perform tasks to achieve goals Create new content based on learned patterns.

Decision-Making Proactive and goal-oriented Reactive to prompts or inputs

Learning Approach Reinforcement learning, iterative planning Pattern recognition from large datasets

Human Interaction Minimal once deployed Requires continuous interaction for guidance

Examples Autonomous vehicles, RPA systems , Chatbots, image generators, code assistants

Educational Pathways and Certifications

To excel in these fields, professionals can pursue various educational programs:​

AI Course: Provides foundational knowledge in AI principles and techniques.

Gen AI Course: Focuses on the development and application of generative models.

Blockchain Certification: Offers insights into integrating AI with blockchain technologies for secure and transparent systems.​

These courses equip individuals with the skills to design, implement, and manage Agentic and Generative AI systems across various industries.​

Real-World Implications and Future Outlook

The integration of Agentic and Generative AI into various sectors is transforming industries:​

Healthcare: AI systems assist in diagnostics and personalized treatment plans.

Finance: AI agents automate trading and risk assessment processes.

Entertainment: Generative AI creates content, enhancing user experiences.

Education: AI-driven platforms provide personalized learning pathways.​

The distinction between Agentic and Generative AI will become increasingly significant as AI evolves. Understanding these differences is crucial for professionals aiming to leverage AI technologies effectively.​

Conclusion

While Agentic and Generative AI are pivotal in the AI landscape, they serve distinct purposes. Agentic AI focuses on autonomous decision-making and task execution, whereas Generative AI emphasizes content creation. By pursuing relevant certifications and courses, professionals can gain the expertise to navigate and innovate within these domains, driving industry advancements.​

[spinnrblog]

Vengo AI boosted our trial signups by 86%—and we didn’t lift a finger. It’s like adding a product expert to your homepage 24/7.

Get started: VengoAI.com

[vengoai]

Vengo AI boosted our trial signups by 86%—and we didn’t lift a finger. It’s like adding a product expert to your homepage 24/7.

Get started: VengoAI.com

[nicksblogsworld]

Looking for the best Agentic AI vendors in 2025? This detailed guide features 10 standout companies pushing the boundaries of AI automation, autonomy, and decision-making. Whether you're exploring AI agent development or considering vendor partnerships, this list highlights the most innovative players in the space—perfect for businesses ready to scale with Agentic AI.

[jcmarchi]

DeepSeek's AI breakthrough: Fewer resources, big impact

New Post has been published on https://thedigitalinsider.com/deepseeks-ai-breakthrough-fewer-resources-big-impact/

DeepSeek's AI breakthrough: Fewer resources, big impact

On December 26th, a modest-sized Chinese company named DeepSeek introduced advanced AI technology, rivaling the top chatbot systems from giants like OpenAI and Google.

This achievement was noteworthy for its capability and the cost-efficiency with which it was developed. Unlike its large competitors, DeepSeek created its artificial intelligence, DeepSeek-V3, using significantly fewer specialized processors, which are typically essential for such advancements.

Cost efficiency and technological breakthrough

These processors are at the heart of a fierce tech rivalry between the U.S. and China. The U.S. aims to keep its lead in AI by restricting the export of high-end chips, such as those from Nvidia, to China.

However, DeepSeek’s success with fewer resources raises concerns about the effectiveness of U.S. trade policies, which have inadvertently spurred Chinese innovation using more accessible technologies.

DeepSeek-V3 impressively handles tasks like answering queries, solving puzzles, programming, and matching industry standards. Remarkably, it was developed with just around $6 million worth of computing resources, starkly contrasting the $100 million Meta reportedly invested in similar technologies.

Chris V. Nicholson from Page One Ventures pointed out that more companies could afford $6 million than the heftier sums, democratizing access to advanced AI technology.

Strategic implications and global impact of DeepSeek

Previously, experts believed only firms with substantial financial resources could compete with leading AI firms, which train their systems on supercomputers requiring thousands of chips.

DeepSeek, however, managed with just 2,000 chips from Nvidia. This efficient use of limited resources reflects the forced innovation resulting from chip restrictions in China, as Jeffrey Ding from George Washington University noted.

Recently, the U.S. tightened these restrictions to prevent China from acquiring advanced AI chips via third countries. This is part of ongoing efforts to limit Chinese firms’ potential military use of these technologies, which have resorted to stockpiling chips and sourcing them through underground markets.

