What is Agentic AI?
Define Agentic: A Transformational Leap into the Agentic World
Agentic AI represents one of the most significant breakthroughs in the evolution of artificial intelligence. Unlike previous waves of innovation that focused largely on boosting productivity through automation, an agentic AI system is an autonomous AI framework that coordinates multiple AI agents to manage complex tasks and processes with limited human oversight. Within an agentic AI system, an ai agent is an intelligent entity capable of perception, reasoning, and action, working as a component of the broader system. These systems are designed not only to perform specific tasks, but also to operate independently, set and pursue their own goals, and fundamentally reshape how humans interact with machines.
But how can businesses effectively embrace this transformative technology? This is where InvestGlass steps in as a vital partner. InvestGlass is pioneering the development of agentic AI systems that empower organizations to automate complex workflows, make autonomous decisions, and continuously learn with minimal human intervention. By integrating advanced AI models and scalable computing power, InvestGlass enables enterprises to unlock the full potential of agentic AI, driving efficiency, adaptability, and innovation across industries.
This is not just a technological upgrade—it is a reimagination of enterprise workflows. An ai system integrates multiple AI agents to manage complex workflows, enabling businesses to become faster, more adaptive, and more responsive to evolving customer demands. Agentic AI systems can operate independently to perform complex or routine tasks across industries, often with minimal human oversight. They are also capable of autonomously setting and pursuing their own goals, adapting to changing business needs without direct human intervention. Across industries, this shift is already taking root: in Insurance, autonomous systems are streamlining error-prone claims adjudication; in Industrials, intelligent automation is addressing inventory misalignments before they cause disruption; in Retail, generative AI is delivering tailored solutions at scale; and in Life Sciences, AI agents are accelerating complex workflows such as pharmaceutical discovery. Agentic AI systems process data to perceive situations, reason about them, and make autonomous decisions, enabling real-time interactions and complex planning. Autonomous decision making is a core capability of agentic AI systems, allowing them to independently analyze information and act without human input.
These real world applications are more than just conceptual. According to BCG’s most recent IT Buyer Pulse Check, over 90% of enterprises report an intent to deploy agentic AI solutions within the next three years. Mentions of agentic AI in software company filings have increased twelvefold over the past year, underscoring its growing relevance in the enterprise landscape. Meanwhile, leading software vendors are positioning agentic AI as a strategic priority, evidenced by a wave of product releases and acquisitions focused on empowering autonomous agents and enhancing AI applications across business processes.
Agentic means using machine learning to power autonomous systems that act independently, inspired by real world examples
The market implications are equally profound. Globally, agentic AI represents a potential $1 trillion opportunity, with $350–450 billion in the United States alone—surpassing the current U.S. spend on traditional enterprise services. If it follows the trajectory of cloud computing, $300–600 billion of this global opportunity could be realised between 2035 and 2040, if not sooner. In this context, the partner ecosystem—including platforms like InvestGlass—has a critical role to play in driving adoption and value creation.
At InvestGlass, we see ourselves as a vital catalyst in this shift. We are not simply adding AI tools to legacy systems; we are building infrastructure that supports agentic AI systems, capable of autonomous decision making, continuous learning, and automating complex workflows with minimal human intervention. By doing so, we aim to empower financial institutions and enterprise clients to not only adapt—but to lead—in this new agentic world.
The Agentic World: How InvestGlass Embraces the Future of AI-Powered Automation
The rise of agentic AI systems is transforming the way businesses operate, especially in sectors that rely heavily on process automation and decision making. At InvestGlass, we are fully engaged in this exciting shift—building tools that reflect a deeper understanding of agentic AI and its real world applications in financial services, customer engagement, and supply chain management.
Defining the Agentic World
Agentic AI refers to artificial intelligence systems—especially autonomous AI agents—that can act independently to pursue complex goals in dynamic environments. In this context, agentic means possessing the ability to take initiative, make decisions, and perform specific tasks with minimal human intervention.
These systems combine large language models, machine learning algorithms, reinforcement learning, and natural language processing to analyse vast amounts of data, generate insights, and adapt based on user preferences and past interactions. The result is an intelligent automation framework capable of handling complex workflows and tackling tasks that were once solely the domain of human operators.
Agentic AI Fundamentals
Agentic AI refers to a new generation of artificial intelligence systems that can reason, plan, and take action independently, requiring minimal human oversight. Unlike traditional AI, which often relies on direct instructions or constant supervision, agentic AI systems are designed to handle complex tasks and make decisions based on their own goals and objectives. These advanced systems are composed of multiple AI agents working collaboratively to accomplish intricate assignments, leveraging large language models, machine learning, and natural language processing to understand and interact with their environment.
