What Should Be the First Step When Building an AI Agent?

 The first step in building an AI agent is defining a clear use case, scope, and success criteria before choosing models or frameworks. Learn how to start the right way.

When people decide to build an AI agent, they often begin in the wrong place. They compare frameworks, watch demos of multi-agent systems, or debate which model to use. That feels like progress, but most official guidance points somewhere else: the real first step is defining the exact job the agent should do, its boundaries, and how success will be measured. OpenAI, Anthropic, Microsoft, and Google Cloud all emphasize that strong agent systems begin with a narrow, well-defined use case rather than architecture-first thinking.

That idea matters because not every AI application should be an agent in the first place. Anthropic distinguishes between workflows, where steps are fixed in advance, and agents, where the model decides how to use tools and complete tasks dynamically. Google Cloud similarly frames agent design as a pattern choice based on the task, rather than a default starting point. In other words, before building an agent, you should first ask whether the problem truly requires agentic behavior at all.

So, what is the first step?

It is this: define one specific problem the agent will solve. Microsoft’s guidance says to begin by identifying the use case and defining the agent’s purpose and scope. That includes the task it will perform, the problem it solves, the outcome you want, the limits of its role, the data it needs, and the metrics that will define success. Google’s design guidance also says that before building an agent, teams should clarify objectives, user expectations, and interaction requirements.

That means the first deliverable should not be code. It should be a short design statement. For example, “This agent reads incoming support emails, identifies billing issues, drafts a response, and sends it for human approval.” That is much stronger than saying, “I want to build a customer service AI agent.” The first statement is concrete, testable, and narrow. The second is too broad to design well. OpenAI’s practical guidance on agents stresses identifying promising use cases and designing clear agent logic before scaling complexity.

Why starting with the problem matters

When teams skip the problem-definition step, they usually create systems that are hard to evaluate, expensive to run, and difficult to trust. If the agent’s job is vague, then its tools, permissions, prompts, and outputs also become vague. That leads to the most common early failure: a system that looks impressive in demos but is unreliable in real work. Anthropic notes that many of the most successful agent implementations were built with simple, composable patterns rather than overly complex frameworks.

Starting with a tightly defined job also helps you control scope. One of the biggest risks in agent design is letting the system do too much too soon. Microsoft’s guidance on autonomous agents stresses setting clear boundaries, role definitions, and limitations so that an agent does not act beyond what is intended. This is especially important if the agent can access tools, databases, or communication channels.

A narrow scope also improves trust. When users understand what an agent is supposed to do, they are more likely to accept its help and notice when it goes off track. Google’s agent design guidance emphasizes designing around the end user’s goals and expectations, not just the system’s internal logic. That is a useful reminder that AI agents are not built just to “be smart.” They are built to help a person complete a defined task.

What you should define before building

Before selecting a model or framework, write down the answers to a few basic questions.

First, what exact task will the agent handle? Be specific. “Help sales” is too broad. “Draft follow-up emails after discovery calls using CRM notes” is much better.

Second, who is the user? Is the agent serving a customer, an employee, a researcher, or a developer? Different users require different levels of autonomy, speed, and accuracy.

Third, what tools or data sources will the agent need? Some agents only need a knowledge base. Others need calendars, ticketing systems, APIs, or internal databases.

Fourth, what actions is the agent allowed to take on its own, and which actions require approval? This distinction is essential for safety and governance.

Fifth, how will you know the agent is successful? If you cannot answer that clearly, the project is not ready. These planning points align closely with Microsoft’s recommended first step and with OpenAI’s broader emphasis on clear tools, instructions, and operating logic.

Define success before you build

This is where many AI projects become fuzzy. People say they want an agent that is “helpful,” “smart,” or “efficient,” but those are not usable engineering targets. OpenAI’s evaluation guidance says evals are necessary because generative systems are variable, and structured testing is how teams measure real performance in production. Anthropic’s documentation makes the same point very directly: a successful LLM-based application starts with clearly defining success criteria and then building evaluations around them.

A better goal looks like this: “The agent should classify support tickets into the right category with at least 90% accuracy, draft a polite response in brand tone, and escalate refund cases above a set amount for human review.” That target is measurable. It gives you something to test, improve, and compare over time. Without a definition like that, it becomes almost impossible to decide whether the agent is actually working.