ChatGPT vs Bard: What are the top key differences?

We’re taking a look at Bard vs ChatGPT and their key differences like technology, internet connection, and training data.

DeepSeek, a company rooted in quantitative stock trading, has been leveraging its profits to invest in Nvidia chips since 2021, fueling its AI research rather than consumer products. This focus has allowed it to bypass stringent Chinese regulations on consumer AI, attracting top talent and exploring diverse applications from poetry to complex examinations.

While leading U.S. firms continue to push AI boundaries, DeepSeek’s recent achievements underline its growing prowess in the field. It also highlights the broader shift towards open-source AI, gaining traction as companies like Meta openly share their technologies. This shift increasingly positions China as a central player in AI development, posing a strategic challenge to U.S. dominance in the field.

As the debate continues over the potential risks of open sourcing AI in the U.S., such as spreading misinformation, the global open source community, increasingly led by China, might shape the future of AI development, suggesting a significant geopolitical shift in the technology landscape.

Have you seen our 2025 event calendar?

From agentic AI to LLMOps, this year will be bigger than ever – join us in one of our 19 in-person events across the globe and network with other AI leaders.

Like what you see? Then check out tonnes more.

From exclusive content by industry experts and an ever-increasing bank of real world use cases, to 80+ deep-dive summit presentations, our membership plans are packed with awesome AI resources.

Subscribe now

[newtonaitech]

Agentic AI: The Future of Autonomous Decision-Making

Over the years, the artificial intelligence field has revolutionized, giving birth to Agentic AI, a form of autonomous decision-making. Whereas conventional AI models were created to require human intervention in processing AI model interactions and decision-making, Agentic AI alternatively manages to perform such complex things with very little human oversight.

With the rise of automation and the professed meaning of efficiency gearing itself up in industries, the journey to Agentic AI requires a proper understanding. The present paper henceforth would be diversified towards understanding the changes that Agentic AI has sharply brought within the techno-realm, instances with respect to its application, the advantages it does present, the challenges which it is posing, and the possible future that the system bears in store. 

What is Agentic AI?

Agentic AI is thus an AI system designed such that it can act autonomously, make decisions for itself, and work towards its goals without human instruction every step of the way. Yet, unlike traditional AI, which takes in instructions and reacts to them, Agentic AI functions almost always in proactive mode-that is, it observes and evaluates its environment, takes decisions on its own, and alters behavior in real-time to further the designated objective. Therefore, Agentic AI is a step forward in AI because it has high-level autonomy and learns from its actions, adapting to changing situations.

Key Characteristics of Agentic AI:

1. Autonomy

Agentic AI will also function independently of being continuously supervised by humans. It will execute tasks, make decisions, and change its approach based on the data it obtains and act independently in dynamic environments.

2. Goal-Oriented Behaviour      

These AI systems are designed to achieve some goals and can plan and act toward predetermined objectives-whether goals are long-term or short-term. The system evaluates different courses of action to maximize its chances of success.

3. Decision-Making and Reasoning

An agentic AI system helps itself in making decisions by analyzing the available information, weighing up various options, and predicting different combinations of events. The decision-making process often involves complex algorithms that allow it to reason and act like humans do.

4. Learning and Adaptability

Agentic AI is one of the features that will be the main feature of learning by experience. These systems progressively improved their performance toward the goal. These systems improve their movement by reinforcement learning, based on the results they return.

5. Interaction with the Environment

Most of these AI systems interact with their physical or digital worlds. They input data or sensory data and use that to make decisions and act.

6. Real-world Applications

Applications of Agentic AI are a colorful variety of examples: from self-driving cars where such AI systems enable cars to drive on the roads, avoiding obstacles and making decisions while driving; to trading in finance, where algorithms trade stocks depending on the market by buying and selling automatically; and even warehouse robots that can do the same but for task operations.

The Role of Agentic Artificial Intelligence in Various Industries

Agentic AI is changing the face of many industries by fostering autonomy in decision-making, promoting efficiency, and providing unprecedented solutions. With its ability to understand large data, enabling itself to be the architect of its environment, and to make independent decisions, Agentic AI is revolutionizing everything from healthcare to finance. Some of the industries that employ Agentic AI most importantly are as follows:

1. Healthcare

Agentic AI is greatly influencing healthcare by improving processes like diagnosis, treatment planning, and patient management. The AI system can act autonomously to interrogate medical data available in X-rays, MRIs, etc., and patient histories to detect conditions and propose treatment options. Cancer or heart conditions may appear in AI tools early, being difficult to spot for human physicians.

