Agentic AI: Understanding the Next Frontier of Autonomous Technology

In brief:

• Agentic AI marks a leap forward from static generative outputs to intelligent systems capable of acting autonomously, adapting to change, and achieving defined objectives without constant human input.
• As organizations navigate an increasingly dynamic digital economy, agentic AI offers the potential to transform operations, optimize workflows, and enable smarter decision-making at scale.
• While agentic AI’s potential is vast, organizations must address significant risks, such as ethical concerns, security vulnerabilities, and integration complexities, to ensure responsible and effective adoption. 

Autonomous generative AI agents, or agentic AI, promises to redefine how businesses operate and innovate. Unlike its generative counterparts, which focus on creating outputs like text, images, or recommendations, agentic AI is designed to act autonomously, pursuing objectives, making decisions, and navigating complex environments. 

For tech leaders, this shift is a strategic imperative and a critical enabler to stay competitive in the digital economy. By 2028, agentic AI will be included in 33% of enterprise software applications, an increase from less than 1% in 2024, allowing 15% of daily work decisions to be executed autonomously.

Agentic AI is not about automation, it’s about intelligent action. From optimizing supply chains in real-time, orchestrating multi-cloud operations, or managing complex technological projects, agentic AI will have a transformative impact on business processes and workflows.

In this blog, we explore what Agentic AI is, how it differs from other AI technologies, and critical risks and vulnerabilities. Understanding these distinctions will be key for technology leaders seeking to harness its potential while addressing the challenges of this nascent field.

What is Agentic AI?

Agentic AI is a class of AI systems designed to act autonomously or semi-autonomously to achieve defined goals. While traditional AI models require explicit user prompts or operate within pre-defined parameters, agentic AI integrates reasoning, decision-making, and adaptability to operate in dynamic scenarios. 

These systems can proactively initiate actions, manage tasks, and respond to changing circumstances. By leveraging capabilities such as planning, goal-setting, and continuous learning, agentic AI represents the evolution from static AI tools to intelligent agents that drive outcomes. 

Forrester has aptly coined this emerging technology “AI with arms,” referring to its ability to not only learn from patterns in data and make forecasts, but also to make and act on decisions via interaction with other systems. 

Agentic AI is like an intelligent assistant that doesn’t wait for instructions, but can determine what needs to be done and how to do it. It plans its steps and chooses the right tools to accomplish its task - all on its own - without requiring constant guidance from a person or another system.

Recent agentic AI hype implies this technology is novel, but the concept dates back to the 1980s. Multi-agent systems were used when building systems for the Space Shuttle, which was too complicated for a single system. As AI capabilities advanced, these agents have become increasingly independent and can act according to various goals. Satellite and communications managed services provider, Hughes Network Systems, has also deployed agentic AI for years to address service degradation problems. Network and customer equipment stats are continuously fed into their algorithms to facilitate adaptability to fluctuating conditions and anomaly identification.

Over the next few years, agentic AI will be integrated into software, IoT devices, robotics, SaaS platforms, and AI assistants. This trend is already gaining traction; numerous startups are positioning themselves within the market as AI-agent-building platforms, and hyperscalers are embedding their AI assistants with agentic AI. By 2025, over 25% of organizations that use GenAI are expected to launch agentic AI pilots. 

How Does Agentic AI Work?

Agentic AI combines the agility of large language models (LLMs) with the accuracy of traditional programming. Multiple agents, which could be AI models or basic search tools, can be deployed throughout different applications, with their behavior coordinated by an LLM. The AI agents are self-improving, learning from user behavior, databases, and networks to manage multi-step AI applications that have previously been beyond the capabilities of traditional AI.

By blending the strengths of LLMs with traditional programming, agentic AI achieves a balance between adaptability and precision. LLMs provide natural responsiveness and flexibility to tasks requiring dynamic interaction, while rule-based programming ensures reliability for critical processes like security. This hybrid design allows agentic AI to autonomously execute tasks, adjust to new information or changing conditions, and maintain the accuracy and consistency demanded by traditional algorithms. 

