Agentic AI: The Next Frontier in Artificial Intelligence.

Artificial intelligence has undergone remarkable transformations over the past few years, rapidly shifting from basic automation tools to sophisticated generative models. Large language models (LLMs) like GPT-4o or Gemini, which can understand and produce human-like text, have pushed the boundaries of machine learning, enabling unprecedented applications in content creation, customer support, and beyond. These advancements have shown how AI can process and interpret vast amounts of data, yet they operate within predefined limits, often lacking the flexibility to adapt in real time.

Now, a new paradigm known as Agentic AI is emerging, poised to revolutionize AI’s role from merely interpreting and answering prompts to autonomously managing complex tasks. This leap promises a future where AI not only enhances productivity and decision-making but also navigates dynamic environments autonomously, making it a transformative technology across industries.

What is Agentic AI?

Agentic AI refers to AI systems that possess a degree of autonomy, capable of making decisions, planning actions, and learning from experiences to achieve specific goals without constant human intervention. Agentic AI systems are designed to have "agency," meaning they can autonomously perform tasks on behalf of users or other systems by designing their own workflows and utilizing available tools.

Unlike traditional large language models that respond to prompts within predefined parameters, agentic AI can adapt and learn from new data and user interactions, plan and execute complex, multi-step tasks, interact with external tools and databases, and improve over time through feedback loops and continuous learning. This capability brings together the versatility and flexibility of LLMs and the precision of traditional programming, offering the best of both worlds.

Previous issues

Traditional large language models, though powerful, have inherent limitations that agentic AI aims to overcome.

First, LLMs are trained on vast amounts of data that represent a snapshot in time, which restricts their knowledge to the date their training data was collected. Consequently, they lack awareness of events or information that arise after their last training update, limiting their utility in scenarios requiring current data. Additionally, adapting monolithic models like LLMs is challenging and resource-intensive, as fine-tuning them requires considerable amounts of data and computational power to adjust their behavior.

Another key limitation is that standard LLMs operate without context-specific access to user information or databases. For example, if someone queries a model to determine their available vacation days, a typical LLM cannot retrieve this information because it lacks access to personalized, secure data. While traditional LLMs can assist with tasks like summarizing documents or drafting text, they fall short when users need specific answers based on real-time or individualized data sources.

Furthermore, the rigid structure of LLMs makes it difficult to add components that verify or validate outputs dynamically. While they can generate responses based on patterns in their data, they are unable to access external databases or tools autonomously to check or supplement their responses. This limits their accuracy and flexibility in handling complex tasks that may require multi-step solutions involving real-world data.

Agentic AI overcomes these constraints by integrating modular components—like real-time search tools, logic verifiers, and specialized databases—that extend LLM capabilities beyond static responses. With the flexibility to access external resources and perform complex tasks, agentic AI combines the language processing power of LLMs with the precision of rule-based programming. This allows AI systems to generate accurate, context-aware responses across a broad range of applications, opening the door to a new level of utility and responsiveness.

In the following section, we explore how agentic AI leverages this adaptability to provide precise, real-time interactions, increased autonomy, and an improved user experience across diverse domains.

Benefits of Agentic AI

Flexible and precise

One of the primary reasons why agentic AI is considered the next big thing in AI research is its ability to combine flexibility with precision. LLMs excel at processing and generating human-like text, making it easier for users to interact with AI using natural language commands. They can generate responses or actions based on nuanced, context-dependent understanding, which is useful in scenarios where traditional programming might struggle to cover all edge cases.

On the other hand, traditional programming offers structured, deterministic algorithms ideal for tasks requiring precision, repeatability, and verifiability. Agentic AI systems leverage the flexibility of LLMs to handle tasks that benefit from dynamic responses, while relying on traditional programming for strict rules, logic, and performance. This combination enables the AI to be both intuitive and precise.

A prime example is Sales Development Representative (SDR) Agentforce agents, which can autonomously engage with prospects, answer common questions, manage objections, and even schedule meetings based on available CRM and external data. By leveraging Retrieval-Augmented Generation (see our post for more information about it), these agents can ground their responses in real-time data, ensuring relevance and accuracy. Through its Google Workspace connection, Agentforce can dynamically create Google Slides presentations or update CRM records based on conversations in Gmail, providing an integrated, automated workflow for sales teams.

Real-time interactions

Another significant advantage of agentic AI is its extended reach and ability to interact with real-time data and external environments. Traditional LLMs are typically trained on static datasets representing information up to a certain point in time and cannot actively gather new information from the web or interact with external tools or data processing systems.