In addition, AI robots help out with surgical procedures, with high precision of movement and reduced human error. Lastly, personalized therapy is done through AI systems that adapt depending on patient responses, thus optimizing results for each individual.

2. Finance

In finance, Agentic AI has been set up to automate trading, risk assessment, and fraud detection. Algorithms analyze market data, predict trends, and make high-frequency trading decisions at a speed far beyond any human trader. As it feeds on endless stashes of financial data, AI systems are constantly improving through experience, honing their ability to identify profit-making opportunities and ward off risk.

In addition, AI is helping to stop fraud. It analyzes transaction patterns, autonomously flagging anomalous activities that could be potential financial crimes and proactively preventing them without human intervention.

3. Transportation

The world is changing in such a way that the transportation industry will continue to experience major shifts due to the inevitable emergence of autonomous vehicles. The role of agentic AI is very crucial in developing self-driving modes of transportation including cars, trucks, and drones. These types of artificial intelligence are capable of processing real-time data from sensors, cameras, and GPS and making decisions about navigation, avoiding obstacles, and optimizing routes while on the go. One of the best examples would be how self-driving cars can autonomously decide when to speed up or down, change lanes, or stop to make sure everything's safe and efficient on the road.

Alternatively, with drones for delivery, the vehicles are designed to fly autonomously from one point to another, taking steps to avoid obstacles and subsequently updating their routes according to some atmospheric concerns.

4. Manufacturing

Vehicles will have AI enhancements that will cover automation, supply chain management, and quality integration, which are important within the manufacturing industry. Robots now form an important part of assembly lines, carrying out tedious repetitive tasks at an amazing level of precision and consistency. With the ability to monitor their process, these robots can identify flaws in the product and instantly alter the parameters allowing quality improvement.

Along with a search engine, AI will predict maintenance through the analysis of data from machines and equipment and will replace detection with predicting failure before it occurs. It reduces the amount of downtime and the cost of maintenance, allowing the continuous smooth production process.

5. Customer Service

AI-enabled chat-bots and virtual assistants have fundamentally changed the universe of customer service. These systems can autonomously interact with customers, answer questions, process requests, and provide support 24/7. With every encounter, Agentic AI learns to perform better, offering sharper answers and personalization of the customer experience.

Benefits of Agentic AI in Decision-Making

In decision-making, Agentic AI enables transformative benefits across sectors. Data-driven autonomous decisions by Agentic AI led to improved efficiency, accuracy, and scaling options. Below are the contributions of Agentic AI to decision-making:

1. Enhanced Speed and Efficiency

The distinguishing value that real-time processing of massive data grants Agentic AI is the speed of decision-making. It is quite unlike humans who can take a lot of time even to analyze a little complex data. AI systems are capable of almost real-time assessment of thousands of variables to provide the best possible decision. This helps when decisions must quickly be ratified, such as the cases with financial trading or emergency response scenarios.

In stock trading, for instance, AI systems can analyze the market activity, bordering on news and social media, to maximize profit opportunities in the time frame of milliseconds when a buy or sell decision is made opportunity a human trader may miss since he cannot respond similarly fast.

2. Data-Driven Accuracy

Agentic AI derives value from extensive data analysis while minimizing human errors. The ordinary human thinks his way, or at the very least intuitively, into and out of varying degrees of data sets and falls prey to his biases or oversights. AI could, however, avail itself of high-volume, highly varied data points and draw inferences that would not strike anybody at first.

For instance, within healthcare, AI can diagnose diseases based on medical images, genetic information, and patient history with accuracy often exceeding that of a human doctor. Thus, with decision-making largely depending on data, it also positions itself to facilitate reliable and precise decision-making, which is capable of improving outcomes and lowering misdiagnoses.

3. Objective and Unbiased Decisions

Decision-making can be laden with human emotions, cognitive biases, and preconceived ideas. Agentic AI, however, applies algorithms or models to build decisions free of such biases. With the assistance of Agentic AI, the result can be more objective, fair, and impartial-especially in fields where fairness is critical, like employment, loan approval, and legal judgments.

In HR management, AI can analyze resumes and assess candidates with objectivity, analyzing solely skills and qualifications, independent of any unconscious bias, be it gender, age, or ethnicity.

