Optimization of Back-Office Processes with AI Agent Implementation: A Practical Example

Marta Kuprasz — Wed, 19 Mar 2025 12:38:32 +0000

Are you looking for practical examples of AI agent applications in a business setting? In our previous blog, we discussed integrating an AI agent with email in a freight forwarding company. You can find the full text here >>. This time, we will explore how an AI-powered agent can support legal, administrative, and human resources departments. Check out our latest case study.

AI agents are intelligent systems based on machine learning algorithms. You can learn more about them in this text >>. They can be integrated with various tools such as email, ERP, or CRM systems. In this example, we will demonstrate an integration with the Slack communication platform.

Slack is used as a team collaboration tool that enables messaging, organizing discussions in thematic channels, file sharing, and integration with other business applications.

One of the available options is API integration with a selected LLM (Large Language Model) such as Gemini, ChatGPT, or DeepSeek. This time, our AI agent will be based on ChatGPT from OpenAI and Google Search.

You are surely familiar with situations where an offer or contract needs to be urgently prepared by filling in a template. Every day, employees have to draft, analyze, and negotiate various documents, including commercial agreements, supplier contracts, non-disclosure agreements (NDAs), and collaboration agreements.

Many managers wonder how artificial intelligence can automate and accelerate some of these tasks without compromising the high quality of work. The solution once again lies in implementing a dedicated AI agent that supports employees by searching for and filling in information based on data from the National Court Register.

In our example, we designed the AI agent to receive commands via the Slack platform and automatically fill in contractor details in a pre-existing NDA template. Thanks to its integration with Google Search, the agent retrieves specific data from the National Court Register (KRS), including the company’s address, tax identification number, business registry number, KRS number, and the authorized representative of the company.

Next, the assistant asks for verification of the retrieved information. Once confirmed, the employee receives the NDA with the partner company’s details filled in. The completed fields are highlighted in yellow to ensure visibility of the changes made.

This is just one of many examples of how an AI agent can support documentation and front- and back-office processes. Using our developed method, we can create a tool that prepares contracts for companies dealing with, for example, seasonal employment or those with high employee turnover.

AI agents are a relatively new topic but are rapidly gaining popularity. Many companies recognize benefits such as time savings on repetitive tasks, reduced operational costs, and increased business process efficiency. By automating routine tasks, employees can focus on more strategic and creative aspects of their work.

Moreover, AI agents can operate 24/7, eliminating delays caused by staff availability. Their applications span various fields—from customer service and legal document analysis to advanced decision-support systems in business.

Companies are increasingly adopting open language models, allowing for greater control over data and the customization of AI agents to meet specific organizational needs. As technology evolves, we can expect even deeper AI integration into daily processes, along with a growing awareness of ethical considerations and security in their use.

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Artykuł Optimization of Back-Office Processes with AI Agent Implementation: A Practical Example pochodzi z serwisu Inero Software - Software Consulting.

What are AI Agents and how can they help your company

Marta Kuprasz — Fri, 28 Feb 2025 09:51:15 +0000

The term artificial intelligence has been prominently featured in numerous publications as a solution to challenges related to efficiency, organization, and creativity. Many companies are following this trend, striving to incorporate AI-driven solutions into their offerings. These efforts take various forms. In this article, we will take a closer look at AI Agents, which can provide valuable support, particularly in back-office processes.

For some time now, we have been observing a significant rise in the popularity of terms related to the use of artificial intelligence. So, let’s start from the beginning.

What is "Artificial Intelligence"?

The term “artificial intelligence” encompasses Large Language Models (LLMs), natural language processing (NLP) systems, machine learning algorithms, neural networks, and generative AI models.

LLMs, such as ChatGPT from OpenAI or Gemini from Google, are models trained on vast datasets that can analyze, process, and generate text in a way that mimics human reasoning. They are used in various applications, ranging from chatbots and voice assistants to advanced systems supporting business analysis and process automation in companies.

Artificial intelligence is not limited to text processing. Modern models can also analyze images, audio, video, and numerical data, making them highly versatile tools in business. AI enables not only the automation of repetitive tasks but also the detection of patterns in large datasets, trend forecasting, and support for strategic decision-making in companies.

Who are AI agents?