This is one reason evaluation should be part of the first step, not an afterthought. OpenAI’s eval resources describe evaluation as essential for understanding how well your system performs against expectations, especially as prompts, models, and workflows change. That means the first step in building an AI agent is partly a product strategy exercise and partly an evaluation design exercise.

Decide whether you need a workflow or an agent

Another reason to define the task first is that it helps you choose the right system type. Anthropic explains that some problems are better solved with workflows, where the process is predictable and coded directly. Agents become more useful when the path to completion is dynamic and the system must choose among tools or sub-steps.

For example, if your system only needs to summarize documents into a fixed template, that may not need an agent at all. A prompt plus retrieval and some rules may be enough. But if the system must read a request, decide what tools to call, gather information from several places, handle exceptions, and ask for clarification when needed, then agentic behavior may be justified. Google Cloud’s design-pattern guidance is useful here because it frames agent architecture as something chosen after understanding the work, not before.

This step saves time and money. Agentic systems often introduce more latency, more cost, and more failure points than simpler pipelines. Starting with the task helps you avoid overengineering. That is why so much current guidance recommends beginning with the simplest pattern that can solve the job.

Start with one narrow use case

A strong beginner strategy is to build for one small scenario first. OpenAI’s guidance on building agents focuses on identifying promising use cases and designing safe, predictable systems before expanding. Microsoft’s step-by-step advice also begins with a specific use case rather than a broad ambition.

Good first-agent examples include:

• an agent that triages support emails
• an agent that drafts meeting summaries from notes
• an agent that checks policy documents and answers internal questions
• an agent that prepares a first-pass sales follow-up from CRM data

These are good starting points because the task is visible, bounded, and easy to review. A vague project like “build an AI employee” or “make an autonomous business assistant” is usually too broad for a first attempt. The narrower the job, the easier it is to prompt, test, secure, and improve. That pattern is consistent across OpenAI, Anthropic, Microsoft, and Google Cloud guidance.

Think about permissions early

Once you define the job, you can define permissions. This is another reason the “first step” matters so much. If the agent is supposed to answer questions from internal documentation, it may only need read access to a knowledge base. If it is supposed to create tickets or schedule meetings, then it needs more powerful permissions. Microsoft’s autonomous-agent guidance emphasizes least-privileged access, clear operating boundaries, and oversight for higher-impact actions. OpenAI’s production-oriented guidance also emphasizes strong foundations, safety, and operational control when moving systems toward real use.

This is where many teams realize their first version should be assistive, not fully autonomous. An agent that drafts recommendations for a human reviewer is often a better first release than one that executes actions automatically. Starting with a narrow use case makes that decision much easier.

Do not start with multi-agent architecture

One of the most common temptations in AI development is starting with a multi-agent design because it sounds advanced. But current guidance generally recommends restraint. OpenAI advises maximizing a single agent’s capabilities first and only using multi-agent systems when the task truly requires additional specialization or orchestration. Google Cloud likewise presents design-pattern choice as an architectural decision made after task analysis, not as a default.

For most first projects, a single, well-scoped agent is enough. It is easier to evaluate, easier to debug, and easier to govern. If it succeeds and the workload later justifies specialization, then you can split responsibilities into multiple agents. But complexity should be earned by the use case, not added because it looks impressive in a diagram. Anthropic’s research strongly supports this practical approach.

Keywords: AI agent, first step in building an AI agent, how to build an AI agent, AI agent design, agentic AI, AI workflow, AI automation, single-agent system, multi-agent system, AI agent evaluation, AI agent best practices, prompt engineering

References

1. OpenAI, A practical guide to building agents.
2. Anthropic, Building Effective AI Agents.
3. Microsoft, How to Build and Train AI Agents.
4. Microsoft Learn, guidance related to autonomous agent capabilities and configuration.
5. Google Cloud Architecture Center, Choose a design pattern for your agentic AI system.
6. Google Cloud Dialogflow, General agent design best practices.
7. OpenAI API Docs, Evaluation best practices and Working with evals.
8. Anthropic Docs, Define success criteria and build evaluations.