What is an AI Agent Engineer?
- Authors
- Name
- Amit Shekhar
- Published on
An AI Agent Engineer is an AI Engineer who specializes in building AI agents, which are systems built around an LLM that decide what to do next, use tools, and keep working step by step until a goal is reached.
In this blog, we will learn about the AI Agent Engineer role. We will also see what an AI agent is, what an AI Agent Engineer owns in a product, the skills the role needs, what a normal day in the role looks like, how the role differs from a general AI Engineer, and who should choose this role.
We will cover the following:
- What is an AI Agent Engineer?
- What is an AI agent?
- What does an AI Agent Engineer own?
- Skills needed for an AI Agent Engineer
- A day in the role of an AI Agent Engineer
- How does an AI Agent Engineer differ from an AI Engineer?
- Who should become an AI Agent Engineer?
I am Amit Shekhar, Founder @ Outcome School, I have taught and mentored many developers, and their efforts landed them high-paying tech jobs, helped many tech companies in solving their unique problems, and created many open-source libraries being used by top companies. I am passionate about sharing knowledge through open-source, blogs, and videos.
I teach AI and Machine Learning at Outcome School.
Let's get started.
What is an AI Agent Engineer?
An AI Agent Engineer is an AI Engineer who specializes in building AI agents. The role owns everything that turns an LLM (Large Language Model) into a system that can decide, act, and finish a task on its own.
An AI Engineer builds products on top of existing models through APIs, connects them to our data, and ships them as features. An AI Agent Engineer does the same, but the product is an agent. An agent does not give one answer and stop. It takes many steps, and each step can go wrong.
So, the AI Agent Engineer makes sure that every one of those steps works, and that the agent stays safe, cheap, and fast while it works.
What is an AI agent?
Before jumping into the role, we must know what an AI agent is.
An AI agent is a system built around an LLM that decides what to do, uses tools, and loops toward a goal.
Let's say we ask an agent to book a meeting room. The LLM reads the request and recommends a tool, for example, "call the calendar tool with these inputs". The LLM never runs the tool. Our code runs the tool and sends the result back to the LLM. The LLM looks at the result and decides the next step. This repeats until the room is booked. This cycle of decide, act, and observe is the loop of an agent.
So, an agent has three parts: the LLM that decides, the tools that act, and our code that runs the loop. The AI Agent Engineer builds all three parts and makes them work together.
A quick note for you
No matter which tech domain you work in, get familiar with these topics:
- LLM
- RAG
- MCP
- Agent
- Fine-tuning
- Quantization
We put it all together in one video:
AI Engineering Explained: LLM, RAG, MCP, Agent, Fine-Tuning, and Quantization
No need to stop reading - bookmark it and watch later when you get time. Future you will thank you.
Now, let's get back to the topic.
What does an AI Agent Engineer own?
Now, let's understand what we own as an AI Agent Engineer.
Tool design and tool calling: A tool is a piece of code that does one job for the agent, for example, search the database or create a ticket. We decide which tools the agent gets, write a clear name and description for each tool so that the LLM picks the right one, and build the code that runs the tool when the LLM recommends it.
Memory: An LLM forgets everything once the conversation ends. We decide what the agent must remember, for how long, and where it is stored. Short-term memory holds the current task. Long-term memory holds facts about the user and past work. We also decide what goes into the context, which is the text sent to the LLM at each step, because it has a limited size.
The planning loop: This is the code that runs the agent. It sends the current state to the LLM, reads the recommended tool, runs it, adds the result, and repeats. We decide when the loop must stop, how many steps are allowed, what happens when a tool fails, and when the agent must ask a human before going further.
Guardrails: An agent can take real actions, so a wrong step has a real cost. Guardrails are the limits around the agent. We decide which tools need approval, what the agent must never do, and how to check the input before it reaches the LLM and the output before it reaches the user.
Multi-agent orchestration: Some tasks are too large for one agent. We split the work across specialized agents, for example, one agent that reads documents and one agent that writes the report. Orchestration decides which agent runs when, how they pass work to each other, and how their results are combined.
Agent evaluation: An agent can finish the same task in a different way every time, so we cannot test it against one fixed expected output. We build a set of test tasks, run the agent on them, and measure how many tasks were completed, how many steps were taken, and where the agent went wrong. We also read the traces of failed runs, which are the step-by-step records of what the agent did, and fix the tool, the prompt, or the loop.
Cost and latency: Every step of the loop is one call to the LLM. An agent that takes 30 steps costs about 30 times more than one that takes one step, and the user waits much longer. We track the tokens and the time per task, pick a smaller model for simple steps, and cut the steps that add nothing.
Skills needed for an AI Agent Engineer
The skills of an AI Agent Engineer come in two groups: agentic coding and judgment.
