Agentic AI: AI That Gets the Job Done 

Recently, we crossed a critical inflection point in the evolution of Artificial Intelligence. Just a year ago, Large Language Models (LLMs) felt like advanced autocomplete engines: impressive but limited. Fast forward to today, and the paradigm has shifted. What once resembled a helpful assistant now acts more like an autonomous colleague. The catalyst? Agentic AI. 

The Real Breakthrough 

LLMs are powerful on their own, but the real breakthrough comes when you give them goals, context, and the ability to act independently. That’s Agentic AI. AI that doesn’t just respond to prompts, but takes initiative, executes, and completes tasks end-to-end. As Marcel de Vries, Global MD and CTO Microsoft Services at Xebia, puts it: “AI has evolved from a helpful assistant to a seasoned professional. One that drives progress, collaborates across teams, and delivers real results. That’s the leap we’re experiencing right now with Agentic AI.” 

What Is Agentic AI?  

Agentic AI is about delegation, not just interaction. It’s AI that gets the job done. Imagine receiving an email requesting a revenue report. An AI agent can read it, pull data from spreadsheets, run calculations, format the report, and send it for approval. Autonomously. Other examples:  

• Marketing agents draft social posts, choose optimal publish times, source images, and post them. 
• Business travel agents find flights, book hotels, and create itineraries based on your preferences. 

Workflows that once required hours of manual effort can now be handled independently by AI. Crucially, human approval remains in the loop, ensuring accountability and supporting responsible AI use. 

What Does This Mean for Software Development?  

To understand the shift from ‘just AI’ to ‘Agentic AI’ in the Software Development Lifecycle (SDLC), let’s zoom in on GitHub Copilot. Once a code-suggestion tool, it has evolved into a full agentic system, enabling developers to offload tasks like refactoring, test generation, documentation, and pipeline management. As a result, teams can focus less on repetitive coding and more on architecture and innovation. 

In DevOps, Agentic AI goes further. AI agents monitor pipelines in real time, proactively detecting issues, resolving bottlenecks, and ensuring smooth, continuous delivery. This is Agentic DevOps, where AI acts as an autonomous partner, not just a helper. 

More examples of Agentic AI across key SDLC stages: 

• Requirements & Planning: AI agents analyze past project data and stakeholder input to help define clear, achievable requirements and generate project plans with risk assessments. 

• Coding: Beyond autocomplete, Agentic AI refactors entire modules, writes boilerplate code, and generates unit and integration tests automatically. 

• Code Review: AI agents perform deep analysis to spot security vulnerabilities and compliance issues, then suggest or apply fixes autonomously. 

• Testing: Agents generate and execute test cases, adapt tests as the code evolves, and even simulate user behavior to identify edge cases. 

• Build & Deploy: AI monitors CI/CD pipelines, predicts failures, resolves conflicts, and optimizes resource allocation for faster releases. 

• Monitoring & Maintenance: Post-release, AI agents detect anomalies, predict downtime, and can trigger automated rollbacks or patches before issues impact users. 

Leaders at a Crossroad  

Today’s leaders are standing at a crossroad. De Vries, “Some are excited about Agentic AI. Others are hesitant. That’s natural. Change is uncomfortable, especially when you’ve built your current success on the old rules. But with AI, the rules have changed.” He continues, “In this new world, innovation will not come from top-down mandates or isolated innovation labs. It will emerge everywhere. Your new role as a leader is to enable experimentation. Provide safe, secure tools, run internal hackathons, and get every department, from HR to Finance to Operations, hands-on with AI agents. Once your teams experience what’s possible, the value becomes undeniable.”  

Are You Ready?  

With Agentic AI, the nature of work hasn't changed, but the way we work is, drastically. Tasks that once took hours of manual effort, like analyzing data, or just simply, preparing for a meeting, can now be completed instantly by AI. The output might look the same, but the process behind it is entirely different.  

This shift, from manual effort to intelligent automation, is only accelerating. Agentic AI isn’t on the horizon. It’s already here. The question is: are you ready? 

At Xebia, we guide organizations through this shift. From vision and strategy to cloud platforms and AI development environments, we help you lay the foundation for agent-powered transformation.