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Agentic AI: Next Generation of Artificial Intelligence

Accenture and NVIDIA have announced a significant expansion of their partnership, aiming to accelerate the adoption of Agentic AI and drive business innovation on an unprecedented scale.

Intelligent agents - new wave of AI innovation
Alia Shkurdoda

Alia Shkurdoda

Content Specialist

IT services giant Accenture and leading chip manufacturer NVIDIA have announced a significant expansion of their partnership. This partnership, marked by the formation of Accenture’s new NVIDIA Business Group, aims to leverage generative AI solutions to drive business innovation on an unprecedented scale. At the heart of this alliance is a shared vision to accelerate the adoption of Agentic AI among enterprises worldwide. 

In this article, we will highlight the critical points of this partnership, introduce the Agentic AI definition and its governing principles, discuss the growing importance of AI in the business world, and explore the potential impact of intelligent agents across industries.

Key takeaways:

  • Accenture and NVIDIA have expanded their partnership to accelerate AI adoption in businesses, forming a dedicated NVIDIA Business Group.
  • Agentic AI is an advanced form of artificial intelligence that can act autonomously and perform complex tasks.
  • Agentic AI has significant potential across various industries, including finance, IT, manufacturing, and e-commerce.
  • It’s estimated that 33% of enterprise software applications will include agentic AI by 2028.

Accenture-NVIDIA partnership: Accelerating AI adoption

Accenture and NVIDIA have announced an expanded partnership to accelerate AI adoption in businesses. As part of this collaboration, Accenture is forming a dedicated NVIDIA Business Group. It will combine NVIDIA’s cutting-edge hardware, notably its advanced GPUs and AI capabilities, with Accenture’s deep industry expertise and consulting acumen to deliver high-performance, scalable AI solutions tailored to the specific needs of its clients.

With generative AI demand driving $3 billion in Accenture bookings, the new group aims to lay the foundation for agentic AI functionality, leveraging the AI Refinery framework. This platform harnesses the full potential of NVIDIA’s AI stack, including AI Foundry, AI Enterprise, and Omniverse. Accenture AI Refinery will allow companies to reimagine their processes and operations, discover new ways of working, and produce artificial intelligence using their own infrastructure, data, workforce, and business networks. 

To support this initiative, Accenture has trained over 30,000 professionals on NVIDIA’s technology. This global pool of experts will help clients seamlessly integrate AI solutions into their existing enterprise systems and reinvent processes to drive continuous change and create value. The company also plans to introduce a network of hubs with deep engineering skills and the technical capacity for using autonomous AI agents to transform large-scale operations. In addition to existing centers in Mountain View and Bangalore, Accenture is going to open AI Refinery Engineering Hubs in Singapore, Tokyo, Malaga, and London to provide global support.

With all the current and future developments, the Accenture-NVIDIA partnership is positioned as a catalyst for comprehensive business reinvention and innovation, preparing enterprises for the AI-driven economy of tomorrow. It aims to help companies overcome common challenges related to AI adoption, such as cost, complexity, technical debt, data, and talent readiness, and yield the full potential of this technology. This collaboration lays the groundwork for a future where AI becomes an integral, self-directed component of business strategy and operations. 

The rise of Agentic AI – the next frontier in enterprise intelligence

While many companies are enjoying the benefits of generative AI, the next generation of technology is already on the horizon: Agentic AI. It is a more advanced and autonomous form of artificial intelligence, which, unlike conventional AI systems, can not only generate output from a given prompt but also understand and interpret complex instructions, adapt, and operate independently and proactively to achieve desired business goals.

The rise of AI agents is considered one of the most exciting developments in artificial intelligence. This technology is part of the autonomous AI cluster, which also features multi-agent systems, large action models, machine customers, humanoid working robots, and reinforcement learning. All these tech advancements appear in the “Innovation Trigger” section of Gartner’s Hype Cycle for Emerging Technologies.

