Tech trends we see in 2025 and beyond (including Agentic AI)

As technology continues to evolve at an unprecedented pace, businesses are increasingly turning to innovative solutions to stay competitive.

One such advancement is the emergence of agentic AI, a sophisticated form of artificial intelligence that empowers machines to operate with human-like autonomy.

This paradigm shift enables AI systems to make decisions, understand context, and achieve complex goals with minimal human intervention.

Companies like Tesla are already leveraging autonomous decision-making capabilities to advance self-driving car technologies, while firms like Google are utilizing AI to enhance search algorithms and predictive text services.

As enterprises explore these possibilities, they realize that successfully implementing agentic AI requires a blend of strategic partnerships and cutting-edge technologies.

1. Agentic AI

Agentic AI refers to a type of artificial intelligence system that allows AI agents to act autonomously, make decisions, and take actions with minimal human supervision, effectively mimicking human-like agency by understanding context, adapting to situations, and pursuing complex goals without explicit instructions at every step.  

Essentially, it’s an AI system designed to operate independently and solve problems with a high degree of autonomy, learning and improving through interactions with its environment. 

However, as highlighted by Jayesh Chaurasia, analyst, and Sudha Maheshwari, VP and research director, successfully implementing agentic AI poses considerable challenges. They note that “three out of four firms (75%) that build aspirational agentic architectures on their own will fail.”  

This is because constructing advanced agentic architectures requires not only diverse and multiple models but also sophisticated retrieval-augmented generation stacks, advanced data architectures, and specialized expertise.  

Mature companies acknowledge these complexities and choose to collaborate with AI service providers and systems integrators, taking advantage of their specialized skills to develop state-of-the-art agentic solutions. 

Key characteristics of agentic AI 

• Autonomous decision-making: Agentic AI systems are engineered to assess situations and make decisions without requiring constant human oversight. They utilize complex algorithms and machine learning models to weigh different options, predict outcomes, and select the most appropriate course of action. 

• Contextual understanding: A crucial element of agentic AI is its ability to comprehend and interpret context. This means understanding the environment, user intentions, and relevance of information to make informed decisions, much like a human agent would in similar situations. 

• Learning and adaptation: These AI systems continuously learn and adapt from their interactions with the environment. Drawing on techniques such as reinforcement learning and neural networks, they refine their strategies and improve performance over time, enhancing their ability to handle new and unforeseen scenarios effectively. 

• Goal-driven behavior:Unlike traditional AI systems programmed to perform specific tasks, agentic AI can set and pursue complex, multi-step goals. They identify objectives, plan strategies to achieve them, and adjust their methods as circumstances change. 

• Minimal supervision: While human oversight is still occasionally necessary, agentic AI minimizes the need for detailed human intervention. It thrives on self-sufficiency, operating effectively within defined parameters or learning boundaries set during its development and deployment phases. 

Mechanisms enabling agentic AI 

• Machine learning models: Core to agentic AI are advanced machine learning models that enable the system to process data, recognize patterns, and draw conclusions that inform its decisions. 

• Natural Language Processing (NLP): NLP technologies facilitate the understanding and generation of human language, allowing agentic AI to interact with users or other systems in a human-like manner, thereby enhancing its contextual understanding and decision-making capabilities. 

• Reinforcement learning: This technique allows agentic AI systems to learn from a system of rewards and punishments, refining their strategies to optimize outcomes. It is particularly effective in training AI to perform complex tasks without step-by-step human guidance. 

• Cognitive architectures: Cognitive architectures model human cognitive processes, providing agentic AI with a framework for reasoning, problem-solving, and learning that mimics human thought patterns. 

Applications and implications 

• Healthcare: In medicine, agentic AI can assist in diagnosing diseases by autonomously analyzing patient data, identifying symptoms, and recommending treatment plans without direct physician input. 

• Autonomous vehicles: For self-driving cars, agentic AI is critical as it enables vehicles to navigate roadways, make split-second driving decisions, and interact safely with other road users even in complex environments. 

• Financial services: In the financial sector, agentic AI autonomously manages investment portfolios, analyzing market conditions, and executing trades with minimal human intervention based on projected outcomes and risk assessments. 

