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In the ever-evolving landscape of artificial intelligence (AI), a new term that promises to redefine how businesses interact with technology is Agentic AI.
Explore the four must-know technology trends in 2025 here.
What is Agentic AI?
Agentic AI represents a significant leap in artificial intelligence.
Similar to how human specialists handle distinct roles and tasks, agentic AI organizes independent AI agents into collaborative groups. These agents leverage advanced reasoning and strategic planning to tackle complex, multi-layered problems, with large language models serving as their “brains” to guide decision-making.
Therefore, what sets Agentic AI apart is its agency—its ability to design workflows, optimize processes, and make real-time decisions without requiring constant human intervention.
Agentic AI has the following key characteristics.
- Autonomy: Agentic AI can act independently, handling complex, multistep tasks.
- Reasoning: It uses contextual understanding to make decisions beyond simple if-then logic.
- Reinforced learning: These systems continuously improve by learning from outcomes and user interactions.
- Language understanding: They interpret and generate natural language, allowing seamless human-AI communication.
- Workflow optimization: Agentic AI can analyze data, identify inefficiencies, and optimize processes on the fly.
The use of agentic AI is rapidly gaining momentum across various sectors. In fact, 90% of respondents from non-tech companies have already implemented or plan to implement agents, a figure nearly on par with the 89% reported by tech companies.
How does agentic AI work?
At its core, agentic AI functions through a combination of goal-setting, decision-making, and adaptability. Here’s how these components work:
- Goal-setting: The system identifies a clear objective—whether optimizing a process, automating a task, or solving a problem.
- Decision-making: It evaluates available data, analyzes potential actions, and selects the best course based on real-time information.
- Adaptation: Using reinforced learning, the system evolves its approach, improving efficiency and effectiveness over time.
One of the most prominent agentic AI examples is in supply chain management. Agentic AI frameworks can be leveraged to manage inventory levels, predict demand spikes using historical data, and automatically adjust orders to ensure optimal stock levels. If a disruption occurs—such as delayed shipments—the AI adapts by identifying alternative suppliers or redistributing stock from other warehouses.
Why is agentic AI the next big thing in the AI landscape?
Agentic AI is rapidly gaining traction as a game-changer in the AI landscape thanks to its unique combination of flexibility, autonomy, and real-time adaptability – four key reasons outlined by IBM.
Combines flexibility and precision
Agentic AI systems offer the best of both worlds by leveraging the strengths of both large language models (LLMs) and traditional programming.
LLMs excel in processing natural language, offering flexibility, adaptability, and creativity in tasks such as content generation, code completion, and summarization, which traditional programming struggles to handle.
Meanwhile, traditional programming ensures precision, repeatability, and efficiency for complex workflows, high-performance tasks, or systems requiring deterministic control.
By combining these capabilities, agentic AI can autonomously perform tasks that require both the adaptability of LLMs and the reliability of traditional code, allowing for more dynamic and powerful solutions.
Extended reach and real-time information
During the early months after hyper-intelligent chatbots such as ChatGPT or Gemini were released, a prominent drawback was their inability to provide up-to-date answers since they had been trained on data gathered up to a certain point of time only.
This challenge is common in the case of LLMs. LLMs are generally trained on static datasets that represent a snapshot of information up to a specific point in time. They cannot actively gather new data from the web after their training period and can only generate responses based on the knowledge they’ve been trained on. These models do not have the ability to access or update real-time information from external sources independently.
Furthermore, LLMs are not capable of directly interacting with external tools or data systems, such as spreadsheets, cloud platforms, or analytics software, nor can they autonomously set up processes to monitor and collect ongoing data, like from IoT sensors, business workflows, or system logs, as they are not designed to handle continuous tasks.
To counter this problem, agentic AI systems can access real-time data from external sources beyond their initial training data. They can interact with APIs, databases, and IoT devices, and continuously gather new information through feedback loops. This ability to leverage up-to-date data empowers AI systems to make more informed and dynamic decisions over time.
