Table of Contents

• From Responding to Acting
• What Makes an AI Agent Unique?
• A Real-World Example: Automating Employee Onboarding
• Use Cases Across the Business
• The Technology Behind It
• From Technology to Digital Teammate
• Final Thoughts

Imagine arriving at the office and finding your digital assistant already at work. It’s checked your emails, prepared a weekly performance report, responded to a customer inquiry, and scheduled a team meeting. Not because someone instructed it step by step, but because it knew what needed to be done to help you reach your goals.Welcome to the era of AI agents—a new generation of intelligent systems that are radically transforming how artificial intelligence integrates into real-world business processes.

From Responding to Acting

Until now, we’ve interacted with AI systems that could answer questions, generate content, translate text, or create images. But even the most advanced models have remained largely static—they respond, but they don’t act. As outlined in the paper “Agents” (Google, 2024), AI agents break through this boundary. They’re designed to actively interact with the external world—business systems, databases, web services, calendars, APIs—enabling them to make decisions, plan actions, and complete tasks autonomously, all while pursuing specific goals​.

What Makes an AI Agent Unique?

According to the whitepaper, an AI agent combines three key elements:

1. A model, typically a large language model (LLM), is responsible for understanding requests, reasoning, and generating outputs.
   
2. Tools that allow the agent to interact with external systems.
   
3. An orchestration layer manages the decision-making process and organizes tasks into a coherent flow​.
   

Among these tools, APIs (Application Programming Interfaces) play a central role. Think of an API as a standardized channel through which different software systems can communicate. For AI agents, APIs serve as bridges to external systems, enabling actions like retrieving data from a CRM or sending an email.

A Real-World Example: Automating Employee Onboarding

To better illustrate the potential of AI agents, let’s consider a common corporate process: employee onboarding.

Traditionally, onboarding a new hire involves:

• Creating corporate credentials
  
• Sending out contract documents
  
• Assigning software licenses
  
• Scheduling a welcome meeting
  
• Notifying the team
  
• Updating the HR system
  

An AI agent can manage this entire flow autonomously. Once the hiring is confirmed, the agent can:

1. Retrieve the new hire’s information from the HR database
   
2. Trigger the creation of credentials through the IT system (via an API)
   
3. Send a customized welcome email with relevant documents
   
4. Schedule meetings using the company calendar
   
5. Assign access to the necessary software tools
   
6. Log the completed steps back into the central system
   

All of this is done seamlessly, without manual intervention, through coordinated reasoning and interaction across multiple platforms​.

Use Cases Across the Business

The whitepaper highlights how AI agents are being deployed across a variety of business areas:

• Customer Service: respond to support queries, access order history, and update client records in real time.
  
• Marketing: analyze campaign performance, generate personalized content, and launch multi-channel initiatives.
  
• Sales: provide intelligent suggestions during client calls, manage real-time offers, and sync data with CRMs.
  
• Operations: automate document workflows, schedule tasks, process invoices.
  
• Human Resources: handle onboarding, respond to FAQs, recommend learning paths​.

The Technology Behind It

Three core technologies empower AI agents to interact with the outside world:

• Extensions: direct connections to external services that the agent can use to perform live actions (e.g., calendar updates, email responses).
  
• Function Calling: enables the model to generate function calls that are executed on the client side, ideal for security-sensitive environments.
  
• Data Stores: dynamic repositories that provide access to structured or unstructured data, often used in Retrieval Augmented Generation (RAG) scenarios​.
  

These tools allow agents to access fresh, context-aware information, reason effectively, and act with precision.

From Technology to Digital Teammate

A well-designed AI agent isn’t just a tool—it’s a digital collaborator that supports complex processes and adapts to real-world needs. As the whitepaper notes, each agent must be tailored to its environment through continuous improvement and domain-specific knowledge​.

Final Thoughts

AI agents represent the next evolution of artificial intelligence—one where models don’t just think, they act. By combining reasoning, planning, and external interaction, they are no longer passive tools but proactive contributors to business goals.

At Neodata, we see AI agents as enablers of genuine transformation. They help organizations extend their cognitive capabilities, automate intelligently, and unlock new forms of value across departments.

This is more than a shift in technology. It’s a new mindset—one where AI becomes a true partner in decision-making, execution, and innovation.