4. Continuous Learning and Improvement

Among the unique features that characterize most Agentic AI systems is that they learn from their decisions. Reinforcement learning is commonly used to govern AI outcomes that change over time in response to environmental feedback or new data. These changes in the learning paradigm ensure that increasingly good decisions are made in light of feedback, refining the decision-making abilities of the AI to changing conditions.

In marketing, for instance, AI permits analysis of customer behaviour, the effectiveness of campaigns, and better-optimized strategies for engagement and conversion that keep changing with customer preferences.

5. Scalability and Consistency

Another major advantage of AI is its ability to deal with very large-scale datasets and to make uniform decisions over time. Human decision-making can be inconsistent at times or bias-driven under pressure; AI systems, however, produce reliable and consistent results in large volumes. This works well for the customer service industry, where AI chatbots guarantee reliability and standardization by providing consistent responses to thousands of interactions.

The Era of Agentic AI & More: GenAI Predictions For 2025

​Such agentic AI is growing very fast; all this is an adoption of agentic AI across industries. Here are a few statistics showcasing this growth:

Market Size and Growth: The global Agentic AI market is expected to grow from about USD 5.2 billion in 2024 to nearly USD 196.6 billion by the end of 2034, registering a compound annual growth rate (CAGR) of 43.8% during such period.

Enterprise Integration: By the end of 2028, agentic AI is expected to become a part of 33% of all enterprise software applications up from less than 1% in 2024.

Regional Insights: North America formed the leading region in the Agentic AI market, consisting of above 38% share in revenue of USD 1.97 billion in 2024 due to the U.S. market, which alone was valued at USD 1.58 billion, expanding at a CAGR of 43.6%.

Industry Applications: Agentic AI is changing sectors like health care, finance, and customer service, among others. A retailer reported a 15% increase in sales through the use of AI personalization, indicating the huge economic value in creating consumer experiences that are more tailored.

Economic Impact: Generative AI, a segment of agentic AI, is likely to create a very rich economy, which projected estimates might say would contribute between 2.6 and 4.4 trillion dollars a year to global GDP by 2030 across all relevant sectors.

Challenges & Ethical Concerns of Agentic Artificial Intelligence

With Agentic AIs penetrating deep into various industries, it brings awesome promises, great challenges, and ethical concerns that need careful handling during the responsible deployment of AI systems that operate using autonomy. Below are some salient challenges and ethical issues around Agentic AI.

1. Accountability and Responsibility

The foremost concern in Agentic AI revolves around issues of responsibility if things go bad. Since agents work autonomously, making a decision without human intervention, it is difficult to hold anyone responsible in the event of a mistake or an adverse outcome. For instance, in a case where an autonomous vehicle is involved in a mishap, it becomes unclear whether to attribute liability to the developers, the user, or the AI itself.

2. Bias and Fairness

AI systems are trained on large datasets that often contain biases stemming from human decisions or historical inequalities. Hence, should Agentic AI systems inherit these biases, they could perpetuate discrimination in hiring practices, law enforcement, or lending. For example, a hiring algorithm could discriminate against certain demographic groups over others due to biased training data, thus leading to unfair decisions. Ensuring that AI systems are unbiased and fair is a huge task.

3. Transparency and Explainability

Agentic AI systems usually employ 'black-box' type complex machine learning models, deep learning, for their operation- into which decision-making processes are opaque. The absence of explainability can prove problematic, especially in high-stakes areas like healthcare or the judicial decision-making process, where understanding AI decision-making across different attributes is crucial if the system is to be trusted and held accountable. The challenge lies in enabling AI power with interpretability so that stakeholders can trust and verify the decision-making process.

4. Privacy Concerns

Many Agentic AI systems require access to vast amounts of personal and sensitive data to function effectively. For instance, in healthcare, AI may analyze patient records to suggest treatments, while in finance, it may use financial data for decision-making. This raises significant privacy concerns, particularly regarding how this data is collected, stored, and used. There’s a need for stringent data protection policies to ensure users’ privacy rights are respected.

5. Security Risks

Access to a very large amount of private and sensitive data is required for most Agentic AI to function well. For example, in healthcare, the AI may analyze a patient's record to suggest treatments. In finance, it uses financial data to make decisions. These all-pose privacy risks in data collection, storage, and use. There is a need for robust data protection policies to guarantee the respect and rights of users towards privacy.