“AI agents” are intelligent systems based on machine learning algorithms, natural language processing (NLP) models, and Large Language Models (LLMs). Their purpose is to automate processes, support decision-making, and interact with users in a natural and context-aware manner.

This means that virtual assistants are based on well-known and widely used LLMs such as ChatGPT, Gemini, Claude, Mistral, or DeepSeek, which can generate coherent responses, analyze texts, and adapt to the context of a conversation.

However, AI agents differ from language models in that they are designed to perform specific tasks autonomously. In practice, this means they are equipped with additional modules that enable them to gather information, process data in real-time, and make decisions based on business rules.

Unlike traditional chatbots, AI agents not only answer questions but can also handle complex processes, integrate with enterprise systems, and learn from user interactions. As a result, they are used in various areas, from administrative support and document analysis to the automation of operational processes in enterprises.

Also read: Meet Your Personal AI Agent – A Case Study for a Freight Company

The operation of AI agents is based on several key components:

- Communication interface – allows the agent to interact with users through text, speech, or other data formats.
- Decision engine – based on AI models and business rules, it enables situation analysis and the selection of optimal actions.
- Integration with external systems – AI agents often operate in conjunction with databases, business applications (ERP, CRM), or cloud services, allowing them to access up-to-date information.
- Process automation – they can perform specific tasks, such as generating reports, processing requests, sending notifications, or initiating predefined processes in IT systems.

What are the types of AI agents?

AI agents may take various forms depending on their application and level of autonomy. Leveraging advanced artificial intelligence models, they can assist users in a wide range of activities, from customer support to data analysis and business process management.

We can distinguish several main types of AI agents:

- Conversational agents – include chatbots and voicebots that interact with users through text or speech. They can answer questions, handle customer inquiries, and support sales processes.
- Analytical agents – specialize in processing and interpreting data. They use machine learning algorithms to analyze trends, detect anomalies, and generate reports.
- Operational agents – automate business tasks by integrating with enterprise systems. They can manage documentation, process documents, or coordinate activities within corporate processes.
- Autonomous agents – operate independently, making decisions based on collected data and predefined business rules. They are used in areas such as logistics, resource management, and dynamic operational planning.
- Decision-support agents – provide recommendations based on advanced data analysis, helping managers and specialists make strategic decisions.

Each of these types can operate independently or collaborate with other systems, creating a complex AI-driven environment. In the following sections, we will explore specific applications of AI agents and their impact on the operational efficiency of businesses.

Cloud or on-premise solution – how can an AI agent be implemented in a corporate environment?

Implementing an AI agent in an organization requires selecting the appropriate deployment model that best meets business, technical, and regulatory requirements. Companies can choose between a cloud-based solution (SaaS) or an on-premise deployment, depending on their needs for flexibility, security, and integration with existing systems.

The choice of the appropriate model depends on various factors, which are presented in the table below.

Comparison: SaaS vs On-Premise

Criterion	SaaS (Cloud)	On-Premise (Local)
Deployment model	Cloud-based (AWS, Azure, Google Cloud)	Operates on the company’s own infrastructure
Infrastructure	Cloud service provider	Local servers
Initial costs	Low	High
Operational costs	Subscription-based	Fixed maintenance and energy costs
Scalability	Very high	Limited (dependent on hardware)
Data security	Limited (processed outside the company)	High (full control over data)
Regulatory compliance	May require additional agreements and certifications	Easier to meet regulatory requirements
Ease of implementation	Easy and fast	Requires hardware purchase and setup
Updates and maintenance	Automatic, provided by the vendor	Self-managed updates and maintenance
Integration with enterprise systems	Strong API support and pre-built integrations	Full control but may require additional integration

The choice of the appropriate deployment model—cloud-based or on-premise—depends on the company’s specific requirements regarding security, costs, and integration with existing systems. Regardless of the chosen strategy, AI agents can significantly enhance operational efficiency and allow employees to focus on tasks that require creativity and strategic thinking.

The development of AI technology is undoubtedly one of the strongest technological trends in recent years. Therefore, it is worth considering now how AI agents can support your company’s growth and become a key element of its digital transformation.

We will create an AI Agent for your company.

Artykuł What are AI Agents and how can they help your company pochodzi z serwisu Inero Software - Software Consulting.

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