What is agentic AI?

For a while now, organisations have pursued intelligent automation (IA) as a means to streamline operations, reduce costs, and enhance productivity. These systems – combining AI, machine learning, robotic process automation (RPA), and business process management (BPM) – have delivered significant value by executing predefined workflows and automating routine tasks. However, conventional intelligent automation approaches are reaching their inherent limitations, particularly when confronted with complex, dynamic, and unpredictable situations that require contextual understanding, creative problem-solving, and adaptive responses.

Agentic AI represents a paradigm shift beyond traditional automation. Rather than simply following predetermined rules and workflows, Agentic AI systems can autonomously make decisions, take actions, and continually learn from interactions without constant human intervention. This evolutionary leap transforms AI from a passive tool executing predefined tasks to an active participant capable of understanding context, reasoning through complex problems, and adapting strategies in real-time.

Gartner predicts that by 2028, 15% of daily work decisions will be made autonomously by Agentic AI, up from zero in 2024. This trajectory signals not just a quantitative increase in AI capabilities but a qualitative transformation in how we conceptualise the role of artificial intelligence in organisational contexts.

What is agentic AI?

Before we define what Agentic AI is, we must clarify what is meant when we say Artificial Intelligence. Cognitive artificial intelligence is a computer engineering discipline – a series of mathematically or logic-based techniques that simulate cognitive processes:

• Machine Learning
• Deep Learning
• Rule-Based Systems
• Optimisation Systems
• Natural Language Processing
• Knowledge Graphs
• Agent-Based Computing
• Ambient Intelligence

AI Agents are autonomous or semiautonomous software entities that use AI techniques to perceive, make decisions, take actions and achieve goals in their digital or physical environments. The following are not considered ‘AI Agents’, despite what others may suggest:

• Large language models (these are models)
• A set of instructions that carries out a specific task
• Automated software functions (like procedures)
• Automated procedures from an RPA workflow (processes)
• Conversational assistants (or any other assistant)
• An interface to an assistant (like an assistant UX)

Example use case: Conference invitation

Imagine you receive an invitation to a conference in another country. Without any manual intervention, an AI agent detects the calendar invite in your inbox and autonomously determines whether you can attend. If attendance is possible, the agent:

• Analyses Past Preferences: Reviews previous bookings to identify preferred flight times, airlines, and hotels.
• Automates Travel Arrangements: Books flights and accommodation using the company’s travel system.
• Updates the Calendar: Blocks off the necessary time and adds booking details.
• Handles Payments: Completes the transaction without requiring manual approval.
• Interacts with Organisation’s Expense System: Ensures expenses are registered by following the organisation’s procedures and monitors that payments are made in a timely manner.

Key characteristics of the AI agent

• Fully Autonomous: The agent is not manually triggered – it continuously operates in the background.
• Perceptive and Context-Aware: It recognises an event (calendar invite) and determines the necessary action.
• Data-Driven Decision Making: It leverages historical preferences to make optimal choices.
• End-to-End Execution: It makes bookings, pays for them, and updates schedules seamlessly.

The inclusion of AI agents into intelligent automation

Over the past decade, the intelligent automation market has been fiercely contested. Leading RPA vendors such as UiPath, SS&C Blue Prism, Automation Anywhere, and Microsoft Power Automate have continuously expanded their technology stacks to enable broader automation capabilities. Meanwhile, business process automation specialists like Pegasystems and Appian have been vying for dominance in the same space. Despite this competition, no single tool has emerged as the definitive market leader, and the boundaries between end-to-end business process management (BPM) platforms and intelligent automation solutions are becoming increasingly blurred.

AI agents are now entering this landscape, with a surge of providers racing to enhance existing automation platforms. The focus is on enabling developers to leverage AI agents within their existing tools, making them a natural extension of intelligent automation capabilities. As this evolution unfolds, AI agents are set to become a core component of modern automation strategies.

Defining characteristics of agentic AI

The transition from intelligent automation to Agentic AI is marked by several defining characteristics:

1. Autonomous Decision-Making and Action

While intelligent automation relies on predefined rules and requires human oversight for complex decisions, Agentic AI possesses greater autonomy. It can independently pursue complex objectives, adjust actions dynamically in response to changing conditions, and make strategic decisions aligned with reestablished goals.

2. Contextual Understanding and Adaptation

Agentic systems move beyond the limited learning capabilities of traditional automation, which typically requires manual updates or retraining to adapt to new scenarios. Instead, these systems continuously refine strategies based on new data and experiences, adapting to changing environments in real-time with greater flexibility and responsiveness.

3. Complex Problem-Solving

Where intelligent automation excels at streamlining predefined workflows and handling routine tasks, Agentic AI demonstrates the capacity to manage complex workflows, identify inefficiencies proactively, and tackle multi-step processes requiring sophisticated decision-making.

The future of agentic AI: 2025 and beyond

Looking ahead to 2025-2026, several trends indicate the continued evolution and expansion of Agentic AI:

Technological evolution

• Advanced Reasoning Capabilities: By 2025, major AI platforms like Claude 3.7 and Gemini 2.5 will combine text, audio, and image processing with enhanced reasoning for complex problem-solving.
• Multimodal Integration: Agentic systems will increasingly incorporate and coordinate across multiple modalities (text, vision, audio), enabling more sophisticated interactions with the physical and digital world.
• Collaborative Ecosystems: The development of standards like MCP will create increasingly rich ecosystems where agentic systems can discover and leverage diverse tools and services.

Market and strategic impact

• Economic Scale: Bain estimates the AI market could reach $780 billion to $990 billion by 2027, driven by demand for hardware, software, and services supporting advanced AI implementations.
• Enterprise Adoption: As implementation barriers decrease, adoption will accelerate, with leading platforms now providing template-based agent teams that businesses can customise in less than 72 hours.
• Strategic Differentiation: Organisations that successfully implement agentic AI will increasingly gain competitive advantages through enhanced decision-making, operational efficiency, and customer engagement capabilities.