6. Job Displacement

The more autonomous Agentic AI will become, more likely it will be manipulated by malicious actors. Hackers will be able to use the AI systems to cause physical and emotional harm and alter its decision-making while using these for exploitation of weaknesses. For instance, the AI model using simple adversarial attacks can easily be tricked into making a wrong decision, directing an autonomous vehicle away from its course, or producing fraudulent financial transactions. The robustness and security of such systems are currently a major concern.

7. Ethical Decision-Making

AI systems, particularly those which perform sensitive public functions, such as health care or autonomous warfare, may increasingly find themselves in situations where they are expected to act ethically. For instance, an autonomous vehicle will have to assess whether or not it takes into account the life of its passenger or those of pedestrians in case of a collision. It remains a research issue how to program AI ethical decisions that match with human values since ethical frameworks are often dependent on culture and circumstance.

Frequently Asked Questions (FAQ) - Agentic AI

1. What is Agentic AI?

Agentic AI centers on autonomous systems making decisions and taking actions according to their environment, data, and predetermined goals, free from further human intervention. Such systems assess situations, reason about them, and adapt to the new circumstances necessary to attain their goals completely independently.

2. How does Agentic AI differ from traditional AI?

In contradistinction to classical AI, which is either command-based or operates under rule-based stipulations, Agentic AI involves decision-making based on proactive measures. Rather than a reactive system, it actively pursues goals by learning from experience and adjusting its actions, thereby facilitating dynamic adaptation through autonomy.

3. What are some common applications of Agentic AI?

Agentic AI is widely used in various industries, such as:

Autonomous Vehicles: Self-driving cars and drones navigate and make decisions independently.

Healthcare: AI systems that analyze medical data for diagnoses or treatment recommendations.

Finance: Automated trading systems that make real-time investment decisions.

Customer Service: AI chatbots that autonomously handle customer inquiries and support.

Manufacturing: Robots that optimize production processes and predict maintenance needs.

4. What benefits does Agentic AI offer?

Speed and Efficiency: Agentic AI can process vast amounts of data quickly, making real-time decisions faster than humans.

Improved Accuracy: By analyzing large datasets, AI reduces human errors and makes more informed decisions.

Scalability: AI can handle repetitive tasks at scale, improving productivity without fatigue or inconsistency.

Adaptability: AI can learn from past experiences and adapt to new situations or data, improving over time.

5. What are the ethical concerns associated with Agentic AI?

Accountability: Determining who is responsible when an AI system makes a mistake or causes harm can be difficult.

Bias and Fairness: AI systems can inherit biases from the data they are trained on, leading to unfair or discriminatory decisions.

Transparency: Many AI systems, especially deep learning models, operate as "black boxes," making it hard to understand how they reach their decisions.

Privacy: The use of sensitive data by AI systems raises concerns about data security and privacy rights.

6. How does Agentic AI ensure fair decision-making?

For fairness, AI systems need to be trained on diverse and representative datasets and undergo continued audits for bias. Additionally, frameworks such as explainable AI (XAI) are being developed to furnish transparency and understanding concerning AI decisions.

7. Can Agentic AI replace human workers?

While Agentic AI may automate repetitive tasks or those that are heavy on data, it is unlikely that it will ever completely displace human beings. The expectation is rather that AI will generally supplement human capabilities such that humans can then account for the higher-level, creative, or strategic tasks. Nevertheless, it raises concerns regarding job displacement in some sectors.

8. How does Agentic AI learn and improve?

Agentic AI is mainly reliant on reinforcement-learning-based techniques of machine learning, wherein the AI Agent learns by trial and error. The AI system is given feedback for its actions and thus modifies its behaviour over the years to maximize rewards/halves better outcome.

9. How do companies implement Agentic AI?

Companies can partner with AI technology vendors or develop one-on-one solutions for the implementation of Agentic AI. Such implementations usually involve data collection, model training, and constant monitoring to ensure system behaviour according to design and meaningful insight generation.

10. What does the future hold for Agentic AI?

The far-away future holds promise for Agentic AI, advancing the fabrication of autonomous systems, deeper penetration into industries such as healthcare and transport, and advances in learning algorithms for AI. The more intelligent AI becomes, the smarter the decision-making, the more advanced automation, and the more solutions to complex problems. So, to say, ethical considerations and regulatory frameworks should develop with this growing technology.

