Building Your First AI Agent: Only 4 Core Components Required

Building your first AI Agent doesn't require a full team of multiple agents. OpenAI's official development guide offers a clear recommendation: start with a single Agent. Only consider scaling up when one Agent genuinely can't handle the job.

This deceptively simple advice cuts through a common misconception among beginners — piling on complex architecture too early makes systems harder to debug and understand. Derek AI Tech breaks down a working Agent into four distinct components in his video, giving newcomers a clean roadmap to follow.

The Four Core Components

A functioning Agent is essentially assembled from four pieces. OpenAI officially defines three of them as core components, while the fourth (state) is the most common practical addition.

1. Model

The model is the Agent's brain. It reads user requests and decides what to do next. In other words, the model plays the decision-making role — it doesn't directly execute actions, but rather determines what action to take given the current context.

The quality of the model directly determines the Agent's reasoning ability. A powerful model can understand intent more accurately and plan steps more effectively — and this is where the entire system begins.

2. Instructions

Instructions set the Agent's goals and boundaries. On one hand, they tell the Agent what to accomplish (the goal); on the other, they define guardrails it cannot cross.

This component is often underestimated. Clear instructions make Agent behavior predictable and prevent it from making unexpected decisions in ambiguous scenarios. Think of instructions as the Agent's "job description" — they define its mission while constraining its behavior.

3. Tools

Tools are functions the Agent can call to actually execute actions — such as querying weather, reading a database, or calling an external API.

The model itself can only think and decide; the ability to interact with the outside world comes from tools. An Agent without tools is just a chatbot. Connect it to tools, and it gains the capacity to act. The design of your tools directly determines what an Agent can do and how far it can go.

In technical implementation, tools are typically integrated via Function Calling. Developers pre-define a function's name, parameters, and return format. During inference, the model outputs structured JSON specifying which function to call and what arguments to pass; an external program then actually executes the call and returns the result to the model. This design maintains a clean boundary between the model and the real world: the model only decides what to call and with what parameters, while actual network requests, database queries, or file operations are handled by the host program. Common tool types include: Retrieval-Augmented Generation (RAG) for querying knowledge bases, code executors for running dynamic computations, external API calls for fetching real-time data, and file read/write operations for persisting intermediate results.

4. State

State saves the conversation context so that follow-up questions still make sense.

For example: if a user first asks "What's the weather like in Beijing today?" and then follows up with "What about tomorrow?", the Agent needs to remember the context from the previous message. State is the mechanism that maintains this continuity. OpenAI treats three components (model, instructions, tools) as the foundation, with state being the most common practical addition.

Bringing the Four Components to Life: The Run Loop

Once the four components are wired together, what actually brings an Agent to life is a loop.

The logic of this loop is straightforward: the model makes a decision → calls a tool → observes the result → makes another decision, repeating until the task is complete.

This "decide → act → observe → decide again" loop is what distinguishes an Agent from a simple one-shot Q&A model. It gives the system the ability to continuously push a task forward: adjusting when intermediate results aren't ideal, and stepping through multi-stage operations one at a time. Understand this loop, and you understand the fundamental nature of how an Agent runs.

This loop is commonly referred to in academic and engineering literature as the ReAct framework (Reasoning + Acting), introduced by Princeton University in 2022. Its core idea is to have the model alternate between "Thought → Action → Observation" at each step, rather than producing a final answer in one shot. A related pattern is Plan-and-Execute, where the model first generates a complete step-by-step plan and then executes it incrementally. Each pattern has its use case: ReAct is more flexible and can dynamically adjust based on intermediate results; Plan-and-Execute works better for long tasks with clear, fixed structures. Understanding the loop's essence makes debugging faster — you can quickly pinpoint whether the problem is a bad model decision, an unexpected tool return, or lost context in the state.

When Do You Actually Need Multiple Agents?

Another key point from the video: don't introduce multi-agent architecture too early.

Multi-agent systems can handle more complex tasks, but they also bring coordination overhead, increased debugging difficulty, and unpredictability. For a first project, a single Agent with clear tools and instructions is usually more than enough.

The right approach: get a single Agent running, understand the role of each component, and only expand to multiple agents when one truly can't meet your needs. This is both OpenAI's official position and the more robust path in engineering practice.

Typical scenarios where multi-agent architecture makes sense include: tasks that require parallel processing (e.g., scraping multiple data sources simultaneously), subtasks that need fundamentally different specialized instructions (e.g., one agent for code generation, another for code review), or when a single context window can't fit the entire task history. Common multi-agent patterns include Orchestrator-Subagent and Pipeline. In the former, a primary Agent handles task decomposition and scheduling, delegating subtasks to specialized agents. In the latter, agents process tasks serially, with each agent's output feeding into the next. The biggest new cost when introducing multiple agents is reliability of inter-agent communication — you need to clearly define message formats, error handling mechanisms, and termination conditions, otherwise the system can easily fall into infinite loops or silent failures.

Summary

Building your first AI Agent isn't mysterious. It can be broken down into four clear components and one run loop:

• Model handles decision-making
• Instructions set goals and guardrails
• Tools provide execution capability
• State maintains conversational continuity
• The loop drives the entire process forward until completion

Starting simple — connecting the basic components into a working system — is far more valuable than chasing complex architecture from day one. When a single Agent truly hits its limits, that's the right time to talk about scaling up.