According to a new blog by Anekanta, the shift from insight to agency is the defining pivot of 2026, creating new opportunities and new risks for organisations. The piece explores the Good, Bad and the Ugly of Agentic AI, and gives insight into what it means and how it works in practice. This is an extract with a link to the full article, below.

The first wave of Generative AI (Gen AI) largely focused on analysis and content generation. Systems could analyse data, summarise reports, generate text, images, code or assist users with queries. In almost all cases, involvement of Gen AI in business activities needed to be initiated and the outputs used or controlled directly by humans.

Agentic AI changes that model.

Rather than simply responding to prompts, AI agents can be instructed to plan a chain of tasks, select tools and execute actions across systems. Instead of producing information alone, the system may now initiate and complete work. Agentic AI allows organisations to move beyond edge productivity and narrowly defined automation, towards enterprise wide, outcome-driven operations, where systems can coordinate tasks and execute actions to achieve goals.

For organisations already familiar with automation technologies such as Robotic Process Automation (RPA) or machine-to-machine (M2M) integrations, the concept is not entirely unfamiliar. Whilst RPA is about compliance to a path, Agentic AI is about commitment to a goal. The delegated decision-rights and operational autonomy of agentic AI systems represent a significant step forward.

As with many technological advances, the emergence of Agentic AI presents both opportunities and risks.

Understanding the distinction is now becoming an important leadership issue.

What actually makes AI “Agentic”?

Traditional automation systems operate using deterministic workflows. These workflows follow predefined logic that is predictable and bounded.

For example: If invoice > £10,000, then route to finance director for approval. Every possible pathway is defined in advance. Agentic systems operate differently. Instead of following fixed instructions, the system is given a goal and determines how to achieve it.

Agentic AI systems typically demonstrate three characteristics:

• Autonomy – the ability to initiate and execute tasks through accessible software tools
• Goal-driven reasoning – the ability to determine how to achieve an objective
• Adaptability – the ability to adjust behaviour in dynamic environments

This means the sequence of actions may not be predetermined.

The potential benefits of Agentic AI are discussed in a recent paper published by the European Commission supporting the use of agentic systems to streamline complex workflows by orchestrating tasks across multiple systems and automating routine decisions.

For example, an AI procurement agent tasked with securing the best supplier contract might: analyse previous purchasing, data, search external supplier markets compare price and reliability indicators, request quotes initiate procurement documentation. The exact path taken by the system can vary depending on the circumstances. In other words, the system is reasoning about how to achieve an objective, a step beyond simply executing predefined instructions.

This is what distinguishes agentic AI from traditional automation.

The Good: AI that gets work done

The potential advantages of Agentic AI are significant. Rather than assisting humans with individual tasks, AI agents can potentially complete multi-step processes across systems, where specialised agents coordinate subtasks and tools to achieve a larger objective.

For example, an agent could:

• Retrieve relevant operational data
• Analyse compliance requirements
• Generate documentation
• Submit reports
• Escalate issues where required

This type of automation may extend beyond what traditional workflow systems can achieve.

For organisations operating large digital infrastructures, the ability to automate complex workflows may deliver significant efficiency gains.

Agentic commerce (Banco Santander & Mastercard)

In the financial sector, early experiments are emerging around agent-driven transactions, where AI systems initiate and execute actions within tightly controlled environments.

One of the clearest signals that Agentic AI is moving from theory to practice emerged in early 2026 when Banco Santander and Mastercard completed Europe’s first live end-to-end payment executed by an AI agent within a regulated banking framework.

In the pilot, the AI agent was able to initiate and complete a purchase transaction on behalf of a user through Mastercard “Agent Pay” infrastructure. The transaction was executed on live banking rails under controlled conditions, demonstrating that AI systems are now capable not only of recommending purchases but also of executing financial transactions autonomously within defined governance limits.

The experiment marks a significant milestone in what some analysts describe as “agentic commerce,” where AI systems search, negotiate, and transact on behalf of humans. While still experimental, the development suggests that financial institutions are actively preparing for a future in which non-human actors participate directly in economic activity.

Cybersecurity Implications – Agentic AI as both defender and attacker

Agentic AI is also beginning to reshape cybersecurity operations. Research suggests that autonomous AI agents could eventually assist security teams by automating tasks traditionally performed in Security Operations Centres (SOCs), including threat detection, vulnerability analysis, and incident response.

As agent architectures become more complex – combining reasoning, memory, and system integration – traditional cybersecurity frameworks may struggle to fully address the emerging risk landscape.

Read the full piece, here

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