The place of Agentic AI in artifigial intelligence developments || Source: Gartner

Forrester also includes AI agents in its Top 10 Emerging Technologies for 2024. Intelligent agents are classified among medium-term technologies and are expected to deliver their full potential benefits within the next two to five years.

Agentic AI represents a crucial shift from purely generative AI and promises to bring more sophisticated and robust automation capabilities to various industries and applications. It also plays a pivotal role in the evolution of other emerging technologies, including TuringBots, autonomous mobility, and edge intelligence.

What is Agentic AI?

Agentic AI refers to artificial intelligence systems designed to autonomously perform complex tasks, make decisions, and achieve specific objectives with limited direct human supervision. These systems combine the capabilities of generative AI (like large language models) with classical automation techniques to create more operational and business-specific solutions. 

Agentic AI operates as a coordinated system of individual components that perform specific tasks. These components work together to solve complex problems in a distributed and collaborative way. Such multi-agent systems exhibit characteristics traditionally found in human operators, including decision-making, planning, interaction, and adapting execution techniques based on inputs, predefined goals, and environmental considerations. 

Let’s take a closer look at the properties that define the Agentic AI model:

  • Autonomy. Agentic AI systems can initiate and complete tasks with minimal human intervention, making data-driven decisions and executing actions independently in dynamic environments.
  • Self-prompting. Agentic AI can autonomously cycle through acting, evaluating, and planning without constant external triggers, continuously working towards desired end-states.
  • Goal-oriented behavior. These systems are programmed with clear objectives and strategies to achieve them and focus on outcomes rather than just generating creative content.
  • Proactive planning. AI agentic systems can identify patterns, anticipate future scenarios, and devise strategies to address them, allowing for better strategic agility and preparedness for potential risks.
  • Advanced reasoning. Agentic AI can analyze vast amounts of data, draw nuanced inferences, and make sophisticated decisions considering multiple variables and potential outcomes.
  • Memory. Agentic AI maintains internal knowledge representations, accumulating and utilizing information from past interactions and experiences.
  • Continuous learning. Agentic AI systems evolve by incorporating new data and experiences, interacting with their environments, and learning from feedback to refine their strategies and actions to better meet goals.

Agentic AI represents a significant evolution from traditional rule-based systems or simple generative AI models, offering more flexible, intelligent, and autonomous solutions for complex business processes and problem-solving scenarios. According to Capgemini, 10% of organizations already use AI agents, while another 82% plan to integrate them within the next three years. As AI technology progresses, they will transition from the role of supportive tool to that of independent agent with full execution capability.

There are already several examples of Agentic AI being implemented by world-renowned companies. LG has developed an AI agent capable of managing smart-home devices, monitoring the well-being of pets, enhancing domestic security, and improving energy efficiency without user oversight. Klarna, a Swedish payments company, utilizes an AI assistant that addresses service requests, manages refunds, and handles returns in various languages, handling the workload equivalent to 700 employees and cutting repeat inquiries by 25%.

Agentic AI model: new possibilities for the future

The technology behind agentic workflows is still in its early stages, but that is about to change. It is predicted that 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously. 

There is already some level of trust in AI agents for certain tasks, such as composing work emails, coding, and analyzing real-time data. However, organizations are also aware of the need to implement safeguards for verifying AI-generated decisions. This approach aims to maintain transparency and ensure accountability in AI-driven processes, striking a balance between leveraging AI capabilities and maintaining human oversight.

How does Agentic AI work?

Agentic AI operates through a sophisticated, multi-stage process that enables it to tackle complex challenges with a high degree of autonomy. At its core, the Agentic AI workflow is designed to mimic human problem-solving processes, breaking down complex tasks into manageable components and addressing each with specialized knowledge and tools. 

This step-by-step process allows the agentic AI system to solve complex, multi-faceted problems autonomously while maintaining the flexibility to incorporate user feedback and adapt to changing requirements throughout the execution. 

