Understanding what AI agents are and how to implement them in your organization has become essential for executives who need to evaluate the impacts of strategies on operations. 

Artificial intelligence has moved from an experimental space to playing a prominent role within companies. 

According to the Gartner, By 2028, approximately 33% of enterprise applications will include AI agents, compared to less than 11% in 2024. It is also projected that at least 15% of day-to-day operational decisions will be made by intelligent agents by the end of the decade. 

This change goes far beyond a technological transformation, but also alters operations, governance, and strategic monitoring within companies. 

What are AI agents in practice? 

AI agents are systems that interpret context, execute actions, and assist in decision-making. For these systems to function, rules, goals, and limits must be defined beforehand. 

Unlike more well-known AI models, agents are not limited to generating responses but act on corporate processes. 

With this, AI makes it possible to monitor indicators, identify deviations, generate action plans, execute follow-ups, and analyze risks in just a few minutes, meeting a greater demand in everyday life. 

And it is precisely this multipotential that transforms the capacity with which these agents operate within your organization. 

What is the difference between a chatbot, a copilot, and an agent? 

While chatbots operate reactively and copilots assist users, AI agents can autonomously execute workflows and processes. 

In general, artificial intelligence agents have a more advanced operational capability. This means they can easily interpret objectives and access systems, as well as perform tasks and consult data. 

Copilots, on the other hand, can be very well utilized simply as an assistant, supporting decisions and automating tasks, but they still need humans to tell them exactly what to do. 

Very different from agents that, according to OpenAI, are systems capable of “perform tasks on behalf of the user using tools, memory, reasoning, and action capabilities". 

Why are AI agents gaining ground in businesses? 

Companies operating with multiple systems and large volumes of data benefit from AI agents, as they replace repetitive tasks and the lack of integration between areas. 

This growth appears to be linked to operational complexity, which has been on the rise in recent years. 

According to McKinsey, organizations that apply advanced AI to operations and workflows can boost productivity by up to 401% in certain corporate functions. 

However, there are still difficulties in implementing these technologies, some of which are: 

• Lack of governance; 

• Decentralized data; 

• Disconnected processes; 

• Low operational maturity; 

• Departmental silos; 

• Lack of traceability; 

• Limited integration between systems. 

This means that before thinking about acquiring AI agents, organizations need to structure a functional operational environment so that they can act safely and efficiently. 

The biggest challenge for AI agents in companies 

A large part of the market is dazzled by the ease with which these agents operate; however, they are still treated as an isolated tool and not as part of the operational flow. 

Just like with a human agent, AI agents only operate efficiently when the environment has organized data, clear governance, and structured processes. 

Otherwise, they will just be another expensive operational tool that doesn't reflect the investment in Improvement in companies. 

This is where most C-level executives' fears lie when it comes to implementing an AI agent in their operations. 

To be perfectly clear, without a good structure, AI makes wrong decisions, data leaks occur, scalability is low, and operational control is lost, which causes many pilots to not even get past the experimental phase. 

How AI agents work within corporate management 

AI agents operate on different fronts within an organization, functioning from combinations of language models, corporate data, workflows, system integrations, and company rules. 

This means that these agents act on the organization's operational context and, precisely for this reason, the quality of information available for these agents, it's the factor that defines whether they will make a difference in your business or not. 

For this reason, platforms that centralize strategy, indicators, risks, audits, goals, and operational routines create a much more favorable environment for implementing intelligent agents. 

Key applications of AI agents in businesses 

AI agents can be created to meet different corporate needs, which means these systems can advance rapidly in strategic areas. 

Among the most interesting applications for companies, we can mention: 

• Automated strategic monitoring: continuous analysis of indicators and identification of relevant deviations from targets; 

• Automated action plan generation: Agents can suggest corrective actions based on operational history, internal benchmarks, and execution standards.; 

• Intelligent risk management: On certain platforms, agents can cross-reference KRIs, audits, mitigation plans, and indicators to predict exposure scenarios; 

• Follow-up automation and governance: Agents can update statuses, consolidate pending issues, track SLAs, and generate alerts.; 

• People management in certain programs, AI agents can assist in performance analysis, recommend PDIs and identify gaps and developments; 

• Operation and operational routine: agents can act directly on checklists, inspections, operational audits, and monitoring of critical routines. 

As we've seen, AI agents can operate on practically every front of a business, whether in the strategic, risk, or people areas, which makes these tools extremely interesting for modern organizations. 

What does it take to create an AI agent in companies? 

To create an AI agent in a company, you need to start with the technology that will be used, but not only that; it's also essential that the organization has operational maturity. 

Therefore, before even developing an agent, the company must have well-structured points such as data, governance, workflow, integration, and processes, in addition to defining traceability. 

Only after the company's structural layer is defined can we think about creating an AI agent. 

From a technical standpoint, agents generally combine: 

• LLM Models; 

• Corporate APIs; 

• Frameworks like LangChain and CrewAI; 

• Memory mechanisms; 

• Workflow orchestration; 

• Safety rules; 

• Permission control. 

NVIDIA highlights that AI agent architectures require robust observability, monitoring, and governance environments to operate at scale. 

Are AI agents a trend or a structural transformation? 

The debate about doing business with AI agents has already moved from the experimental field to the everyday reality of many companies that have realized that AI represents a new operational layer for the corporation. 

The change is comparable to the adoption of ERPs in the 1990s or analytics in the 2010s. The difference is that now automation is no longer just transactional, but involves operational decision-making capabilities. 

According to the McKinsey, Leading AI companies tend to significantly broaden their competitive advantage precisely because they can accelerate execution, reduce operational friction, and improve decision-making speed. 

In this context, agents don't necessarily replace people. 

They tend to replace: 

• Repetitive tasks; 

• Manual consolidations; 

• Operational monitoring; 

• Mechanical analyses; 

• Administrative follow-ups. 

Meanwhile, leadership is shifting focus to decision-making with higher added value. 

AI agents as part of the corporate future 

Many companies are implementing this model as a new technological tool. With this, it is expected that in the coming years these agents will part of a new model operational in companies. 

With this, intelligent agents can act continuously on strategic points, operations, risks, performance, and corporate execution. 

But there is a decisive factor: companies that already have integrated data and structured governance will be far ahead. 

Corporate AI will not be defined solely by the quality of its models. It will be defined by its ability to connect intelligence to actual business execution. 

This way, understanding What are AI agents? It's no longer just a technological discussion. Today, it's about a strategic decision related to operational efficiency, governance, and corporate execution capability. 

The market is rapidly moving towards a model where intelligent agents will act as digital operators capable of monitoring indicators, executing workflows, supporting decisions, and automating critical processes. 

However, companies will only be able to capture this value sustainably if they have an integrated, structured, and governable operational base. 

It is precisely in this context that Actio differentiates itself. 

Actio's solutions integrate AI across all fronts of a business, for example, platforms like Strategic Management of Actio integrate the company's objectives with action plans. 

If your organization is evaluating how to apply AI practically, scalably, and securely, it's worth learning how Actio's platform can accelerate this transformation.