In the world of technology, the year 2026 will probably go down as the moment when the definition of ‘user’ changed permanently. For years, we took it for granted that there was a human on one side of the screen and a machine on the other, executing commands. Today, this boundary is becoming fluid. The advent of autonomous agents, capable of operating autonomously in network and transaction systems, means that artificial intelligence is no longer just a tool in the hands of an employee. It has become a new autonomous link in the structure of an organisation.
Autonomy mechanism: Out of sight
The evolution from simple language models to agent-based systems such as OpenAI Atlas has changed the dynamics of working with data. Today’s business environment is based on processes where AI not only generates reports, but can call APIs on its own, manage logistics or interact with external ecosystems. This shift from a ‘question-answer’ to a ‘goal-implementation’ model takes the burden of repetitive tasks off teams, but also introduces a new layer of complexity.
In this set-up, the so-called process debt becomes a challenge. It arises discreetly when the automation of successive work steps is carried out without full insight into the logic behind the decisions made by the machines. Unlike human errors, which are usually visible immediately, AI-based system errors can accumulate for years within an organisation as small, undetectable deviations, affecting the ultimate profitability of operations in ways that are difficult to diagnose unequivocally.
A shift in security: From blocking to identity management
As AI agents become more autonomous, the traditional approach to security, based on static filters and firewalls, seems to be losing relevance. In 2026, the discussion about protecting corporate assets is shifting towards AI Access Fabric – a concept in which each AI process has its own verifiable identity.
Instead of asking ‘how to block AI’, organisations are starting to look at ‘how to empower it’. The modern approach is that an AI agent acting on behalf of a company should be subject to the same rigours as any other system user. Classification of data at source and isolation of risky sessions inside agent browsers are becoming standard elements of digital hygiene. This maintains operational fluidity while reducing the risk of external malicious data sources manipulating the model.
The new role of corporate governance
The integration of AI systems into the company’s bloodstream has meant that the management of their status (AI-SPM) has naturally become part of the wider corporate governance framework. Compliance with standards such as NIST or ISO is no longer seen as a bureaucratic requirement and is beginning to be regarded as a foundation for operational stability.
Traceability is becoming a key element of this new structure. The traceability of an agent’s decision path – from data intake to analysis to final action – is today not only a security issue, but also a matter of business transparency. Organisations that rely on transparent workflows are building error-proof systems that cannot be detected by the naked eye.
The perspective of tomorrow: Strategic symbiosis
Observing the business landscape of 2026, it is hard not to get the impression that success no longer depends on the sheer scale of AI implementation, but on the quality of the architecture on which it is embedded. Artificial intelligence, operating in a predictable manner and subject to clear governance rules, becomes a catalyst for growth that does not burden organisations with unforeseen risks.
In this new paradigm, the role of business leaders is evolving. Instead of overseeing technology, they are designing an environment where people and autonomous agents can collaborate within secure, auditable and understandable rules. This is not a revolution in security, it is a new definition of digital maturity for the modern enterprise.
