For years, organizations have treated AI as a tool that helps employees do daily work — writing emails, summarizing meetings, generating code, answering customer and employee queries, and analyzing data.
But this model is beginning to change as artificial intelligence moves from being a tool in the employee’s hand to becoming the employee himself through intelligent agents, which are autonomous systems capable of managing entire projects and automating supply chains without daily human intervention, which reshapes organizational structures.
From digital assistant to virtual employee
Agents differ from traditional assistants in the nature of the tasks they perform. The assistant waits for the user’s instructions, carries out the specified request, and stops when the task is finished.
While the agent starts from a general goal set by management, he formulates the plan, divides it into steps, chooses the appropriate tools, follows up on implementation, and evaluates the results, with the possibility of re-adjusting the plan if he encounters obstacles during the work.
The agent can use various business applications, such as customer relationship management systems, enterprise resource planning, email, meeting tools, and databases, making him an active element within the company’s operational structure.
Many major factors helped accelerate this change, such as the development of artificial intelligence models’ capabilities for multi-step thinking and the emergence of protocols that allow these models to be linked to institutional tools and systems, in addition to increasing pressure on companies to raise productivity and reduce costs.
A report issued by Deloitte entitled “The State of Artificial Intelligence in Enterprises” describes this change as a transition from systems that provide recommendations to systems that get work done within the organization, which no longer views agents as a future experience, but rather as part of digital transformation strategies over the next few years.
The birth of the software employee
The idea of the software employee has gradually begun to become a reality within institutions, heralding the emergence of an economy of virtual employees who are able to manage complete processes starting from analyzing data and ending with implementing procedures within institutional resource systems.
The agent may obtain an official e-mail address or an institutional digital identity, depending on the nature of the system, which gives him a legal personality within the company’s system.
On some platforms, the agent gets an encrypted wallet or spending limits via virtual cards assigned to specific tasks, such as managing an ad campaign with a specific budget that aligns with company rules, performance indicators and compliance policies.
The software employee differs from the traditional employee in that he does not need an office, a monthly salary, health insurance, or annual vacations, but he is linked to operating, maintenance, and computing costs that differ from the costs of human employees.
This software employee works around the clock without interruption, runs dozens of processes in parallel, and switches between multiple tasks within a few seconds, giving organizations the ability to massively expand their operations without a corresponding increase in human structures.
With this expansion, performance indicators within companies began to change, and instead of measuring employee productivity only, departments began measuring agent productivity, operating costs, speed of task execution, and the percentage of errors.
Current experience suggests that real economic value is achieved when humans and agents work in hybrid teams, in which agents handle repetitive and analytical work, while humans reserve tasks that require creativity, complex negotiation, understanding context, and sensitive decision-making.
When humans become a minority within the hybrid office
Historically, companies arranged their management structures as a result of human constraints related to the distribution of powers, internal control, decision-making, division of responsibilities, and the limits of human supervision, which led to the emergence of hierarchical structures and middle managerial layers.
But the entry of agents into the work environment changes this traditional model, replacing it with what is known as the hybrid administrative structure, which redefines the organizational structure.
In some future scenarios, you may find that the department manager is the only human element, while his entire team consists of specialized agents. The sales manager may manage different agents who analyze market trends, suggest prices, negotiate with customers, track inventory, and prepare daily reports.
In this case, the role of the human manager himself changes, and instead of focusing on following up on the implementation of daily work manually, his role becomes closer to designing the digital work environment, determining the powers of agents, monitoring the quality of decisions, and managing risks, while intervening in exceptional cases or sensitive strategic decisions.
As a result, a new management challenge emerges: knowing when to grant an agent full autonomy and when to impose strict human review, with new positions expected to emerge in the labor market in the coming years, such as agent manager and digital workforce governance officer.
Agents who manage operations
Companies are moving towards building business models that rely increasingly on agents, with each agent handling part of the process before passing the task on to another agent so that the work cycle is completed without human intervention except when necessary.
