What Is Agentic AI?

AI agents represent a new generation of artificial intelligence that can autonomously pursue complex goals — with minimal human oversight. At their core are machine learning models that simulate human decision-making processes and solve problems in real time. When multiple agents work together, an intelligent orchestration system coordinates the various subtasks.

The key difference from conventional AI solutions is that while traditional systems operate within rigidly defined frameworks and require regular intervention, modern AI agents act independently, goal-oriented, and flexibly. The term "agentic" describes precisely this capacity for action — the ability to operate independently and with clear intent.

This technology builds on the foundations of generative AI and leverages Large Language Models (LLMs) for dynamic use cases. But where generative systems like ChatGPT primarily create content, agentic solutions go a step further: they use generated outputs to autonomously accomplish tasks by integrating external tools. A practical example: while a traditional system would merely suggest the optimal time to climb Everest, an agentic system independently books flights and hotels, perfectly aligned with your calendar.

Autonomy as a core feature

The most significant innovation lies in autonomous operation. These systems complete tasks without continuous supervision. They pursue long-term objectives, master multi-step problem-solving, and track progress over extended periods.

Intelligent responsiveness

Agentic systems combine the best of both worlds: the flexibility of LLMs, which generate nuanced, context-aware responses, with the reliability and structure of classical programming. This enables agents to "think" and "act" in a human-like manner.

A key advantage is that while LLMs work in isolation, agents can actively interact with their environment. They search the web, communicate with APIs, query databases, and use this information to make informed decisions and take concrete actions.

Task-specific expertise

Agent specialisation varies widely. Simple agents reliably execute repetitive individual tasks. Advanced systems leverage perception capabilities and memory functions to tackle sophisticated problems.

Architecturally, there are several approaches: vertical structures with a "conductor" LLM that makes overarching decisions and coordinates specialised agents — ideal for sequential workflows but susceptible to delays. Horizontal architectures rely on decentralised, peer-to-peer collaboration, offering greater flexibility but sometimes slower execution. The choice depends on the specific use case.

Continuous improvement

Agents can learn from experience, process feedback, and continuously optimise their behaviour. Given the right framework, these systems steadily evolve. Their scalability enables deployment in large-scale projects.

Natural interaction

Because LLMs form the foundation, users communicate with these systems in natural language. This revolutionises the user experience: complex software interfaces with countless tabs, menus, charts, and other UI elements are replaced by simple voice or text inputs. Every software interaction is theoretically reduced to a "conversation" with an agent that retrieves the information you need and acts accordingly. The productivity gains are enormous — just consider how much time learning new tools typically requires.

From input to action

Agentic AI systems can be designed in various ways, depending on the task at hand. However, the fundamental workflow typically follows this pattern:

1. Data collection
The agent gathers information through sensors, APIs, databases, or direct user interactions. This ensures the system always has up-to-date data for analysis and decision-making.

2. Analysis and interpretation
After data collection comes processing: using Natural Language Processing (NLP), computer vision, or other AI technologies, the system interprets queries, identifies patterns, and grasps the overall context. This phase determines which steps are situationally appropriate.

3. Strategy development
Based on predefined parameters or user specifications, the AI defines concrete objectives and develops strategies to achieve them. This often involves decision trees, reinforcement learning, or similar planning algorithms.

4. Action selection
The system evaluates various options and selects the optimal one based on criteria such as efficiency, precision, and expected outcomes. This employs probabilistic models, utility functions, or ML-based inference methods.

5. Execution
Once a decision is made, the agent carries out the action — either by communicating with external systems (APIs, databases, robotics) or by delivering responses to users.

6. Optimisation through feedback
After each action, the system can collect results and derive insights to improve future decisions. Through reinforcement learning or self-supervised learning, the AI continuously refines its strategies and becomes increasingly effective at similar tasks.

7. System coordination
AI orchestration refers to the control and management of multiple agents and systems. Orchestration platforms automate AI workflows, monitor task progress, manage resources, control data flows and storage, and handle error events. With the right architecture, hundreds or even thousands of agents could theoretically work in harmony.

Where agentic AI is already in use

Agentic solutions can be deployed in virtually any real-world scenario and integrated into complex business processes:

Financial markets: AI trading bots analyse stock prices and economic indicators in real time, generate forecasts, and execute transactions automatically.

Mobility: Autonomous vehicles use GPS and sensor data for safe navigation and real-time traffic management.

Healthcare: Agents continuously monitor patient data, adjust treatment recommendations based on new test results, and support clinicians via chatbots with instant feedback.

Cybersecurity: Security agents continuously analyse network traffic, system logs, and user behaviour to detect anomalies indicating vulnerabilities, malware, phishing, or unauthorised access attempts.

Logistics: In supply chain management, agents optimise processes through intelligent automation — independently placing orders or adjusting production schedules for optimal inventory levels.

Key considerations

Despite their enormous potential, agentic systems also carry risks. Their greatest strength — autonomy — can have serious consequences if poorly managed. The well-known risks of AI are amplified in autonomous systems.

Some agentic solutions use reinforcement learning with reward functions. If poorly designed, agents may exploit loopholes to achieve "success" in undesirable ways.

Specific risk scenarios:

• Social media agents optimised for engagement that favour sensational or misleading content, inadvertently spreading misinformation

• Warehouse robots optimised for speed that end up damaging products

• Financial AI systems that pursue profit maximisation through risky or questionable trading practices, destabilising markets

• Content moderation AI that censors legitimate discussions in its effort to reduce harmful content

Another concern is that self-reinforcing behaviours can lead to unintended escalation without safety mechanisms, particularly when the system optimises too aggressively for a single metric. Since agentic architectures often comprise multiple autonomous components, potential failure points multiply — delays, bottlenecks, resource conflicts. Such disruptions can trigger cascading effects.

The solution: Models need precisely defined, measurable objectives along with feedback mechanisms to continuously align with actual business intentions.