Key features of agentic AI

Decision-making
Agentic AI is designed to make decisions on its own. It works towards pre-defined goals and can choose the best course of action with little or no human input. This is a step beyond generative AI, which waits for instructions.

Problem-solving
Agentic AI uses a step-by-step loop: it perceives what’s happening, reasons about the best response, takes action, and learns from the result. This cycle allows it to improve over time and handle complex, changing situations more effectively.

Autonomy
Autonomy is what sets agentic AI apart. It doesn’t just respond—it acts. It can operate independently, make decisions, and carry out tasks without constant guidance. This makes it ideal for organisations looking to automate processes at scale.

Interactivity
Because of its proactive design, agentic AI can interact with its environment. It gathers real-time data, reacts to new information, and adjusts its actions. A great example is a self-driving car, which constantly scans its surroundings and makes safety decisions without human input.

Planning
Agentic AI can manage multi-step tasks. It creates strategies to reach a goal, follows through, and adapts as things change. This is key in areas like logistics, customer support automation, or robotic operations, where sticking to a fixed path isn’t enough.

Key features of generative AI

Content creation
Generative AI is built for producing content. It can write emails, generate blog posts, answer questions, and even help with coding. Tools like ChatGPT are great examples—it can create helpful replies, lists, and summaries based on what you ask. This makes it a powerful support tool for teams who need content fast, from marketing to software development.

Data analysis
Generative models can sift through huge amounts of data to uncover trends and patterns. This allows them to support business functions like supply chain management or customer service by offering insights and suggestions in real time.

Adaptability
Generative AI adapts its output based on the input it receives. For example, if a user rephrases a question or gives extra feedback, the AI adjusts and improves the response. This flexibility makes it useful for tasks where the final result needs refining.

Personalisation
Generative AI can tailor its responses and recommendations to individual users. In sectors like retail or e-commerce, this leads to more personal shopping experiences, as the AI learns customer preferences and makes relevant suggestions.

Agentic AI use cases

Customer service automation
Unlike traditional chatbots that follow scripts, agentic AI can interpret customer intent and emotions to take meaningful action. It doesn’t just respond—it resolves. For example, if a customer wants to cancel a subscription, agentic AI could handle the full process: confirm the request, apply the cancellation, and offer relevant follow-ups. This improves customer satisfaction and reduces the need for human intervention.

Healthcare support systems
Agentic AI is being explored for tasks that require real-time decision-making and monitoring. In one example, a smart inhaler system uses agentic AI to track medication use, air quality, and patient routines. It alerts doctors when needed and helps patients stay on track—all without constant manual input. This type of AI has the potential to improve care while keeping patient data safe and secure.

Workflow automation
Agentic AI can independently manage repetitive business processes. In logistics, it can reroute deliveries based on traffic or weather conditions. In an office, it could track inventory, schedule orders, or escalate service issues when something goes wrong, keeping workflows running smoothly behind the scenes.

Financial risk management
In finance, agentic AI can evaluate risks, monitor markets, and adjust investment strategies in real time. A fintech platform might use it to manage portfolios automatically—buying or selling based on live market data and individual risk profiles. It removes the need for constant manual oversight and allows businesses to stay ahead in volatile environments.

Generative AI use cases

SEO content creation
Generative AI helps businesses produce large volumes of SEO-friendly content quickly. Whether it’s blog posts, product descriptions, or landing page copy, these tools can generate keyword-rich content that helps improve visibility on search engines. Marketing teams use this to scale their content output without sacrificing quality.

Marketing and sales support
Sales teams often spend too much time on admin tasks. Generative AI tools can handle outreach emails, summarise meeting notes, and provide quick customer insights. It’s also used in chatbots that help collect leads or answer early-stage questions, giving sales reps more time to close deals.

Product design and development
By analysing market data and customer preferences, generative AI can suggest new product ideas or design variations. For example, a fashion retailer might use generative AI to create new clothing designs based on current trends and buyer behaviour, speeding up the design process and reducing development costs.

Customer support automation
Generative AI is widely used in support chatbots that answer FAQs or assist with order-related queries. For instance, an e-commerce brand might use it to handle refund requests, delivery tracking, and product recommendations—all through an AI assistant that understands customer intent and replies in real time.

Agentic AI trends

Finance and trading
Agentic AI is starting to transform financial services. It can scan market data, make predictions, and carry out trades in real time, without human input. These AI agents can also search the web for the latest updates, helping financial institutions stay informed and react quickly to economic changes.

Warehouse robotics
Companies like Amazon are already using agentic AI-powered robots in warehouses. These systems can navigate spaces, manage inventory, and carry out tasks like picking and packing orders on their own. The result is faster processing and fewer manual bottlenecks in operations.

Urban planning and infrastructure
In city planning, agentic AI can review live traffic feeds, weather conditions, and population data to suggest smarter ways to manage roads, public transport, and utilities. This saves time for city planners and helps improve the quality of life for residents.

HR automation and employee support
Agentic AI is becoming a valuable tool in HR. Unlike simple chatbots, it can manage processes like onboarding, answering policy questions, and recommending benefits based on employee profiles. This reduces repetitive tasks for HR teams and creates a better experience for employees.

Generative AI trends

AI-enhanced applications
Generative AI is being embedded into everyday tools—like CRMs, writing platforms, and design software—to offer smarter, more personalised experiences. These AI-enhanced features help users work faster by suggesting content, offering summaries, or providing creative inspiration based on what’s happening in real time.

Synthetic data for training
When real-world data is hard to find or too expensive, generative AI can produce synthetic data to train machine learning models. This approach is gaining popularity in areas like robotics, autonomous vehicles, and financial forecasting, where realistic yet controlled data is crucial for training systems safely and effectively.

Deepfake generation
Generative AI has made it easier to create realistic images, audio, and video that look and sound authentic. While some applications are creative or fun, others raise concerns around misinformation and ethics. As the tech evolves, more attention is being paid to responsible use and content verification.

Advanced personalisation
Businesses are using generative AI to offer more tailored content and product recommendations. In e-commerce and marketing, for example, AI can analyse user data to deliver custom messages, emails, and experiences, improving engagement and conversion rates across channels.