Agentic AI: When Software Starts Working Towards a Business Goal

Artificial Intelligence (AI) has transformed the way of businesses handle information. Generative AI like ChatGPT, Claude, and Perplexity have the ability to answer the questions and create the content based on a prompt provided by the user. However, the business automations necessitate more beyond a corresponding response. 

This is where Agentic AI comes into play. It reframes the discussion about what an AI model can produce to what the AI system can achieve towards a certain objective. For businesses, the technology's efficacy will be determined by how well it is incorporated into the current processes. 

What Makes Agentic AI Different?

Agentic AI is a qualitative leap of AI development, particularly designed to work towards a goal by planning steps through the exploitation of available tools and responding to the outcomes. An agent could analyze the given information and determine what steps to take next. 

The traditional AI systems usually consist of programmed algorithms and they are more rule-driven. The supervised-learning models address narrow problems. Meanwhile, the Agentic AI Solution has autonomy and adaptability built-in to tackle diverse issues.

As a result, in this case, the main distinction between the AI agent and agentic AI is not that the first one creates while the second one acts.

How Does an AI Agent Complete a Task?

An AI agent needs more than a language model to operate in a business environment. To carry out its work, it may use external tools, business data, defined instructions and a control mechanism.

A typical workflow follows this pattern.

1. Understand the goal

2. Reasoning and Planning

3. Tool usage and Integration

4. Check the result

5. Complete the goal

The process can vary depending on the design of the system. A properly designed agent should also identify when there is missing information, a tool returns an error, and the action is not within its limits. 

Where Can Businesses Apply Agentic AI?

The best use cases start with a process that has a clear business goal. The technology should solve a particular challenge and not be implemented simply because it is available.

1. Customer Service

A customer may contact the company about a delayed order. An AI agent can find the order details, read the delivery status information and prepare an appropriate response. If the company allows for it, the agent can take further action permitted in the system to update the order.

Refunds and other sensitive operations still require an employee’s approval to complete. This way, the process remains compliant with the company’s policies.

2. IT Operations

IT teams often investigate alerts by reviewing relevant logs and incidents. An agentic system can collect all necessary evidence and suggest the possible resolution. With access and permissions allowed, it can perform the resolution steps. The extent of resolution depends on the data available to the system and actions permitted by its access.

For businesses exploring Agentic AI development, these use cases highlight the importance of connecting AI capabilities with existing systems and operational requirements.

3. Business Process Management

Some processes require multiple approvals before completion. An agent can track the progress, collect the required information and take the next permitted step. For example, a purchase request may need a budget approval before any purchasing can happen. An agent can request the needed information and move the process to the next stage. The purchasing itself still requires a manager’s approval unless the company allows otherwise.

Single Agents and Multi-Agent Systems

A single agent may be sufficient to perform a limited number of actions, but more involved processes may require an entire team of workers with different duties. Within a multi-agent platform, one software can be used to collect all of the necessary data, whereas the another one can be tasked with evaluating it against the given criteria. The information can then be processed by either the same component or a different one, according to the specified instructions.

Such a strategy can be applied to break down a multi-step procedure into discrete elements, each of which is easier to execute and verify. However, the more agents are used, the more room there is for errors, rework, and additional expenditures on computation time and resources.

It is always rational to design the architecture around the minimum viable solution that would still allow the desired outcome. In some cases, a traditional automation  workflow may be preferable to a multi-agent framework owing to its simplicity and reliability.

What Should Businesses Check Before Adoption?

Introducing the Agentic AI presents several significant challenges to existing operations in that it can interact with business systems and impact real operations. A proof of concept is not enough to demonstrate that it is ready for everyday use.

Businesses should consider three key aspects before rolling out such a system.

• Access to systems: It is paramount that agents are given only the permissions needed to perform their tasks and that sensitive operations require appropriate levels of authorization.

• Information quality: Wrong, erroneous or out of date information can lead to poor performance. Information relevant to the agent’s tasks must be available to it at all times.

• Performance management: Teams must be able to monitor completion of tasks, any mistakes made, costs incurred in processing and other deviations in performance. Logging and escalation procedures must be in place to allow for post-processing analysis of aberrations.

In addition, human oversight must be built into the processes from the start. Businesses need to define what actions can be taken automatically and which ones must be referred to humans. By doing so, they can ensure that everyone knows the limits of the AI’s capabilities.

Building the Right Solution With Osiz’s Agentic AI Solution

Agentic AI is not a silver bullet for all applications, and it is not a replacement for every existing business process. It has its place in an organization and is most effective when used to perform specific tasks across different systems that make up an organization’s existing technology stack. Some processes will be best automated by using a single agent, while others are better handled through traditional automation, human judgment, or a mix of these approaches.

The first step in pursuing an agentic AI solution is to understand where the value will be, what current processes can be automated, where time is spent pushing information between systems, and where decisions are made according to well-defined rules. At this point, it will become possible to decide which technical solutions are the most appropriate within this framework and what requirements they must satisfy.

As an Agentic AI development company, we focus on building solutions around specific business needs. Developing an effective Agentic AI system means more than enabling an agent to perform a task. It requires the right system design, alignment with business rules, careful handling of unexpected situations, and results that can be verified.

Every Business Has a Problem. We Build the Solution.