AI Agents in Practice: How Intelligent Assistants Boost Productivity

In today’s episode of Business Tech Talks powered by BlueSoft, we explore the concept of Agentic AI—often described as the next major step in the evolution of artificial intelligence, following predictive and generative AI.

Together with our guests—Ula Oleszek from Microsoft and Paweł Bura from BlueSoft—we discuss what makes AI agents different from traditional chatbots, how autonomous systems make decisions, and what it takes to implement them safely in real business environments. We also examine practical use cases, data security considerations, and the legal responsibility associated with autonomous systems.

Below is a detailed summary of the conversation.

The Evolution of Artificial Intelligence

Artificial intelligence has evolved through three major stages:

• Predictive AI, focused on analyzing data and forecasting outcomes
• Generative AI, capable of creating content such as text, images, or code
• Agentic AI, the newest stage, where systems can independently plan and execute tasks

What distinguishes an AI agent from a typical chatbot is agency—the ability to make decisions, plan actions, and pursue a specific goal rather than simply responding to user prompts.

Two broader concepts are also important here:

• Agentic AI – an environment where multiple AI agents can collaborate.
• Agent-based systems – complex digital ecosystems (sometimes connected to the physical world through robotics) where agents work together toward shared objectives in dynamic—and sometimes unpredictable—ways.

A helpful metaphor discussed in the episode compares these technologies to construction work:

• An agent-based system is the entire team building a house under the supervision of a project manager.

• Generative AI is like a toolbox.

• An AI agent is a specialized professional—like a plumber.

Capabilities of AI Agents and Real-World Applications

One of the most powerful features of AI agents is their ability to use tools. This can include logging into systems, accessing databases, or executing operations in external applications.

Thanks to Natural Language Understanding (NLU), modern agents can interpret user intent far more effectively than traditional chatbots.

Example use cases include:

• Banking

An AI agent supporting a call center could independently check a customer’s account balance, execute a transfer, or even assess creditworthiness. It could analyze data across multiple systems—and potentially social media—before making a recommendation about granting a loan.

• Process Automation

Agents are especially useful for automating complex workflows that cannot be fully defined by rigid rules. Instead of following a fixed script, they can dynamically adapt when unexpected situations arise.

AI Implementation and the Role of Employees

The experts strongly emphasize that organizations should not replace entire departments overnight.

Implementing an AI agent should resemble onboarding a new employee—starting with small tasks and gradually increasing the level of autonomy.

Examples include:

• Accounting

An AI agent can process and categorize invoices around the clock. However, final quality control and document approval should still remain with human employees.

• Software Development

Tools like GitHub Copilot can automate repetitive tasks such as writing tests, allowing developers to focus on more creative and complex aspects of software development.

In this sense, AI agents function as “turbo tools.” Just as a chainsaw increases a lumberjack’s productivity, AI can dramatically enhance human efficiency—without replacing human expertise.

How Companies Should Approach Implementation

Introducing agent-based systems requires a structured approach. Organizations should consider several key steps:

1. Identify clear use cases
Determine which business processes could benefit most from automation.

2. Prepare the data environment
AI systems depend on high-quality data and a reliable platform for processing it.

3. Establish monitoring and governance
Companies need mechanisms to audit how agents operate and determine who is responsible if something goes wrong.

4. Address legal requirements
Organizations must consider regulations such as the AI Act, as well as industry-specific compliance requirements.

Microsoft’s Technology Ecosystem

Microsoft provides several tools that support companies at different stages of AI adoption:

Copilot Studio
A low-code platform that allows organizations to build their own agents integrated with tools such as Microsoft 365, Teams, or SharePoint.

AI Foundry
A development environment designed for building fully customized and advanced AI agents.

Marketplace
A platform offering ready-made partner solutions that can be integrated with existing business systems.

Key Challenges: Trust, Errors, and Security

One topic discussed in the episode is the decline in managerial trust in AI, which dropped from 43% to 27% within a year. This trend largely reflects the statistical nature of AI systems—they can and will make mistakes.

The recommended approach is iterative implementation: testing, monitoring performance, and accepting that no system will be perfect from day one.

Security and accountability are also critical considerations:

• Data protection

Using enterprise cloud environments such as Microsoft Azure ensures that company data remains secure and is not used to train public AI models.

• Legal responsibility

AI agents themselves do not have legal personality. Responsibility for their actions ultimately lies with company leadership or with the individual who approves the results generated by the system.

Organizations should avoid situations where employees are required to approve large volumes of AI-generated outputs without a realistic ability to verify them.

Final Thoughts and Recommendations for Leaders

Polish companies are currently in a strong position to adopt AI. The market has not missed the technological shift—but organizations must act now to avoid falling behind.

The most practical approach is to start with an MVP (Minimum Viable Product): implement a small solution, learn from it, and gradually expand automation capabilities.

For both employees and leaders, the best way to understand AI is through hands-on experimentation—for example, by exploring free AI chat tools while respecting data privacy rules.

Direct experience quickly reveals the real potential of this technology—and how it can transform the way organizations operate.