The defining feature of agentic AI is its ability to act independently—an AI agent within such a system can assess situations, set its own goals, and execute actions without waiting for human input. This autonomy allows agentic AI to automate complex workflows, tackle multifaceted challenges, and adapt to changing circumstances in real time. By integrating natural language capabilities and advanced reasoning, agentic AI systems are poised to revolutionize how organizations approach problem-solving, making artificial intelligence a true partner in achieving business objectives.
AI Agents and Autonomous Systems
Agentic AI systems are built on the foundation of AI agents—intelligent entities designed to handle complex tasks and operate independently with minimal human oversight. These AI agents are the driving force behind agentic AI, enabling organizations to automate complex workflows and achieve outcomes that were once out of reach. By leveraging large language models, machine learning algorithms, and natural language processing, AI agents can process data, recognize patterns, and make nuanced decisions in real time.
In the context of autonomous systems, AI agents play a crucial role in transforming how businesses approach chain management and supply chain management. For example, in supply chain operations, AI agents can analyze vast amounts of logistics data to optimize routes, predict demand fluctuations, and streamline inventory management—delivering tailored solutions that boost efficiency and reduce costs. In financial institutions, AI agents are increasingly relied upon to automate repetitive tasks such as data entry, compliance checks, and transaction monitoring, freeing up human experts to focus on more strategic, nuanced decisions.
The integration of AI agents into agentic AI systems brings numerous benefits. These systems can operate independently, adapt to changing conditions, and tackle complex challenges across dynamic environments. As a result, organizations experience improved customer satisfaction, faster response times, and more informed decision-making. By automating complex workflows and enabling intelligent automation, agentic AI systems empower businesses to stay ahead in a rapidly evolving digital landscape, making AI a crucial partner in achieving real world business goals.
From Traditional Methods to Agentic AI Systems
To illustrate the shift from conventional business processes to an agentic state, consider these real world examples:
|
Examples |
The ‘old’ world |
The agentic world |
|---|---|---|
|
Marketing journeys |
Manual A/B testing campaigns with broad audiences and tweaked with imprecise results |
Analyse real-time data to create ‘N-of-1’ experiences, optimise ad campaigns, and forecast market trends |
|
Go-to-market |
Create outreach email templates based on prompts, then hand off to sales representatives |
Drive the full sales motion autonomously with creative discovery, tailored solutions, and tactful follow-ups |
|
Customer service |
Handle basic inquiries using static, scripted responses (~80% coverage) |
Anticipate customer needs, resolve complex queries in real-time, and build lasting relationships |
|
Supply chain |
Use historical data and static rules to forecast demand and manually handle exceptions |
Forecast demand, trigger replenishment, and reroute shipments autonomously to prevent disruptions in chain management |
Today’s AI and its Limitations
In recent years, AI systems have achieved remarkable progress, with advances in machine learning, natural language processing, and generative AI models. However, today’s AI still faces notable limitations. Most current AI systems require significant human oversight to function effectively, especially when handling complex tasks that demand adaptability and nuanced decision-making. In particular, current AI struggles with nuanced decisions that require a deeper understanding of context and subtlety. Generative AI, for instance, excels at content generation but depends on human prompts and lacks the ability to act independently or pursue complex goals on its own.
Autonomous AI agents can perform specific tasks, but they often need extensive programming and struggle to adapt to dynamic environments where conditions change rapidly. This is where agentic AI refers to a new generation of AI systems that go beyond automation. Agentic AI agents are designed to recognize patterns, handle complex tasks, and make decisions with minimal human intervention. By acting independently and pursuing complex goals, agentic AI represents a significant leap forward, enabling AI to operate more flexibly and effectively in real-world scenarios.
Large Language Models and the Rise of Agentic AI
Large language models (LLMs) have played a crucial role in the emergence of agentic AI. These advanced AI models are capable of understanding and generating human-like text, allowing AI systems to interact with users in natural language and respond to complex queries. They are also widely used for content generation in various applications, enabling the creation of articles, reports, and other written materials automatically. By integrating LLMs with machine learning and natural language processing, agentic AI systems can handle complex tasks, automate intricate workflows, and make independent decisions.
This combination of technologies enables agentic AI to deliver tailored solutions across industries. In supply chain management, for example, agentic AI systems can forecast demand, optimize chain management, and adapt to disruptions without manual intervention. In healthcare, these systems can process vast amounts of data to support clinical decision-making and improve patient outcomes. The rise of agentic AI, powered by large language models, is setting the stage for a new era of autonomous systems that can transform how businesses operate and deliver value.