Final Thoughts

So, Agentic AI is no more a futuristic concept; it is already the key to independent decision making in a number of industries. And, with companies like Newton AI Tech actually pioneering innovations in Agentic Artificial Intelligence, we are witnessing a paradigm shift as to how machines will learn, adapt and make independent decisions with transition to direct interaction between human agents and agents-machine interaction over agent-agent interactions.

Agentic AIs, in a transform world, will create an intelligent, effective, and interconnected world by balancing their advancement with ethical considerations. The transition entails coming to terms with the fact that AI will be augmenting our lives henceforth, but its use will be a question of how we will craft a better tomorrow using it. 

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How Agentic AI is Enhancing the Automation of Business Process 

Artificial Intelligence (AI) is revolutionizing industries across the globe, with business process automation (BPA) being one of its most significant beneficiaries. Traditional automation has focused on rule-based systems that execute predefined tasks. However, the advent of Agentic AI—AI that is autonomous, adaptive, and capable of decision-making—has ushered in a new era of intelligent automation. 

What is Agentic AI?

Agentic AI refers to AI systems that exhibit agency, meaning they can perform tasks with minimal human intervention, adapt to new situations, and make decisions based on complex data inputs. Unlike traditional automation, which follows a rigid, rules-based approach, Agentic AI can: 

Learn from experience: Continually refine its processes based on new data. 

Make autonomous decisions: Reduce reliance on human oversight. 

Adapt dynamically: Respond to changing business conditions in real-time. 

Communicate and collaborate: Work alongside human employees and other AI agents. 

This ability to act independently makes Agentic AI a game-changer in business process automation. 

The Role of Agentic AI in Business Process Automation 

1. Enhanced Decision-Making 

Agentic AI allows businesses to move beyond simple task automation to intelligent decision-making. By analyzing vast amounts of data, AI-driven automation can identify patterns, predict outcomes, and optimize processes without requiring constant human input. This is particularly valuable in sectors like finance, healthcare, and supply chain management. 

For example, in the financial sector, AI algorithms analyze market trends, detect fraud, and automate loan approvals based on customer credit history, reducing the need for manual assessments. 

2. Hyper-Automation 

Hyper-automation, a key trend in BPA, involves integrating AI with robotic process automation (RPA) to create fully automated workflows. Agentic AI enhances this by: 

Enabling bots to handle complex scenarios rather than just repetitive tasks. 

Making real-time adjustments based on external factors. 

Reducing the need for manual intervention in high-stakes decision-making. 

Businesses implementing hyper-automation can achieve unparalleled efficiency and scalability, minimizing human effort while improving accuracy. 

3. Personalized Customer Experiences 

Businesses are increasingly using AI-driven automation to provide tailored customer experiences. Agentic AI can: 

Analyze customer interactions to predict needs and preferences. 

Provide personalized product recommendations. 

Automate customer service through AI-driven chatbots and virtual assistants. 

For instance, AI-powered recommendation engines used by companies like Amazon and Netflix analyze user behaviour to offer personalized content, leading to increased engagement and customer satisfaction. 

4. Operational Efficiency & Cost Reduction 

With Agentic AI handling repetitive and decision-intensive tasks, businesses can: 

Reduce operational costs by minimizing human labour in routine processes. 

Improve accuracy and efficiency by eliminating human errors. 

Scale operations without a proportional increase in workforce expenses. 

This translates into increased profitability and allows human employees to focus on more strategic and creative tasks. 

5. Supply Chain & Logistics Optimization 

Agentic AI is revolutionizing logistics by dynamically adjusting supply chain operations based on real-time data. Benefits include: 

Predictive inventory management, reducing stock shortages and overstocking. 

Optimized route planning for deliveries, cutting down transportation costs. 

Automated procurement processes that respond to demand fluctuations. 

For example, AI-driven logistics platforms like Amazon’s fulfillment centers use AI to streamline inventory management and warehouse operations, significantly reducing delays and costs. 

Real-World Applications of Agentic AI in BPA 

Banking & Finance 

AI-driven risk assessment and fraud detection systems improve security. 

Automated financial advisory services assist clients with investments. 

Loan processing is accelerated using AI-powered credit risk assessment models. 

Healthcare 

AI assists in diagnosing diseases with greater accuracy. 

Automated administrative processes, such as patient scheduling, streamline hospital operations. 

Personalized treatment plans are generated using predictive analytics. 

Retail & E-commerce 

AI optimizes pricing strategies based on demand trends. 

Automated supply chain management ensures product availability. 

