What Is Agentic Engineering?

You know vibe coding. You've seen what AI generates.

The field has evolved. This evolution: "Agentic Engineering". How is it different?

From Vibe Coding To Agentic Engineering

Vibe coding was a starting point. You prompt an AI model, look at what comes back, and use it if it seems good enough. For quick prototypes and throwaway scripts, that works okay. However, for production software, "okay" is not the standard. Code that looks correct can still break under edge cases, introduce security vulnerabilities, or degrade the architecture of your codebase.

Agentic Engineering takes a different approach. Instead of accepting AI output on instinct, you direct the process, review the results, and build checks into your workflow.

Think of it as directing a team of engineers under you. They move fast and get a lot done, but you're the one making sure the work holds up to the standard.

Simple Definition

That brings us to a simple definition:

Agentic Engineering is the process of developing software by directing AI agents to write and test code while you guide technical decisions and verify the results meet your requirements.

The key word is "directing." You are not prompting and hoping. You are setting objectives, defining constraints, and evaluating output the same way you would review a pull request from a teammate.

What This Looks Like In Practice

• Give an agent clear instructions and acceptance criteria
• Evaluate the architecture, design, dependencies, tradeoffs
• Verify the code meets your standard and requirements
• Actively identify issues as they arise, add checks to prevent recurrence
• Use a multi-agent pipeline for automated execution (optional)

Each of these steps mirrors what a strong engineering leader already does. The difference is that the team members writing the code are AI agents, and the feedback loop between you and them is faster than a human team can operate.

The Role Of Automation

Automation is not required, but it does vastly speed up the process. It comes at a risk, though. Without a robust process, automation only speeds up low-quality generations.

This is a common mistake. Engineers see how fast agents can produce code and immediately try to wire up pipelines, orchestrators, and multi-agent systems. But speed without quality control is just technical debt on a conveyor belt.

First learn the fundamentals well, then automate.

What Comes Next

Hallucinations are covered next and then First Principles Thinking. Understanding where AI agents go wrong is essential before you can build a process that reliably catches those failures.

September, 2026