Autonomous operations
With the advanced capabilities of LLMs and the specialized focus of agents, agentic AI can operate independently and autonomously to carry out specific tasks without needing constant human oversight. This allows for continuous functioning in environments where human supervision is either limited or unnecessary. Autonomous systems can maintain long-term objectives, manage complex, multi-step tasks, and track progress over extended periods.
Intuitive user interface
Many business functions currently handled by software-as-a-service (SaaS) products could be replaced or enhanced by agentic systems. These systems would allow employees to interact with data and perform tasks more efficiently by using natural language inputs and simplified user interfaces.
For instance, consider a ticketing system used by software developers to track project progress. Such systems typically involve numerous tables, tabs, and workflows, which can be overwhelming and difficult to navigate. To access valuable information, users must sift through a complex maze of menus, often wasting time searching for the right data. Afterward, they might need to manually use that data to create a presentation.
Now, imagine a scenario where instead of navigating through endless tables and menus, the user could simply ask for the information they need in plain language such as:
- Generate a presentation slide showing 5 bar graphs that represent the number of completed tickets per employee for the current month
- Generate a report with a pie chart to demonstrate the progress of all running projects
With agentic systems, these tasks could be streamlined, making the entire process faster and more intuitive.
Challenges and considerations in adopting agentic AI
While agentic AI holds tremendous potential to transform business operations, its implementation is not without challenges.
Accountability
As AI systems gain autonomy, one of the primary concerns is accountability. In a world where AI can make decisions and perform actions on behalf of users, the question arises: Who is responsible if something goes wrong?
To address this question and ensure full accountability should agentic AI make a mistake, Whether it’s an error in judgment or a harmful decision made by an AI agent, businesses must establish clear frameworks to address these issues.
Furthermore, organizations must implement comprehensive governance models that outline the accountability structure in cases of AI-driven failures. This could involve defining roles and responsibilities within AI decision-making processes, ensuring transparency, and making provisions for human intervention when necessary. +
Data privacy and security
Agentic AI systems are data-driven, which means that ensuring data privacy and security becomes even more critical. Since these systems process and learn from sensitive user data, the risk of data breaches or misuse increases.
Data dependency
Agentic AI models require large volumes of data to function properly. This data can include personal, financial, and operational information, making it a target for cyberattacks. Without strong security measures, there’s a risk of exposing sensitive data.
Cybersecurity compliance
Executives must ensure that their organization complies with relevant data privacy regulations, such as GDPR or CCPA, to protect against breaches. Implementing robust security protocols, including encryption and access controls, is essential.
Data integrity
Beyond security, it’s crucial to maintain the integrity of the data. Poor-quality or biased data can negatively impact AI decision-making, leading to inaccurate outputs. Ensuring data accuracy and consistency is key for optimal system performance.
Human-AI collaboration
For agentic AI to be successful, it’s not enough to just implement the technology. Human-AI collaboration is essential to maximize its potential and ensure a smooth transition into business processes.
Training and upskilling employees
Employees must be equipped with the skills to work effectively alongside AI. This includes training in how to interpret AI outputs, understanding its decision-making process, and managing AI-driven workflows. Businesses should invest in continuous learning programs to bridge the skills gap and promote a collaborative culture.
Training and upskilling employees
Employees must be equipped with the skills to work effectively alongside AI. This includes training in how to interpret AI outputs, understanding its decision-making process, and managing AI-driven workflows. Businesses should invest in continuous learning programs to bridge the skills gap and promote a collaborative culture.
Adapting to change
The introduction of Agentic AI will require shifts in organizational workflows. Leaders must promote an open mindset and foster a culture that views AI as a tool for augmentation rather than replacement, ensuring that AI-driven processes are aligned with business goals and objectives.
Conclusion
Agentic AI represents a powerful evolution in artificial intelligence, offering businesses a unique blend of autonomy, adaptability, and precision. As we’ve explored, it combines the flexibility of LLMs with the reliability of traditional programming, allowing organizations to automate complex workflows, optimize processes, and make real-time decisions with minimal human oversight. However, like any emerging technology, Agentic AI brings its own set of challenges, particularly around ethics, data privacy, and human-AI collaboration.
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