Agentic coding: We do not type all the code ourselves. We build with an AI coding agent such as Claude Code. The skill is to describe the task clearly to the coding agent, review the code it produces, test it, and design the system around it. For example, we direct the coding agent to write a tool that creates a support ticket, then we read the code, run it against a test ticket, and check that the error handling is correct.
For this, we must understand Python, how an API call works, how to read a trace of an agent run, and how an LLM reads a prompt, otherwise we cannot verify what the coding agent produced.
Judgment: Judgment is our own job. It means deciding which tools the agent needs, how much freedom the agent gets, when a human must approve a step, which model is good enough for each step, and whether a failed run was caused by the prompt, the tool, or the loop. It comes from building many agents and reading many traces.
Want to become an AI Agent Engineer?
These are the skills this role needs, and we teach them in our AI and Machine Learning Program at Outcome School:
- AI Agent
- ReAct Pattern
- Tool use in Agents
- Memory in Agents
- Multi-Agent Systems
- Evaluation of LLMs and Agents
We take you from the fundamentals to building real AI systems, step by step.
A day in the role of an AI Agent Engineer
The best way to understand the role is by taking an example of one day.
Let's say we are the AI Agent Engineer for a customer support product. Our agent reads a customer message, looks up the order, and either answers or creates a ticket for a human.
In the morning, we open the evaluation dashboard. The agent completed 91 out of 100 test tasks in the last run. We read the traces of the nine failed runs. In six of them, the agent called the order lookup tool with the wrong input, because the customer wrote the order number with spaces, and the tool expected no spaces.
We open our coding agent and describe the fix: "Update the order lookup tool to remove spaces and dashes from the order number before searching. Add a test for an order number with spaces." The coding agent writes the change and the test. We read the change, run all the tests, and confirm that nothing else broke.
Then, we run the evaluation again. The agent now completes 97 out of 100 tasks. Two of the three remaining failures are cases where the agent needed to ask a human and did not. This is a guardrail problem, not a tool problem. We add a rule in the planning loop: if the refund amount is above a limit, the agent creates a ticket for a human instead of acting.
After lunch, we look at cost. The agent takes an average of seven LLM calls per task. The first call only classifies the message into a category, and it does not need our biggest model. We direct the coding agent to switch that one step to a smaller model, and we run the evaluation again to confirm that the completion rate did not drop. The cost and the latency per task both go down.
In this whole day, the coding agent wrote the code, and we directed it and verified the result.
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How does an AI Agent Engineer differ from an AI Engineer?
An AI Engineer builds any product on top of a model: a chat feature, a search over our documents, a classifier, or an agent. An AI Agent Engineer is an AI Engineer who works only on the agent part and goes deeper into it: tool design, memory, the planning loop, guardrails, orchestration, and agent evaluation. In a small team, one AI Engineer does both. In a large team, the AI Agent Engineer is the specialist who makes the agent take real actions reliably.
Who should become an AI Agent Engineer?
The role suits us if we like systems that take many steps and want to make each step reliable.
The role is a good fit if we enjoy reading a trace to find out why a run failed, if we like designing the limits around a system as much as the system itself, and if we are comfortable when a system behaves differently on every run. It also fits a software engineer who already works with a coding agent every day and wants to build the same kind of agent for other people.
The role is a poor fit if we want a fixed input to always produce a fixed output, or if we want to train models rather than build around them.
Now we must have understood what an AI Agent Engineer is, what the role owns, what a day in the role looks like, and who it suits.
Frequently Asked Questions
Does the LLM run the tools in an AI agent?
No. The LLM never runs the tool. The LLM only reads the request and recommends a tool with its inputs. Our code runs the tool and sends the result back to the LLM, and the LLM then decides the next step.
Why can't we test an AI agent against one fixed expected output?
We cannot, because an agent can finish the same task in a different way every time. So, we build a set of test tasks and run the agent on them. Then, we measure how many tasks were completed, how many steps were taken, and where the agent went wrong.
Why does an AI agent cost more than a single LLM call?
Every step of the agent loop is one call to the LLM. An agent that takes 30 steps costs about 30 times more than one that takes one step, and the user waits much longer. So, we track the tokens and the time per task and cut the steps that add nothing.
Does an AI Agent Engineer write all the code by hand?
No. An AI Agent Engineer builds with an AI coding agent such as Claude Code. We describe the task clearly to the coding agent, review the code it produces, test it, and design the system around it. We still need to understand Python, how an API call works, how to read a trace of an agent run, and how an LLM reads a prompt, otherwise we cannot verify what the coding agent produced.
That's it for now.
Thanks
Amit Shekhar
Founder @ Outcome School
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