Enabling Real-World Applications: Data Readiness for AI Agents and Intelligent Automation
Deploying agentic AI requires a shift from traditional notions of data readiness to building smart, adaptable data pipelines that allow AI agents to reliably access and process data in real time. These pipelines must be supported by scalable computing power to ensure efficient operation and growth of agentic AI systems. New generative AI-enabled tools now support rapid creation of knowledge maps and integration of both structured and unstructured data sources (e.g. emails, documents, transcripts), which were previously difficult to manage.
System Integrators (SIs) must help clients map their data environments—including operational flows, databases, APIs, and document repositories—while considering industry-specific AI and data regulations. For early pilots or proofs-of-concept, using available internal, public, or synthetic data can deliver quick value. Technologies like the Model Context Protocol (MCP) and Agent2Agent Protocol (A2A) streamline data access across systems.
As agentic AI systems interact with more data, they continuously improve decision making. While long-term success depends on strong data governance and quality, early adoption is achievable by focusing on intelligent pipeline design and leveraging gen AI’s flexibility to operate within existing data landscapes. Educating customers on these evolving integration patterns is essential.
Why Agentic AI Matters in a Business Context
Today’s AI has evolved beyond isolated automations and is now making waves by integrating autonomous decision making into complex business processes. At InvestGlass, we build AI-powered solutions that do not just automate repetitive tasks but also act autonomously, learn continuously, and handle complex tasks in highly adaptive ways.
This shift is fuelled by scalable computing power, natural language models, and AI models trained to recognise patterns, understand human-like text, and operate within ethical standards under human oversight.
The key benefits include:
- Boost efficiency by eliminating time-consuming manual steps
- Automate complex workflows that involve multiple systems and decisions
- Generate insights that inform strategic planning
- Improve customer satisfaction through faster, more personalised service
- Adaptable planning to accommodate shifting goals and operational changes
InvestGlass and the Future of Agentic AI Applications
InvestGlass is not just reacting to the rise of agentic AI—we are proactively embedding these technologies into our platform. Intelligent automation is a key feature of our platform, enabling clients to streamline processes and enhance decision-making. Here’s how we help clients transition into the agentic world:
Customer Journeys and Personalisation
We use machine learning and natural language processing to build customer journeys that evolve in real time. Our systems analyse user preferences, behaviour, and past interactions to deliver tailored solutions that outperform static campaigns.
Go-to-Market Automation
Instead of templated outreach, our platform enables autonomous agents to generate content (content generation), manage engagement sequences, and follow up using insights drawn from complex data sets.
Agentic Customer Service
By using generative AI and intelligent routing, our systems can tackle complex customer issues, handle complex queries, answer natural language queries, and escalate only when necessary—improving efficiency and reducing reliance on scripted dialogue.
Supply Chain Intelligence
In the realm of supply chain and supply chain management, InvestGlass uses AI to predict needs, automate procurement, and manage exceptions, leading to more resilient and adaptive chain management systems.
The Role of Agentic AI in Data-Driven Decision-Making
Agentic AI systems are designed to analyze vast amounts of data and generate actionable insights that drive better decision-making. By leveraging machine learning algorithms and natural language processing, agentic AI can recognize patterns, tackle complex queries, and make nuanced decisions that were previously out of reach for traditional AI systems.
In a business context, agentic AI empowers organizations to address real-world challenges with greater precision. For example, financial institutions can use agentic AI to detect market trends, assess risk, and enhance customer satisfaction through personalized recommendations. Healthcare providers can leverage these systems to interpret complex medical data, support diagnosis, and improve patient care. By enabling data-driven decision-making, agentic AI systems help organizations boost efficiency, respond to changing conditions, and achieve superior outcomes.
Overcoming Challenges with Agentic AI
Despite the numerous benefits agentic AI brings, several challenges must be addressed to unlock its full potential. Ensuring that agentic AI systems operate within ethical standards and do not perpetuate biases is a top priority. These systems also require scalable computing power and advanced AI models to handle complex tasks and adapt to dynamic environments effectively.
Researchers and developers are actively working to create more adaptable planning and decision-making frameworks for agentic AI. Continuous learning is also essential for agentic AI to remain effective in dynamic environments. By focusing on ethical standards, robust decision-making, and the ability to operate in dynamic environments, the industry aims to overcome these hurdles. As these challenges are addressed, agentic AI will be better positioned to deliver on its promise of transforming industries and improving lives through intelligent automation and autonomous systems.
Responsible Agentic AI Development
As we enter a new era of agentic AI, responsible development and deployment are more important than ever. It is essential to ensure that agentic AI systems are transparent, explainable, and fair, minimizing risks such as bias and job displacement. Developers must prioritize ethical standards and consider the broader impact of these technologies on society.