An overview of agentic AI’s functionality:

• The system collects real-time data from sources like APIs, sensors, or user inputs via tools such as computer vision, natural language understanding (NLU), and sensor fusion technologies.
• Incoming data is processed into structured formats such as knowledge graphs or probabilistic models, enabling the AI to understand its environment (either virtual, physical, or hybrid depending on the use case) and define goals or sub-goals based on user directives or its internal logic.
• Using techniques like reinforcement learning and automated planning algorithms, the AI evaluates potential actions, selects optimal strategies, and sequences tasks dynamically. 
• The system takes action through robotic systems, APIs, or other mechanisms, adapting its behavior based on evolving conditions or feedback.
• Continuous learning methods, such as online updates and meta-learning, allow agentic AI to refine its strategies and improve over time.  

These components are integrated into a streamlined workflow to enable agentic AI to autonomously adapt and act in complex, real-world scenarios.

Agentic AI vs GenAI

Agentic AI focuses on operational decision-making instead of content generation, which is GenAI’s core function. GenAI can only produce static outputs in response to specific inputs, whereas agentic AI integrates GenAI’s generative capabilities with advanced decision-making frameworks to plan, adapt, and perform tasks as part of an active process to achieve end-to-end autonomy. Not all Agentic AI systems use GenAI, but incorporating it enhances versatility in content-intensive or creative applications. Imagine you’re building a team at your organization to plan and implement a big event. GenAI is like the creative consultant by generating ideas, designing invitations, or drafting speeches based on your specific prompts. It creates what you need but waits for you to ask. Agentic AI is like the event manager; it doesn’t just create, it plans the schedule, delegates tasks, addresses problems as they arise, and ensures the event runs smoothly. 

Agentic AI vs RPA

Robotic Process Automation (RPA) automates repetitive, rule-based tasks, such as data entry or invoice processing, using predefined scripts and workflows. It operates within rigid parameters and can’t modify its action based on unforeseen changes without manual reprogramming, making it ideal for static, predictable processes. Conversely, agentic AI combines decision-making, planning, and learning capabilities to perform sophisticated, multi-step tasks, adapt to dynamic environments, and pursue high-level objectives. RPA is like a factory conveyor belt following a fixed, predefined path to accomplish repetitive tasks efficiently but can’t handle unexpected changes. Agentic AI, on the other hand, is like a skilled worker on the factory floor that can assess new situations, make decisions in the moment, collaborate with others, and adjust their approach to achieve broader goals.

Agentic AI vs Co-pilots

Co-pilots, powered by a foundation of LLMs, can engage intuitively with humans, aggregate complex data, and produce content. However, co-pilots lack the degree of autonomy characterized by agentic AI, which leverage additional methods and technologies to reason effectively and take action on behalf of a user. For instance, co-pilots that conduct testing and recommend code to augment software developers are a popular GenAI use case. But these co-pilots are simply reacting to engineering prompts; agentic AI can transform the ideas provided by a human engineer via a prompt into executable code, automating numerous software development steps.

Agentic AI Complexity Increases Implementation Challenges

Organizations must plan for agentic AI implementation challenges due to its complexity and the need for high adaptability. Vendors often imply agentic AI is simple to implement, but industry analysts refute this claim. Gartner indicates that there’s a large gap between existing LLM-based assistants and fully developed AI agents that will first require organizations to learn how to develop, govern, and trust these systems. 

Common implementation hurdles for agentic AI include:

• Ensuring explainability and transparency in decision-making, particularly for critical applications.
• Mitigating the potential for unintended actions by enforcing ethical development and programming and robust fail-safes. 
• Integration with existing systems, data sources, and workflows, as agentic AI requires seamless interoperability across various technologies, including machine learning models and external APIs.
• Ensuring accurate, real-time responsiveness without delays to operate effectively in unpredictable environments that require rapid, context-aware decision-making.
• Promoting trustworthy decision-making based on complete, up-to-date, reliable data via strategic data management and governance practices.
• Achieving scalability while maintaining performance, accuracy, and adaptability across multiple environments. 
• Developing and fine-tuning agentic AI systems due to the substantial required computational resources, which can be cost-prohibitive for many organizations.

