The concepts of agentic AI systems (often called “agentic AI”) and AI agents are prevalent right now. People are sharing their intelligent workflows and assistants, regularly labeling them as “agentic.” But to understand how these systems work and where they can be used most effectively, we need to understand what agentic AI systems and traditional AI agents are. This article explores what sets these technologies apart, their unique strengths and challenges, and how they are shaping the future of intelligent automation.
Why Is it Called “Agent” or “Agentic”?
In computer science, an AI agent is a system that perceives its environment, reasons with data, and takes autonomous actions to achieve specific goals, similar to a human agent who acts independently for someone else. The concept has been foundational since the early days of AI research, describing any program or machine that can sense, process information, and act toward objectives. “Agentic” simply extends this idea, emphasizing proactive, self-directed behavior and the ability to make decisions over extended periods.
What Is the Difference between Agentic AI Systems and AI Agents?
While both agentic AI systems and AI agents are designed to operate independently and support human activities, they differ in their capabilities and scope, since an agentic AI system is a collaboration of at least two AI agents. Let’s have a closer look at the characteristics, advantages, and challenges:
Characteristics and advantages of AI agents
- Task-specific: designed to perform well-defined, specific tasks within set parameters, such as chatbots, automated schedulers, or data processors
- Limited autonomy: act independently within their domain, but require clear instructions and boundaries
- Single-purpose: usually focus on one type of activity or workflow
- Efficiency: highly effective at automating repetitive, rule-based tasks, reducing human workload and errors
- Ease of integration: can be easily added to existing software or workflows without major changes
Characteristics and advantages of agentic AI systems
- Autonomous decision-making: analyze situations, make complex decisions, and act independently with minimal human oversight
- Goal-driven: plans and executes multi-step strategies to achieve broader objectives, rather than just responding to specific inputs
- Learning and adapting: continuously improves performance by learning from interactions and adapting to new circumstances
- Multi-agent coordination: often orchestrates multiple AI agents or systems to manage complex workflows and dynamic environments
- Flexibility: adapts to changing environments and unexpected challenges, making it suitable for dynamic, complex scenarios
- Proactive problem-solving: anticipates issues, identifies opportunities, and takes initiative to achieve goals
- Scalability: can manage large-scale, multi-step processes and coordinate across various tools and systems
- Continuous improvement: learns from experience, improving its strategies and outcomes over time
Challenges of AI agents
- Limited adaptability: can’t handle tasks or situations outside their predefined scope or training data, and responds to triggers rather than proactively seeking solutions or opportunities
- Dependence on human input: requires updates or retraining to address new requirements/changes in the environment
Challenges of agentic AI systems
- Complexity: more difficult to design, deploy, and maintain; coordinating multiple agents and systems requires robust infrastructure and careful management
- Transparency and accountability: autonomous decision-making can make it harder to understand or explain actions, raising ethical and governance concerns
- Risk of unintended consequences: greater autonomy increases the risk of errors or unintended outcomes if not properly monitored and controlled
How Do AI Agents and Agentic AI Systems Work?
AI agents are typically reactive, using conversational and generative AI to interpret user queries, generate appropriate responses, and guide tasks through their predefined workflows. They interact with their environment, collect data, and make autonomous decisions within set boundaries. While they choose actions independently to reach these targets, their flexibility is limited to the rules and scripts they’ve been trained on.
Agentic AI systems go beyond simple reactions, proactively understanding user intentions and context to autonomously pursue broader objectives without relying on strict scripts or prompts. They leverage advanced language models (LLMs) and a blend of machine learning, natural language processing (NLP), and automation to make complex decisions and orchestrate multi-step processes. These could be optimizing supply chains or personalizing customer interactions, for example. Unlike traditional AI agents, agentic AI systems dynamically adapt to unexpected situations, learn from experience, and use both short- and long-term memory to improve their actions and outcomes over time.