Deloitte notes that this model is beginning to appear in the areas of customer service, knowledge management, research and development, cybersecurity, and supply chain management.
Supply chain management stands out as a clear example of this, where multiple agents working simultaneously review inventory levels, forecast demand, issue purchase orders, select suppliers, and track shipments automatically, while reporting back to the human manager when an exceptional situation arises that requires a strategic decision.
Microsoft is investing in this field through the Microsoft Agent 365 platform, which aims to enable organizations to build and deploy agents within their operational environments, while Salesforce has redirected some jobs towards positions related to the development and management of artificial intelligence technologies and agents.
Walmart has advanced the deployment of AI agents across the entire supply chain, including inventory management, optimizing pickup routes, and automating negotiation with suppliers.
Through its partnership with Pactum AI, Walmart succeeded in automating negotiations with suppliers, achieving agreements with 68 percent of the suppliers contacted by the agent while reducing costs by 1.5 percent.
As for Amazon, it launched the “Amazon Connect Decisions” platform, which is an integrated artificial intelligence agent tool for supply chain planning that brings together more than 25 specialized tools in the form of artificial intelligence agents, which undertake operational calculations and analyzes on behalf of human planners.
In addition to this is the “Project Ilona” agent system developed by Amazon to improve workflow in fulfillment centers, in addition to the “Deep Fleet” generative artificial intelligence model that coordinates the movement of the entire robotic fleet.
While some European banks began testing teams of agents to manage “know your customer” processes and update old systems, which reduced time and effort by record levels.
What happens when an agent makes a mistake?
Imagine that an agent authorized to make purchases on behalf of the company misinterprets data, makes a multi-million dollar deal with an unqualified supplier, transfers money to the wrong account, or signs a contract that violates corporate policy.
In this case, the agent cannot be held legally accountable because it is just complex code, and legal systems around the world are built on the idea that the agent in the world is either a natural person or a legal person.
As a result, developers focus on building strict restrictions and oversight mechanisms into the agent’s architecture since its design, so that it cannot perform any high-risk action without going through additional verification stages or human approval.
Sarah Breeden, the Bank of England’s Deputy Governor for Financial Stability, raised concerns that financial systems’ reliance on agents capable of carrying out operations independently may make relying on human supervision alone insufficient and require the development of new regulatory frameworks, such as kill switches and rapid recovery mechanisms.
New challenges with every additional power
Giving agents access to email, accounting systems, and databases opens up new vulnerabilities for attackers who try to fool the agent themselves with carefully crafted commands, or exploit what are known as “instruction injection” attacks, which prompt the agent to perform actions that were not intended.
As a result, a new concept is emerging in the world of cybersecurity called “Know Your Agent”, which calls for treating agents as if they were independent digital users by constantly verifying identity, permissions and behavioral history and applying zero trust principles, so that no agent is granted permanent or unjustified powers.
Are jobs disappearing or changing?
With every major technological leap, fears of job loss return to the fore, but current data confirm that the impact is more complex than just a direct replacement of humans.
Jobs that depend on performing repetitive, routine procedures, such as data entry, periodic reporting, and order follow-up, are more vulnerable to fading or radical change.
In contrast, there is an increasing need for functions related to designing agents, building governance frameworks, auditing decisions, managing risks, and auditing agent systems.
However, global research organizations such as Gartner warn against being too optimistic, predicting that more than 40% of agent projects will falter by 2027 due to high operating costs, lack of clear business value, over-expectations, or poor enterprise data readiness.
This means that the transition towards an agent economy will not be quick or equal across all sectors, but rather depends on the ability of institutions to re-engineer their internal processes and build strict governance models that ensure that artificial intelligence remains a complementary partner to the human element, and not a substitute for it.
In conclusion, current trends indicate that the most successful organizations will not be those that replace humans with agents, but rather those that succeed in building hybrid work teams that combine the two within a clear framework of governance and accountability.