• Robotics: Industrial robots with agentic AI capabilities can independently manage manufacturing tasks, adapt to changes in the production line, and troubleshoot issues, boosting efficiency and productivity. 

Agentic AI offers substantial benefits by providing systems that can think, learn, and operate autonomously, reducing the burden on human operators and enabling faster, more efficient problem-solving. As these technologies continue to evolve, they hold the potential to transform industries by enhancing productivity, precision, and innovation. 

2. Edge computing 

Data processing will move closer to where it is generated, enabling faster response times and real-time analysis through edge devices connected to the IoT.  

Edge computing is an architectural approach where data processing occurs at or near the source of data generation, as opposed to being centralized within distant data servers or cloud infrastructures.  

This proximity allows for data to be processed locally on devices such as sensors, gateways, or local servers—collectively known as edge devices—that are part of the broader Internet of Things (IoT) ecosystem. 

Key components of edge computing

• Edge devices: These include any hardware that generates or collects data. Common examples are sensors, smartphones, wearable devices, and IoT appliances. These devices are often equipped with the necessary computational resources to analyze data locally. 

• Edge gateways: Acting as intermediaries between edge devices and the cloud, gateways perform preliminary data processing and filtering. They ensure that only relevant or necessary data is transmitted to the cloud, reducing the burden on central data warehouses. 

• Local edge servers: In certain scenarios, more robust computing power is required closer to the data source. Local edge servers provide this power, supporting applications that require intensive processing capabilities. 

• Edge nodes: These are distributed points that conduct processing tasks across various locations, reducing the need to transmit raw data back to a centralized cloud. Edge nodes play a critical role in maintaining real-time data processing and analysis capabilities. 

How edge computing works

• Data collection: At its core, edge computing begins with the collection of data from various edge devices. 

• Local processing: Once collected, data is processed locally, enabling immediate analysis. Algorithms running on edge devices can handle tasks such as data filtering, quality control, and real-time analytics. 

• Decision-making: Processed data at the edge allows for instant decision-making, which is vital for applications requiring immediate response actions. 

• Integration with cloud services: Although edge computing emphasizes local processing, it still often works in tandem with cloud services. Important insights, aggregated data, or long-term storage tasks can be handled by the central cloud only after the immediate computing needs have been met locally. 

Applications and use cases

• Industrial IoT (IIoT): In manufacturing, edge computing supports real-time monitoring and automation tasks, such as predictive maintenance, quality control, and operational optimization. 

• Smart cities: Edge computing enhances urban management systems, including traffic control, public safety, and energy management, by processing sensor data locally. 

• Healthcare: Wearable devices that monitor patient vitals in real-time use edge computing to provide immediate feedback and alerts to medical staff. 

• Retail: Edge computing allows retailers to customize in-store experiences and streamline operations through real-time analytics on customer behavior and inventory levels. 

Edge computing enhances operational efficiency by significantly reducing latency, boosting data transfer speeds, and cutting down on bandwidth usage.

Its proximity-based architecture supports real-time data processing, making it an essential component for developing responsive and intelligent applications, particularly when coupled with AI technologies. 

3. Quantum computing 

While still in the early stages, quantum computing could be used to solve complex problems in areas like drug discovery, materials science, and financial modeling. 

Quantum computing is an advanced field of computing that leverages the principles of quantum mechanics to process information. Unlike classical computers, which use bits as the smallest unit of data (represented as 0 or 1), quantum computers use quantum bits or qubits.  

Qubits have the unique ability to exist in multiple states simultaneously, thanks to quantum phenomena such as superposition and entanglement. 

Key concepts in quantum computing

• Qubits:  Qubits are the fundamental units of quantum information. Unlike classical bits, which are binary, qubits can represent a 0, a 1, or both simultaneously through superposition. This characteristic enables quantum computers to perform multiple calculations at once, significantly increasing computational power. 

• Superposition: This principle allows qubits to be in a combination of all possible states concurrently. Superposition provides quantum computers with the ability to process a vast amount of possibilities simultaneously, exponentially expanding the scope of feasible computations. 

• Entanglement: Entanglement is a quantum phenomenon where qubits become interconnected in such a way that the state of one qubit can instantaneously affect the state of another, regardless of physical distance. This property can be harnessed to perform highly complex computations efficiently and create more secure communication channels. 