By focusing on responsible agentic AI development, we can harness the benefits of intelligent automation while freeing humans from repetitive tasks and enabling more creative, strategic work. Staying informed about the latest advancements in agentic AI will be crucial as these systems continue to evolve and play a growing role in shaping the future of business and society.
A Crucial Role for Human Oversight
While agentic AI systems are capable of achieving outcomes independently, the role of humans remains essential. The agentic state—where an autonomous system operates under the direction of an authority figure—ensures that AI operates within appropriate ethical standards and in alignment with strategic intent.
At InvestGlass, we ensure that our AI models are always subject to human oversight, maintaining a careful balance between autonomous systems and responsible governance.
Real-World Implications and Future Directions
The rise of agentic AI is set to reshape industries by enabling organizations to operate more efficiently and intelligently in dynamic environments. In supply chain management, agentic AI can automate repetitive tasks, optimize chain management, and respond to disruptions with minimal human intervention. Healthcare providers are leveraging intelligent automation to analyze vast amounts of patient data, deliver tailored solutions, and support complex decision-making processes. Financial institutions are using agentic AI to pursue complex goals, such as detecting fraud or predicting market shifts, by making nuanced decisions based on real-time data.
Looking ahead, agentic AI will continue to expand its role in the real world, operating independently to solve problems and adapt to new situations. Real world examples include autonomous vehicles that navigate traffic, smart homes that learn user preferences, and intelligent automation systems that continuously improve business processes. As agentic AI evolves, it is crucial to uphold ethical standards and ensure these systems align with human values and preferences. The future promises even greater capabilities, with agentic AI poised to tackle increasingly complex goals and deliver transformative value across sectors.
InvestGlass: Your Ready Swiss Generative AI Partner
InvestGlass stands at the forefront of generative and agentic AI, offering a comprehensive suite of solutions designed to help businesses harness the full potential of artificial intelligence. Their advanced AI models are built to generate insights, recognize patterns, and deliver tailored solutions to even the most complex problems. With InvestGlass, organizations can automate complex workflows, analyze vast amounts of data, and empower AI agents to make independent decisions in dynamic environments.
Whether your goal is to boost efficiency, improve customer satisfaction, or tackle complex tasks that require intelligent automation, InvestGlass provides the expertise and technology to help you succeed. Their AI agents are capable of operating autonomously, adapting to new challenges, and delivering measurable results. By partnering with InvestGlass, businesses can unlock the benefits of agentic AI—streamlining operations, enhancing decision-making, and staying ahead in a rapidly evolving digital landscape.
Conclusion and Next Steps
In conclusion, agentic AI refers to a new generation of artificial intelligence systems that can act independently, handle complex tasks, and make decisions with minimal human intervention. By harnessing the power of AI agents, large language models, and advanced machine learning algorithms, these systems are redefining what’s possible in industries such as healthcare, finance, and supply chain management. Agentic AI systems are uniquely equipped to automate complex workflows, generate insights, and deliver intelligent automation that drives business growth and customer satisfaction.
To fully realize the numerous benefits of agentic AI, organizations must invest in scalable computing power, establish robust ethical standards for AI decision making, and prioritize continuous learning and improvement. This approach ensures that agentic AI systems can operate independently, adapt to new challenges, and pursue complex goals in the real world. As we enter this new era of intelligent automation, the collaboration between humans and AI systems will be key to unlocking unprecedented opportunities and achieving outcomes that were previously unimaginable.
By embracing agentic AI and its transformative capabilities, businesses can position themselves at the forefront of innovation—boosting efficiency, enhancing customer satisfaction, and driving meaningful change across industries. The future of agentic AI is bright, and the next steps involve not only adopting these technologies but also shaping their development to align with ethical standards and real-world needs.
InvestGlass your ready Swiss Generative AI partner
The transition to the agentic world represents a major leap in how AI applications support business operations. With InvestGlass, you’re not just automating processes—you’re embracing a new paradigm of autonomous agents, dynamic adaptation, and intelligent automation.
InvestGlass stands at the forefront of this revolution, delivering cutting-edge agentic AI solutions tailored to meet the evolving needs of financial institutions, service providers, and digital innovators. Our platform empowers organizations to harness the full potential of autonomous AI agents, streamline complex workflows, and achieve unprecedented efficiency and agility.
We invite forward-thinking businesses to partner with InvestGlass and lead the way into the future of AI-powered automation, making waves in the industry with innovative agentic AI solutions. To see real world applications in action, get in touch for a demo or explore our platform in more depth.
Would you like this article formatted for publication, or embedded into a CMS page with visuals and quotes?