Agentic AI overcomes these limitations by being designed to search the web, call APIs, or query databases. Agents can fetch real-time information, retrieve updates, or pull specific data points critical for decision-making. They can initiate and manage tasks such as data logging, real-time monitoring, and trend analysis, providing LLMs with fresh inputs for more informed decision-making and contextual responses. This capability allows agentic AI to provide more relevant and timely solutions, making them invaluable for applications requiring current data and adaptive responses.

Autonomy

Autonomy is another hallmark of agentic AI systems. They can operate independently and autonomously perform specific tasks without the need for constant human oversight, enabling continuous operation in environments where human supervision is limited or unnecessary. Autonomous systems can maintain long-term goals, manage multi-step tasks, and track progress over time. Let’s see some examples:

1. Autonomous marketing campaign management

An agentic AI tasked with managing a marketing campaign can autonomously handle various stages of campaign management, from content creation to performance analysis. For example, the agent could start by developing targeted content based on audience data and recent engagement trends. It would then distribute this content across multiple platforms—social media, email, or websites—and monitor the campaign’s performance in real time.

As engagement data comes in, the AI could dynamically adjust its strategy, such as reallocating budget to better-performing channels or shifting the tone of messaging to resonate with particular demographics. If a particular ad format underperforms, the AI could replace it with a more effective one. This capability not only optimizes the results of the campaign but also frees up marketers to focus on creative strategy and innovation. The agent can operate continuously, ensuring that the campaign is always in alignment with business goals and adapting in real time to maximize return on investment.

2. Real-time healthcare monitoring and treatment adjustments

In healthcare, agentic AI can be a game-changer for patient care by continuously monitoring a patient’s vital signs, medication adherence, and other health indicators. For example, an agent could monitor data from wearable devices, such as monitoring heart rate, blood pressure, and oxygen levels, and compare these metrics with historical data and clinical guidelines. If any metrics fall outside of safe parameters, the agent could alert medical staff or adjust treatment plans automatically, such as increasing medication dosage or recommending a diagnostic test.

In cases involving chronic conditions like diabetes, the agent could continuously analyze blood glucose levels, and suggest dietary adjustments to the patient in real-time. By autonomously tracking patient data and providing clinicians with actionable insights, agentic AI supports proactive and responsive care, potentially improving patient outcomes and reducing hospital visits. It allows healthcare professionals to focus on complex, nuanced care while the AI handles routine monitoring and adjustments.

3. Autonomous cybersecurity surveillance and threat response

In cybersecurity, agentic AI can act as a vigilant and tireless defender by continuously monitoring network traffic, system logs, and user behavior for potential threats. For instance, an agent can track access patterns across a company’s systems, identifying any unusual activity that could indicate phishing attacks, malware intrusions, or unauthorized access attempts. If a specific user’s behavior deviates significantly from the norm—such as multiple failed login attempts or access to sensitive data outside usual hours—the AI could flag this as suspicious and immediately trigger security protocols.

Additionally, the AI could analyze incoming network traffic for signs of distributed denial-of-service (DDoS) attacks, blocking potentially malicious IP addresses in real time. When a threat is identified, the AI can autonomously deploy countermeasures, such as isolating affected servers or restricting access to compromised accounts. This level of autonomous threat response enables organizations to maintain robust security even in the face of rapidly evolving cyber threats, with minimal human intervention needed for routine security monitoring.

Enhanced user experience

Moreover, agentic AI offers an intuitive user experience by allowing people to interact with complex systems using natural language, making technology more accessible and reducing the learning curve associated with intricate software platforms. For instance, instead of navigating through complicated software to generate reports or presentations, a user can simply ask the agent in plain language to "generate a sales report for Q4," and the agent will handle the data retrieval and presentation formatting autonomously. This intuitive interaction enhances productivity and allows users to focus on decision-making rather than data management.

The potential applications of agentic AI are vast and transformative across various industries. In business operations, agentic AI could revolutionize how businesses handle day-to-day operations, autonomously managing supply chains, optimizing inventory levels, forecasting demand, and handling complex logistics planning. In software development, AI agents could not only generate code but also manage entire development lifecycles, autonomously designing system architecture, writing and debugging code, and overseeing quality assurance processes.

Challenges

Ethical considerations

However, as exciting as the prospects of agentic AI may be, they are not without challenges. Ethical considerations, such as ensuring these systems make decisions aligned with human values, are paramount. The complex nature of AI models can make their decision-making processes difficult to understand or interpret, posing challenges for accountability and trust, especially in high-stakes applications. There's also the question of accountability—who's responsible when an agentic AI makes a mistake? Data privacy and security are other critical concerns, as these systems become more autonomous and handle increasingly sensitive information. Robust safeguards will be essential to protect against misuse or breaches.