• Quantum gates: Similar to logic gates in classical computing, quantum gates manipulate qubit states. They perform operations on qubits by altering their probabilities, facilitating complex algorithms and computations not possible with traditional gates. 

• Decoherence: A significant challenge in quantum computing, decoherence refers to the loss of quantum state due to interactions with the environment, which can cause errors in calculations. Maintaining quantum coherence in qubits is essential for accurate and reliable computation. 

How quantum computing works

• Quantum circuits: Quantum computations are performed through quantum circuits comprising a series of quantum gates that manipulate an initial set of qubits to solve complex problems. These circuits are designed to leverage the principles of superposition, entanglement, and interference to perform calculations. 

• Quantum algorithms: Quantum computers rely on specialized algorithms, such as Shor’s algorithm for factoring large numbers and Grover’s algorithm for searching unsorted databases, which capitalize on the unique properties of quantum mechanics to solve problems exponentially faster than classical algorithms. 

• Quantum speedup: The parallelism provided by quantum computing significantly accelerates processing times for particular problems, unlocking new possibilities in domains where classical computers are hampered by time or resource constraints. 

Applications and use cases

• Drug discovery and chemistry: By simulating molecular and chemical reactions at an unprecedented scale and accuracy, quantum computing could revolutionize drug development and materials science, facilitating breakthroughs in the design of novel compounds and materials. 

• Cryptography:  Quantum computing’s ability to factor large numbers rapidly poses threats to traditional encryption methods, leading the way for new quantum-resistant cryptographic protocols that offer enhanced security. 

• Optimization problems:  Quantum computing can tackle complex optimization problems encountered in logistics, finance, and manufacturing, such as optimum route planning, portfolio optimization, and supply chain management. 

• Artificial intelligence and machine learning: The acceleration of data processing and pattern recognition by quantum machines offers the potential to enhance AI and machine learning models, improving predictive accuracy and the development of new algorithms. 

Quantum computing has the potential to revolutionize various fields by providing solutions to complex problems that are currently unsolvable by classical computers.

Its ability to process vast amounts of data simultaneously could lead to significant advancements in areas such as drug discovery, materials science, and financial modeling, offering a strategic advantage in technology development and problem-solving. 
 

4. Low-code/no-code development 

Democratizing app development by allowing non-technical users to build applications with visual interfaces, accelerating innovation. 

Low-code and no-code development platforms are transformative approaches that enable users to create applications with minimal hand-coding. These platforms use visual programming environments where users can drag and drop components, automate workflows, and design applications using pre-built modules.  

This approach democratizes app development, empowering individuals without formal programming skills to build functional applications. 

Key characteristics of low-code/no-code platforms

• Visual development interface: These platforms provide intuitive, graphical user interfaces that allow users to design applications using visual tools. Users can build applications by dragging and dropping UI components and connecting them using logic flows, similar to creating a flowchart. 

• Pre-built templates and components: Low-code/no-code platforms come with a library of pre-built templates, UI elements, and components tailored for common business processes, which can be easily customized to meet specific needs. 

• Declarative logic and workflow automation: Users define application logic and workflows through high-level, human-readable configurations instead of traditional coding. Platform features include rules engines, form builders, and automated task processes. 

• Interoperability and integrations: These platforms offer seamless integration with existing systems, databases, and third-party services via APIs and connectors. This interoperability simplifies connecting new applications to previously established processes and data sources. 

• Scalability and cloud deployment: Low-code/no-code platforms typically operate in cloud environments, offering scalability, deployment flexibility, and automatic updates. This enables users to deploy their applications quickly and expand functionalities with growing demands. 

How low-code/no-code development works

• Application conceptualization: Users identify business needs or problems that require a software solution. The idea is translated into a mock-up using templated workflows and user interface designs available on the platform. 

• Development process: The visual development environment allows users to select components, configure workflows, and establish logic using a drag-and-drop methodology. No in-depth coding knowledge is required; instead, users focus on assembling pre-built functionalities. 

• Customization and iteration: Users can refine and customize the application by modifying templates and adjusting configurations. Iteration is a continuous process, where feedback from prototypes influences future changes and enhancements. 