Step #1: User input

A user provides instructions to the AI system using natural language, much like they would communicate with a human assistant. The system interprets the user’s intent, analyzing the language to understand the desired outcome, and may ask for clarification if needed.

Step #2: Planning

The system processes the user’s prompt into a structured workflow, breaking the overall objective into specific tasks and subtasks. A manager subagent within the system assigns these smaller chunks of work to specialized subagents.

Step #3: Task execution

The specialized agents work on their individual missions, utilizing their domain knowledge, relevant tools (such as APIs or functions), organizational data, and systems, as well as drawing upon prior “experiences” and codified expertise. Throughout the execution phase, the AI agents coordinate with each other, sharing information and results to ensure cohesive progress toward the overall goal.

Step #4: Iterative improvement

The Agentic AI system confirms with the user that the output meets their expectations. If not, it may request additional input from the user to ensure accuracy and relevance, refining the solution based on user feedback and working iteratively until the desired result is achieved.

Step #5: Output

Once the adjustments are done, the system performs necessary actions in the real world to complete tasks, ensuring that all aspects of the original request have been addressed.

Agentic AI workflow: A step-by-step process

The potential of Agentic AI across various industries

From streamlining business operations and accelerating scientific research to enhancing customer interactions, agentic AI is set to become a fully autonomous digital workforce capable of understanding and executing complex business objectives. Its ability to process vast amounts of data, make real-time decisions, and adapt to new or unexpected situations promises to significantly improve operational efficiency, reduce costs, and drive innovation across industries.

Finance

Agentic AI is transforming financial operations, offering unprecedented speed and accuracy in decision-making processes. For example, in portfolio management, AI agents analyze market trends and make split-second trading decisions, leading to more efficient markets and higher returns. For loan underwriting, agentic systems handle complex credit risk scenarios, streamlining the process of compiling and analyzing borrower information. These systems comprising multiple specialized agents can reduce review cycle times by 20%-60%, traverse numerous data sources, and provide transparent insights for rapid verification. In risk assessment, intelligent agents process vast amounts of financial data to deliver more accurate evaluations and enhance fraud detection capabilities. 

IT

Agentic AI is revolutionizing IT operations and software development, transforming traditional manual processes into autonomous, efficient systems. These systems can act autonomously to build complete software products, moving seamlessly between stages, from initial analysis to continuous monitoring, ensuring quality assurance. Let’s take a closer look at agentic AI applications in the IT sector:

  • Proactive infrastructure management. AI agents can automate cloud application deployment, set up environments, and manage scaling. These agents can anticipate application needs, dynamically adjust environments, and optimize costs by reallocating resources with minimal human input.
  • IT service desk. AI agents automate routine tasks, freeing up staff to deal with more complex workflows. They can provide reminders, diagnose issues, search systems, and take contextualized actions, reducing resolution times and enhancing productivity.
  • Code generation. Text-to-code agents can generate code structures from simple text descriptions, allowing engineers to move quickly from concept to implementation. These agents help ensure coding standards and best practices are consistently applied.
  • Automated code reviews. Agentic AI can streamline the code review process by automatically analyzing code changes, flagging potential issues, and assessing code against predefined standards. This shifts software engineers’ focus from routine tasks to collaborating with AI on human-centered design and navigating complex systems.
  • Legacy system modernization. Specialized AI agents can analyze old code, document and translate various code segments, and produce test cases. This streamlines the complex process of modernizing legacy systems, leading to improved productivity and increased efficiency.

Manufacturing

Agentic AI is set to have a profound impact on the manufacturing sector, ushering in an era of digital factories. This technological evolution represents a cornerstone of Industry 4.0, promising potential improvements in productivity, cost reduction, and environmental impact. By leveraging AI-driven automation capabilities, manufacturers can achieve as much as 50% faster designs and 30% reductions in cycle times.

