How to build an AI agent.
A practical, step-by-step guide to building an AI agent that reads, decides and acts with tools, with a Python example of the reasoning loop.
ZENKEI
ZENKEI
A practical, step-by-step guide to building an AI agent that reads, decides and acts with tools, with a Python example of the reasoning loop.
To build an AI agent, you give a language model a goal, a set of tools it can call, and a loop that lets it decide, act, observe the result and repeat until the task is done, with memory and guardrails around it. The steps below walk through each part, from a plain model to an agent that gets work done.
An AI agent is a system built around a language model that can take actions to reach a goal. Instead of answering one question and stopping, it plans a step, calls a tool, looks at the result, and decides what to do next, looping until the task is finished. A chatbot answers; an agent acts. The two things that turn a model into an agent are tools and a reasoning loop.
Every agent, however it is built, has the same handful of parts:
Say precisely what "done" looks like and how you will know. A vague goal produces an agent that wanders; a sharp goal with a clear stopping condition is what makes it useful.
Expose the actions it can take as functions with clear names and typed arguments, using the model's tool or function calling. Each tool is one capability: search the web, query a database, send an email.
On each turn, let the model either call a tool or return an answer. Run the tool, feed the result back, and loop. This decide, act, observe cycle is the heart of the agent.
Keep the running conversation as short-term memory, and add a longer-term store, often retrieval over your documents, when the agent needs knowledge beyond the current task.
Limit which tools it can use and what they can do, cap the number of steps, require approval for risky actions, and validate inputs and outputs. Keep a human in the loop wherever the cost of a mistake is high.
Build a set of realistic tasks with known outcomes and measure whether the agent completes them, how many steps it takes, and where it fails. Evaluation is what turns a demo into something you can trust.
Run it against real work, log every action, and watch cost, latency and failure modes. Refine the tools and prompts from what you see in production.
Here is the core loop in a few lines: the model chooses a tool, you run it, feed the result back, and repeat until the model returns an answer or you hit a step limit. Frameworks add structure, but this is the shape underneath.
tools = {
"search": web_search,
"read": read_document,
"calc": calculator,
}
def agent(goal, max_steps=8):
messages = [{"role": "user", "content": goal}]
for _ in range(max_steps):
reply = llm(messages, tools=tools.keys()) # decide
if reply.tool_call:
name = reply.tool_call.name
args = reply.tool_call.args
result = tools[name](**args) # act
messages.append(reply)
messages.append({"role": "tool", "content": result}) # observe
else:
return reply.content # done
return "Stopped: step limit reached"A production agent adds typed tool schemas, retries and error handling, approvals for sensitive tools, memory, and logging of every step, but the decide, act, observe loop stays exactly this.
Start with one agent and a good set of tools. It solves most problems and is far easier to debug. Reach for a multi-agent system, where a supervisor coordinates specialised workers, only when a task genuinely splits into distinct roles. More agents means more coordination and more ways to fail, so add them when the problem demands it, not by default. For a fuller tour of the options, see AI agent architecture patterns.
A reliable agent with the right tools, guardrails and evaluation is real engineering, not a prompt. That is exactly our AI agent development service. Pair it with RAG development when the agent needs your knowledge, or explore the full AI hub.
An AI agent is a system built around a language model that can take actions to reach a goal: it decides what to do, calls tools or APIs, observes the result, and repeats until the task is done, rather than just answering a single question.
A chatbot answers questions. An agent takes actions: it plans steps, uses tools and APIs, and completes a task end to end, with human oversight where you want it. The reasoning loop and the tools are what make it an agent.
Anything you expose as a function: web search, your APIs and databases, file and document access, code execution, a browser, email or internal systems. The agent chooses which tool to call and with what arguments.
With guardrails: limit which tools it can use and what they can do, cap the number of steps, require human approval for risky actions, validate inputs and outputs, and log every action so it is auditable.
Not usually. Frameworks such as LangGraph and LangChain handle the loop, tool calling and memory, so you focus on the tools and logic. For production reliability and guardrails, many teams have a developer build it.
Tell us the task. We build an agent with the right tools and guardrails.