• Testing and deployment: Applications are tested within the platform for functionality, user experience, and bug resolution. Once tested, applications can be deployed to users with a few clicks, enabling rapid time-to-market. 

Applications and use cases

• Business process automation: Organizations use low-code/no-code platforms to automate internal workflows, such as approval processes, employee onboarding, and inventory management, enhancing operational efficiency. 

• Custom business applications:  Businesses can create customized solutions tailored to specific departmental needs, such as CRM platforms, data collection tools, and content management systems, without extensive development time or cost. 

• Prototyping and proof of concept development: These platforms enable rapid prototyping and concept testing, allowing businesses to experiment with ideas and features before investing in full-scale development. 

• Rapid response to market changes: With low-code/no-code development, businesses can quickly adapt applications in response to changing market conditions, customer feedback, and new opportunities. 

Low-code/no-code development significantly accelerates application development, reduces costs, and fosters innovation by allowing non-technical users to build and iterate applications efficiently.

This democratization of app creation empowers organizations to respond to business needs with agility and creativity, enhancing their overall competitiveness. 

How Calibo functions as a low-code/no-code platform 

The Calibo platform is a type of low-code/no-code platform. To explain how this works, let’s explore the features and capabilities that enable it to significantly reduce data and software product development time by up to 50%. 

Visual development environment: 

• Drag-and-drop interface: Calibo provides an intuitive visual interface that allows users to build data pipelines without writing extensive code. Users can design and assemble data pipelines and related workflows and integrations by simply dragging and dropping components and applying configurations or the minimal code needed. 

Pre-built components and templates: 

• Calibo offers a rich library of pre-configured components and templates that users can leverage to quickly assemble applications and data pipelines tailored to their specific business needs. This eliminates the need to develop common functionalities from scratch, speeding up the development process. 

Data orchestration tools: 

• The platform facilitates the integration and management of data from multiple sources. By using pre-built connectors and integration modules, users can streamline data workflows and perform complex data manipulations without needing to write code. 

Automated workflows: 

• Users can define and automate business processes through visual workflow configurations. Calibo’s platform supports easy setup of automated tasks and decision trees, enhancing operational efficiencies without requiring extensive programming skills. 

Customization and extensibility: 

• While primarily designed for low-code/no-code applications, Calibo allows further customization through code when necessary. This flexibility means users can build upon the existing low-code framework to add unique features that meet advanced requirements. 

Rapid prototyping and deployment: 

• Calibo supports rapid development cycles with tools designed for fast prototyping and testing of applications and data products. Users can quickly iterate on product ideas and deploy functional applications in a fraction of the time traditionally required. 

Scalable cloud deployment: 

• The platform leverages cloud infrastructure to facilitate scalable self-service deployment options, enabling applications and data product solutions to grow and adapt with changing business demands. 

Benefits and impact on development time 

• Reduced development time: By providing a low-code/no-code environment with pre-built assets and streamlined workflows, Calibo cuts down traditional programming and integration time significantly. This enables teams to bring products to market faster. 

• Empowered non-technical users: Calibo’s user-friendly design means that domain experts and non-technical team members can contribute directly to software and data product development, fostering innovation and reducing bottlenecks associated with traditional development life cycles. 

• Cost efficiency: The reduction in development time also translates to cost savings, as projects require fewer resources and shorter durations from conception to deployment. 

• Improved agility: Businesses using Calibo can quickly adapt to evolving market conditions and customer needs by iterating and deploying updates rapidly, ensuring that they remain competitive and responsive. 

Overall, Calibo exemplifies a true low-code/no-code platform by enabling rapid, efficient, and flexible development of data and software products, thereby transforming how organizations approach digital solutions. 

In conclusion, as agentic AI, edge computing, quantum computing, and low-code/no-code platforms evolve, they offer significant opportunities for businesses to innovate and enhance operational efficiency.

Enterprises that embrace these technologies are better positioned to address industry challenges, optimize processes, and drive future growth. By understanding the capabilities and applications of each innovation, businesses can make informed decisions that not only meet current demands but also anticipate future needs.

As organizations adapt to these emerging technologies, they will play a pivotal role in shaping the landscape of tomorrow’s digital economy, leading to transformative changes across various sectors.