Personalized marketing campaigns increase conversion rates. 

Manufacturing 

Predictive maintenance minimizes equipment downtime. 

AI optimizes production schedules to reduce waste and improve efficiency. 

Automated quality control detects defects faster than human inspectors. 

Human Resources 

AI-driven recruitment tools analyze resumes and match candidates with job roles. 

Automated onboarding enhances employee engagement. 

AI monitors workforce productivity and suggests improvements. 

“Moving beyond static workflows, Agentic AI revolutionizes business process automation by embedding adaptive decision-making at every step. This breakthrough not only automates tasks but continuously refines how businesses operate.”

— Michael Roberts, Senior VP of Digital Operations at Innovare Systems

How to Implement Agentic AI in Your Business 

Implementing Agentic AI in business process automation requires careful planning and execution. Below are the key steps to successfully integrate AI-driven automation: 

1. Identify Business Needs and Goals 

Assess areas where automation can add the most value, such as improving customer service, streamlining operations, or reducing costs. 

Define clear objectives for AI implementation, such as increasing efficiency, accuracy, or scalability. 

2. Select the Right AI Tools and Technologies 

Evaluate AI platforms and solutions that align with your business needs. 

Consider cloud-based AI services for scalability and ease of integration. 

Identify the necessary machine learning models and automation tools to support AI-driven decision-making. 

3. Ensure Data Readiness 

AI thrives on high-quality data; therefore, businesses must ensure data is clean, structured, and accessible. 

Establish data governance policies to maintain data integrity and security. 

4. Integrate AI with Existing Systems 

Ensure AI solutions can seamlessly integrate with enterprise resource planning (ERP) systems, customer relationship management (CRM) platforms, and other business software. 

Leverage APIs and automation frameworks for smooth implementation. 

5. Test and Optimize AI Performance 

Conduct pilot tests before full-scale deployment to assess AI effectiveness and identify potential issues. 

Use feedback loops to continually refine AI models and improve performance. 

6. Train Employees and Foster AI Adoption 

Educate employees on AI capabilities and how it enhances their roles rather than replacing them. 

Develop training programs to upskill staff in AI-related competencies. 

7. Monitor, Measure, and Scale AI Implementation 

Track key performance indicators (KPIs) to measure AI's impact on business processes. 

Continuously refine AI strategies to maximize benefits and expand AI-driven automation across departments. 

Things to Consider 

While Agentic AI presents numerous advantages, businesses must address several challenges: 

Ethical and Regulatory Concerns 

Ensuring AI decision-making aligns with ethical standards and regulatory guidelines is critical. Transparent AI governance is necessary to avoid biases and ensure fair decision-making. 

Data Security and Privacy 

AI-driven automation relies on vast amounts of data, making cybersecurity a top priority. Businesses must implement robust security measures to protect sensitive information from breaches. 

Workforce Adaptation 

Employees need to be upskilled to work alongside AI systems effectively. While AI automates repetitive tasks, human employees must shift towards roles requiring creativity, critical thinking, and strategic planning. 

Implementation Costs 

Deploying Agentic AI requires substantial investment in technology, infrastructure, and employee training. However, businesses that successfully implement AI-driven automation often see a high return on investment in the long run. 

Comparison: Traditional Automation vs. Agentic AI 

What’s Next for Agentic AI in Business Process Automation?

The future of BPA with Agentic AI looks promising, with advancements in AI models, natural language processing (NLP), and machine learning (ML) driving even more sophisticated automation capabilities. Businesses that adopt this technology early will benefit from increased efficiency, scalability, and a competitive edge in their respective industries. 

Emerging trends include: 

Autonomous AI agents that can collaborate with human teams more seamlessly. 

AI-powered decision-making systems that continuously improve with minimal supervision. 

Greater integration with the Internet of Things (IoT) for real-time process optimization. 

As AI keeps advancing, bringing Agentic AI into the heart of business operations will go from being a nice-to-have to an absolute must for thriving in a fast-moving, tech-focused world. 

As businesses navigate challenges and opportunities, Agentic AI offers a powerful way forward. By blending intelligent decision-making with automation, it has the potential to redefine efficiency, creativity, and scalability. Yet, the true value of Agentic AI lies not just in its transformative capabilities, but in its ability to empower businesses to focus on what truly matters—innovation, customer satisfaction, and long-term growth.  

The future of business is not just automated—it’s Agentic. 

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