The key Agentic AI use cases in manufacturing include:

  • Virtual facility robot fleet simulation. Accenture launched the NVIDIA NIM Agent Blueprint, enabling industrial companies to build autonomous, robot-operated software-defined factories. This technology allows for optimizing layouts, workflows, and processes in a virtual environment, significantly reducing time and costs associated with real-world trial and error.
  • Inventory optimization. AI agents can accurately forecast demand and optimize inventory levels, reducing carrying costs and minimizing stockouts. These systems evaluate supplier performance based on criteria such as price, quality, and delivery times, automatically selecting the best suppliers and generating purchase orders in real time.
  • Production scheduling. Agentic AI analyzes production requirements, machine availability, and workforce capacity to create and adjust schedules based on real-time data and changes in demand. This dynamic scheduling maximizes productivity and minimizes downtime, adapting quickly to market fluctuations.
  • Logistics management. Agentic AI leaders use this technology to optimize route planning for faster and more reliable deliveries while reducing transportation costs. These systems can adapt in real time to changing conditions, ensuring efficient distribution networks.
  • Sustainability optimization. By continually optimizing production processes, agentic AI helps reduce energy consumption and waste. This contributes to more sustainable manufacturing practices and often results in significant cost savings.

E-commerce

Agentic AI empowers e-commerce businesses to optimize operations, deliver exceptional customer experiences, and drive sales growth in an increasingly competitive digital marketplace. AI agents analyze data from various touchpoints like the website, mobile app, and social media to gain deep insights into customer preferences, purchasing patterns, and behaviors, allowing for personalized offerings and more meaningful engagement. AI solutions also enable dynamic pricing strategies, adjusting prices in real time to maximize sales during high-demand periods. In addition, AI-driven chatbots and virtual assistants can handle customer interactions, categorize issues, and resolve them instantly or escalate to human agents with full context.

Conclusion

Agentic AI is poised to redefine the future of work in the coming years. Currently, it primarily enhances human expertise, but its potential extends to the full replacement of tasks, roles, teams, and even entire business functions.

The Accenture-NVIDIA partnership is the first stepping stone towards that. This collaboration could reshape the business landscape by positioning AI as a transformative force for comprehensive organizational reinvention. By combining Accenture’s industry expertise with NVIDIA’s technological prowess, it paves the way for intelligent agents to be incorporated into AI assistants, software, SaaS platforms, IoT devices, and robotics.

While agentic AI is still in its early stages, it’s rapidly evolving. Organizations are urged to familiarize themselves with this technology, develop strategies to manage associated risks and prepare their technical infrastructure for integration. 

As businesses navigate this AI-driven landscape, partnering with experienced technology consultants like Neontri can provide valuable guidance and support. Our team brings almost 15 years of experience in developing and implementing AI solutions for fintech, retail, and banking industries. By working with Neontri, companies can:

  • access cutting-edge AI technologies and expertise; 
  • ensure compliance with regulatory requirements;
  • seamlessly integrate AI solutions with their existing systems;
  • benefit from ongoing support and continuous improvement of AI capabilities.

Contact us to explore how agentic AI can drive innovation and efficiency in your organization.

FAQ

What is the difference between agentic and traditional AI systems? 

Agentic AI vs. non-agentic AI differ primarily in their ability to autonomously pursue goals and make decisions. Agentic AI systems can independently plan actions, adapt to new situations, and work towards objectives with minimal human intervention. Conventional AI, in contrast, typically follows predefined rules or patterns and requires more direct human guidance to complete tasks or respond to changes in the environment. 

How do Agentic AI vs generative AI compare?

Agentic AI focuses on autonomous decision-making and goal-directed behavior, while generative AI specializes in creating new content like images, human-like text or audio based on patterns in training data.


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Agata Tomasik
Agata Tomasik
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Head of Outsourcing
agata.tomasik@neontri.com

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