Agentic Engineering
Agentic Engineering refers to the discipline of building AI systems that independently pursue goals, make decisions, and execute multiple steps in sequence – without a human instructing every single step. It is the difference between a tool that responds on command and a system that sees a task through to completion on its own.
Most AI applications work like a simple question-and-answer machine: a human asks a question, the machine answers, done. Agentic Engineering goes further. It involves building systems that are given an overarching goal and then independently plan how to achieve it. The system carries out many individual steps one after another, observes the results along the way, and adjusts its plan – until the task is completed. Such a system is called an AI agent. The term “Agentic” comes from the English word “agency,” meaning roughly the capacity to act or take initiative.
Why AI agents make a difference
A classic language model – that is, a program that understands and generates text – can only answer what is directly asked of it. It has no memory across multiple conversations and cannot trigger anything in the real world. An AI agent, by contrast, can write and send emails, search databases, execute code, or access websites – all one after another, on its own initiative.
This makes a huge difference in practice. Tasks that used to require many manual intermediate steps can now be fully automated. A company no longer has to make dozens of individual requests; it gives the agent a goal and waits for the result. For developers, however, this also brings new responsibility: a system that acts on its own can also make mistakes on its own – and several at once.
How an AI agent works internally
The core of an agent is a loop of four steps: observe, plan, act, check. First, the agent takes in its current situation – for example, the content of a webpage or the result of a previous computational step. Then it considers what makes sense to do next. After that, it executes a step, such as entering a search term or calling a function. Finally, it checks whether it has gotten closer to its goal.
For an agent to be able to act at all, it needs so-called tools – that is, programs or interfaces it is allowed to call. An interface, often called an API, is a clearly defined channel through which two programs communicate with each other. Which tools an agent is allowed to use is determined by the developer. This is one of the central decisions in Agentic Engineering: limiting what an agent can do so that it doesn’t accidentally trigger something undesired.
Some systems don’t consist of a single agent but of several working together. A so-called orchestrator agent breaks a large task down into subtasks and delegates them to specialized agents. This architecture resembles a project team: one coordinator, several specialists. Agentic Engineering therefore also encompasses the question of how to reliably build and control such networks of agents.
Where Agentic Engineering already appears today
The best-known examples are systems like Devin, an AI assistant for software developers. Devin receives a task such as “Write an app that retrieves weather data” and then independently handles: research, writing code, fixing bugs, testing. Similar agents exist for customer service, accounting, or market research. Large language models like GPT-4 are also increasingly being embedded in agent frameworks – that is, frameworks that give them tools and a planning structure.
The term appears in financial news when companies announce that they are replacing routine work with AI agents. Analysts speak of Agentic AI being the next big wave after chatbots. The difference is clear: a chatbot responds. An agent acts. This shift is the reason why many tech companies are currently investing heavily in Agentic Engineering.
One important misconception is to consider agents infallible. Because an agent carries out many steps on its own, small errors can compound. An incorrect intermediate result then leads to an incorrect next step – and so on. That is why Agentic Engineering also includes the so-called human-in-the-loop principle: at critical points, the system pauses and waits for human confirmation before continuing.