Current Use Cases
AI agents excel in environments with clear, structured processes, such as customer service chatbots, automated email responses, and simple workflow automation in companies. They are used for tasks like password resets, content or project management, where following a predefined path leads to reliable outcomes and significant productivity gains. Advanced AI agents, such as those built with platforms like Microsoft Copilot Studio, can interact with customers, answer support queries, and arrange workflows.
Lab assistant LabTwin
LabTwin, for example, is a leading digital voice assistant that automates data capture and protocol guidance in the laboratory, supporting scientists with real-time reminders and seamless integration into laboratory systems, making it a powerful tool for efficiency and consistency in research environments. For example, it can support the documentation process in cell culture research by enabling scientists to optimize and document cell culture workflows hands-free at the biosafety hood by using voice commands to record observations, set timers for media changes, and access protocols. The need for manual note-taking is therefore eliminated, and the contamination risk is reduced. Although it uses sophisticated AI to process data and guide workflows, LabTwin primarily acts as an AI assistant, but not yet a truly agentic AI system, as it follows structured instructions and does not independently design or adapt experiments.
So, what does a truly agentic system look like?
Agentic AI systems are transforming industries by autonomously planning, deciding, and acting to achieve complex goals, far beyond simple task automation. True agentic AI can independently set objectives, generate strategies, and adapt its approach as circumstances change, with minimal or no human intervention.
Here’s an example: a financial institution uses an agentic AI system made up of several autonomous agents. One agent analyzes market data and forecasts interest rate trends, while another examines customer behavior and derives individual risk profiles. The agents communicate with each other, align their predictions, and jointly adapt investment strategies, continuously learning from new data and each other’s actions to optimize overall performance. While all actions serve the overarching goal of developing the best possible investment strategies for clients and the institution, what truly makes it agentic is that even the institution’s employees don’t know in advance which strategies will emerge, because they aren’t predetermined.
If you are interested in reading more about a specific example of an intelligent, agentic system, go straight to the article by my colleague, Neil Sinclair: Agentic AI: Creating Value with Intelligent Systems.
How Do Agentic AI Systems Change Teamwork?
Agentic AI systems are fundamentally reshaping teamwork by automating routine tasks and enabling seamless collaboration between humans and intelligent agents. This shift allows teams to focus on creativity and strategy while AI handles execution and coordination. It enables…
…automating repetitive tasks, freeing human talent for high-value work;
…multi-agent collaboration, mimicking effective team structures
…adaptive problem-solving.
But don’t overlook that it requires clear communication and governance to ensure trust and accountability. Also, an agentic AI system’s decision-making processes are often opaque, making it difficult for team members to understand or challenge outcomes. This can undermine trust and create friction in human-AI collaboration.
…And What About Transparency, Accountability, and Ethics?
Transparency, accountability, and ethics are foundational when deploying agentic AI systems, especially as these systems process and manage vast amounts of sensitive data. In the DACH region, strict compliance with the GDPR (DSGVO) is essential, requiring transparent documentation of AI usage, clear data protection policies, and regular security audits. Employees must be trained to understand and responsibly interact with AI systems, ensuring both compliance and ethical standards are upheld across the organization. Additionally, robust oversight mechanisms and human evaluation are critical to address potential biases and ensure that agentic AI systems deliver fair, accurate, and trustworthy outcomes.
Conclusion: What Does This Mean for Companies?
When choosing these kinds of AI solutions, companies should start by identifying processes with the highest potential for optimization: typically repetitive communication tasks and standardized workflows. It’s crucial to align AI implementation with business goals and ensure robust governance, transparency, and compliance, especially regarding data protection and ethical considerations. When this base has been built, businesses can develop more creative and advanced agentic systems.
Outlook
Looking ahead, agentic AI is set to become the standard for customer-facing automation, offering human-like experiences that adapt to complex customer needs and drive satisfaction. The integration of agentic AI into existing SaaS tools, such as project management, CRM, marketing, and finance platforms, will make adoption easier and more cost-effective, allowing businesses to benefit from smarter, more autonomous systems without requiring standalone implementations.
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