FAQ: AI agents in enterprises, governance and automation

Agentic AI refers to an approach in which artificial intelligence is designed as an agent capable of independently working with goals, context, and available tools. Based on a given objective, the AI agent plans its actions, executes individual steps, and continuously evaluates whether it is moving toward the desired outcome.

An AI agent is not capable of bearing responsibility or understanding broader context beyond the data and rules available to it. Without clearly defined boundaries, high-quality data, and ongoing oversight, it may make incorrect or inappropriate decisions, which is why governing agentic AI is essential.

An AI agent can connect to enterprise systems in two main ways. It can either be embedded directly within a given application and operate in its native context of data, permissions, and process logic, or function as a separate layer that accesses systems via APIs and integration services.

An AI agent works with data as it is available at the time of its operation and is fully dependent on the quality of the source systems. The accuracy and timeliness of the data are therefore not properties of the agent itself, but the result of properly configured integration flows, data sources, and update rules.

Control over decision-making is primarily ensured by defining the goals, rules, and constraints within which the AI agent can operate. The organization determines which steps the agent can handle autonomously and where human intervention or approval is required.

Humans remain responsible for process design, setting rules, and overseeing the behavior of the AI agent. In practice, human intervention is especially critical when handling exceptions, approving sensitive steps, or adjusting how the agent operates.

With properly designed agentic AI, it is possible to trace what steps the agent performed, what inputs it worked with, and which rules it followed. Trust in its behavior stems from transparent process design, audit logs, and ongoing oversight of its activities, not from a detailed description of the model itself.

The implementation of agentic AI typically begins with selecting a specific process and clearly defining the goal the agent should achieve. This is followed by architecture design, pilot deployment, and gradual expansion into production based on the results achieved.

Before deployment, it is necessary to have a clearly defined process, accessible and high-quality data, and established integration with relevant systems. It is equally important to define responsibilities, approval rules, and how the agent’s behavior will be monitored.

Operating agentic AI requires continuous monitoring of the agent’s behavior, evaluation of outcomes, and adjustments to rules or scenarios. Changes in processes, data, or systems must also be reflected in the agent’s configuration to keep it aligned with the current reality.

Operations typically involve a combination of IT, process owners, and security or compliance roles. Each of these roles has different responsibilities, ranging from technical operation to content and rules, as well as risk oversight.

Costs include licensing or operational fees for the platform used, work related to process design and integrations, as well as ongoing operation and oversight. The total cost varies significantly depending on the chosen architecture, level of autonomy, and number of integrated systems.

Agentic AI typically delivers faster processes, reduced manual work, and better handling of exceptions in complex scenarios. The benefits are usually reflected more in improved management quality and flexibility than in direct cost savings alone.

Return on investment is evaluated by comparing total costs with achieved time savings, reduced error rates, or lower operational risks. It is important to assess benefits over a longer time horizon and in the context of the entire process, not just individual steps.

Agentic AI is not automatically cheaper or more expensive than traditional automation. In simpler processes, it may be unnecessarily costly, while in complex or frequently changing processes, it can be more efficient in the long run.

The decision should be based on an analysis of specific processes, their complexity, and requirements for flexibility and governance. A suitable first step is a pilot implementation in a limited scenario, which allows you to assess both benefits and risks before broader deployment.