Addressing these challenges requires meticulous planning, cross-disciplinary collaboration, and continuous monitoring and adjustment as the system learns and interacts with its environment. As organizations build and implement agentic AI, they must consider GenAI obstacles in addition to the difficulty of developing bots capable of reasoning, acting, collaborating, and creating. Critically, agentic AI must be dependable enough for enterprise use, regardless of the type of agent, use case, or application.

Agentic AI Risks Could Overshadow Potential Opportunities

Agentic AI offers transformative potential, but also presents unique risks, including ethical concerns, security vulnerabilities, and challenges with accountability and bias. These risks often originate from the very autonomy that makes agentic AI so powerful. 

Without robust safeguards and strategic implementation, the same capabilities that enable agentic AI to solve complex problems could lead to unintended consequences, undermining trust and reliability. Addressing these risks is critical to ensuring agentic AI operates responsibly and safely. 

Primary risks and adoption barriers associated with agentic AI include:

• Autonomous decision-making increases the risk of systems acting unpredictability or outside intended boundaries; agentic AI could potentially “go rogue,” resulting in harmful consequences. 
• Decisions made by agentic AI systems can inadvertently reinforce biases present in its training data or the algorithms guiding its actions.
• Autonomous systems interacting with external environments can increase attack surfaces, creating potential targets for cyberattacks where the AI’s decisions can be exploited or manipulated; for example, facilitating smart malware.
• Determining responsibility for errors or harm caused by autonomous actions can be complex, particularly in high-stakes industries like finance or healthcare.
• Developing, integrating, and maintaining agentic AI requires significant resources, including infrastructure, expertise, and data.
• Agentic AI systems may encounter scenarios requiring value-driven decisions, such as prioritizing one outcome over another, raising ethical concerns.
• Dependence on agentic AI could result in reduced human oversight, increasing the risk of critical failures going unnoticed.
• Evolving AI regulations, such as the EU AI Act, may leave organizations unsure of how to deploy agentic AI while maintaining compliance.
• In multiagent systems, hallucinations can expand across agents; they can influence other agents to execute the wrong action and provide inaccurate answers. 

In response to agentic AI risks, organizations are expected to escalate adoption of guardian agents. By 2028, 40% of CIOs will leverage guidance agents to autonomously monitor and supervise agentic AI systems. This approach will largely be due to increased cybersecurity threats, as agentic AI abuse is anticipated to contribute to 25% of enterprise data breaches by 2028. For example, agentic AI could be manipulated into diverting funds into a fraudulent account. 

Robust governance frameworks, transparent decision-making processes, and continuous monitoring are required to proactively mitigate agentic AI risks. The technology is currently being applied in areas that require autonomous decision-making and complex problem-solving. However, many of its use cases are still in the POC stage and remain in the early stages of development. Ongoing efforts are needed to refine the technology and demonstrate its scalability and reliability in real-world environments. Agentic AI does show promise for use cases such as augmenting regulatory compliance processes in financial services; managing complex customer inquiries in customer support; and automating generating reports to improve cybersecurity systems, addressing skill shortages.

As agentic AI matures, more widespread adoption is anticipated, unlocking new opportunities for innovation, enhanced employee engagement and productivity, and improved customer interaction.

Driving Innovation with Agentic AI and Expert Support

Agentic AI is a transformative leap in how businesses can innovate, enhance workflows, optimize processes, and enhance decision-making. But negotiating its complexities, including identifying the right use cases, integrating with existing systems, and addressing potential risks, requires strategic foresight. 

As this emerging technology transitions from POC to real-world implementation, tech leaders must consider how to mold its goals and boundaries to ensure agentic AI delivers on anticipated value. Building trust will be a critical facet of agentic AI strategies to mitigate potential employee hesitation and promote transparency in how its nascent capabilities are being deployed for the better of your business, workforce, and society. 

At Orange Bridge Marketing, we specialize in empowering tech companies to harness the potential of advanced technologies like agentic AI. From developing impactful messaging to creating comprehensive go-to-market strategies, we help organizations position themselves as leaders in this evolving landscape. If your tech company is ready to explore agentic AI’s possibilities, our expertise in technology marketing and business development ensures you can seize opportunities while overcoming complex product branding challenges.