What is Agentic AI?

Luis Wirth

published on July 8, 2025

„Agentic AI is a framework where autonomous AI agents take on tasks independently, make decisions, and interact with tools and systems all in pursuit of a defined goal.” [1]

Differences from Workflows, Benefits for Companies & Real-World Use Cases

Agentic AI is one of the most promising concepts in the field of artificial intelligence, especially for companies working on data-driven use cases. But what exactly is it? In this article, we explain how Agentic AI differs from traditional workflows, the benefits it offers, and how businesses can leverage it effectively.

Agentic AI – Simply Explained: Definition & Principles

Agentic AI is a framework where autonomous AI agents take on tasks independently, make decisions, and interact with tools and systems all in pursuit of a defined goal.

Workflows vs. AI-Agents

Workflows follow predefined rules and steps. Example: If A, then B, then C.
AI agents operate autonomously. They analyze, plan, and act: choosing the next step based on context and the goal.

Key distinction:

Agentic AI is the overarching architecture or mindset.
AI agents are the operational building blocks within that architecture.

[2]

When to Use Agents Instead of Workflows?

Workflows work well when every step is clearly defined and predictable. But many business processes are far from that:

Multiple input possibilities
Dynamic context
Uncertain or incomplete data
Real-time decisions required

In these scenarios, autonomous agents shine: They offer flexibility and goal-driven behavior.

What Can AI Agents Do?

Modern AI agents are far more than just chatbots:

Access tools like browsers, CRM systems, or databases
Retrieve and assess information
Weigh decision options
Remember prior actions (memory component)
Choose and execute the next best action

While the user interface may be conversational, behind the scenes, agents use APIs, cloud infrastructure, or software programs to act intelligently and autonomously.

Benefits of Agentic AI for Companies

Benefit	Description
Autonomy	Agents don't require a fixed rule Set, but can adapt to their situation and reach their goal more independently
Efficiency	Handle complex tasks faster and more flexibly
Scalability	Once implemented, agents Can be applied to many processes
Tool Integration	Use existing systems and data intelligently
Collaboration	Multi-agent setups allow agents to Work in teams

[3]

Real-World Use Cases of Agentic AI

Agentic RAG (Retrieval-Augmented Generation) Unlike traditional RAG (Retrieval-Augmented Generation), which always queries the same sources, Agentic RAG intelligently adapts to the context of the prompt.

It autonomously identifies the most relevant data sources andcan formulate more complex queries to deliver accurate, actionable responses.

This makes Agentic RAG particularly valuable for:

Compliance-driven use cases, where varying regulations require adaptive retrieval strategies
Customer or stakeholder inquiries, where information needs to be pulled from diverse systems (e.g. CRM, documentation, regulatory databases)

The key advantage: Agentic RAG doesn’t just retrieve.

It reasons, selects, and refines.

That’s a significant step toward more intelligent, business-aligned AI support. [4]

Coding Agents
Example: GitHub Copilot as a coding agent that writes code, comments on pull requests, and resolves issues. [5]
Customer Service Agents
Respond to customer inquiries, access CRM data, detect escalation needs also usable in sales and marketing. [6]
Research Agents
Analyze studies, websites, or internal documentation automatically. That’s ideal for use in pharma, R&D, or market analysis.

Getting Started with Agentic AI

You already have a specific use case in mind.

Perhaps you want to automate parts of a process, reduce manual work, or better use the data you already have. But when it comes to implementing Agentic AI in practice, things often get more complex than expected. Questions about system integration, architecture, and long-term feasibility tend to slow teams down.

Agentic AI is designed to help in exactly these situations.

It allows you to move forward without having to define every detail from the start. It connects with your existing systems and builds on the data you already rely on. And it supports your use case in a way that remains adaptable as requirements evolve.

Before jumping into implementation, it’s worth understanding the basic principles. In the following posts, we will look at concrete decisions and typical challenges – based on real-world project experience.

You’ll see what matters at each stage: from evaluating use cases to making technical decisions and delivering stable, scalable results.

Final Thought: Agentic AI is the Logical Next Step for Data-Driven Teams

Whether you work in marketing, finance, IT, innovation or any other data-driven field, if you work with data, you will benefit from intelligent, autonomous AI systems.

Agentic AI goes beyond the buzz. Together, we turn it into a robust solution that delivers measurable business value.

From automation to RAG: We’ve built real GenAI systems that scale. Luis brings hands-on experience from previous Agentic AI projects.

See what’s worked and what hasn’t in our client cases:

Get in touch with us! Our expert Luis Wirth will be happy to advise you together with our team!

Sources:

[1] Atera Blog (2025.03.19): 18 Motivational Quotes on Agentic AI

[2] LangChain (accessed on2025.07.08): LangGraph Workflows Tutorial

[3] IBM Think (2025.02.24):What is agentic AI?

[4] IBM Think Blog: Agentic RAG – How it works and why it matters.

[5] GitHub Blog (2025.05.19): Meet the new Coding Agent.

[6] ThoughtSpot (2025.05.14): Agentic AI Examples.