Factuality

While agentic AI promises transformative applications across sectors, it also presents challenges related to factuality. The accuracy and reliability of information provided by AI agents ideally require human oversight, but with agentic AI, this supervision can be diminished. This issue arises from the probabilistic nature of large language models, which are not inherently deterministic and can sometimes produce "hallucinations"—responses that seem plausible but are factually incorrect. If data sources are limited or if an incorrect processing step occurs, agentic AI might generate unfounded assumptions or misleading answers. These inaccuracies are particularly problematic in high-stakes fields like healthcare or legal advisory, where precise and verified information is essential for safety, compliance, and overall trust.

Impact on the job market

Moreover, the potential impact of agentic AI on the job market cannot be overlooked. While agentic AI brings new opportunities and boosts productivity, it also risks displacing certain roles, especially those involving repetitive tasks or straightforward decision-making. This shift underscores the need for workforce reskilling, particularly in sectors like customer service, administration, and manufacturing, where agentic AI could automate substantial portions of traditional work. Preparing the workforce through education and targeted skill-building in areas like AI oversight, complex problem-solving, and creative analysis will be crucial. This proactive approach helps mitigate job displacement risks, allowing society to harness the full benefits of agentic AI without compromising employment stability.

Additionally, fostering adaptable career pathways and support systems will be key in ensuring that technological advancements lead to economic growth and societal well-being.

How leading companies are embracing Agentic AI

Major tech companies are making significant strides in agentic AI, with Salesforce (and Google), Microsoft, and ServiceNow leading the charge by deploying advanced AI agents that integrate seamlessly into their existing ecosystems, enabling more autonomous and intelligent task management across industries.

Salesforce’s Agentforce platform, developed in partnership with Google Workspace, stands out as a comprehensive solution for deploying autonomous agents within the Salesforce Customer 360 suite. Agentforce agents can act across multiple channels—like Gmail, Google Docs, and Salesforce apps—to automate sales, service, and customer support tasks. For example, a sales agent can autonomously generate presentations, coordinate meeting schedules, or compile customer insights from Google’s BigQuery without duplicating data.

Additionally, through a low-code agent builder, companies can create tailored AI agents, defining task workflows and even incorporating partner functionalities from platforms like Workday and IBM. This flexibility allows organizations like Royal Bank of Canada and OpenTable to achieve efficiency improvements with case resolutions nearing 90%, a testament to Agentforce’s potential to streamline complex operations across sectors.

Microsoft, meanwhile, has launched Copilot AI agents within its Dynamics 365 and Microsoft 365 ecosystems. Copilot agents support numerous functions by automating tasks within tools like SharePoint, Teams, and Microsoft Office applications, which lets users manage document workflows, respond to inquiries, and generate reports autonomously. Microsoft’s Copilot Studio allows users to build agents that can create support tickets, respond to emails, or update records.

These agents are designed to prioritize secure data handling, leveraging Microsoft’s extensive Azure infrastructure to protect user data, providing organizations with assurance as they scale agentic applications within their workflows. Lumen Technologies, for instance, anticipates major productivity gains and substantial cost savings by integrating Copilot agents across its operations.

ServiceNow also capitalizes on agentic AI through its Now Platform, which emphasizes agents that operate across IT, HR, and customer service functions. ServiceNow agents employ advanced reasoning capabilities to interpret user input within a wide context, creating highly personalized responses while managing multiple workflows autonomously.

These agents can interact directly with a wide range of enterprise systems and data sources, enabling comprehensive and adaptive support across ServiceNow’s platforms. They’re poised to accelerate workflows and free employees from repetitive tasks, allowing more focus on strategic initiatives. ServiceNow envisions agents that could further enhance user experience by incorporating inputs from diverse sources like voice or video, moving beyond text-based commands to support richer, more nuanced interactions.

Oracle has also entered the agentic AI space with Fusion Cloud AI agents, which provide over 50 role-specific agents tailored to optimize tasks across finance, procurement, and human resources. These agents help employees execute repetitive tasks autonomously, such as processing invoices or managing procurement requests, while also being integrated into Oracle’s secure cloud environment to ensure data compliance and reliability.

Conclusion

Despite these challenges, the potential benefits of agentic AI are too significant to ignore. As research in this field progresses, we can expect to see increasingly sophisticated AI agents that can collaborate with humans in ways we've only seen in science fiction. The key to harnessing agentic AI's full potential lies in striking the right balance between autonomy and human oversight. By developing these systems thoughtfully and with a keen eye on ethical implications, we can create AI agents that augment human capabilities rather than replace them.

Agentic AI marks a major step toward more practical AI applications in daily life. By combining the strengths of large language models and traditional programming, these agents can perform tasks that were once thought to be the exclusive domain of humans. As we stand on the cusp of this new era, embracing the possibilities while addressing the challenges will be key to unlocking the full potential of agentic AI. The future of AI is not just about machines that can think but about machines that can act, learn, and collaborate with us to solve complex problems and improve our lives.