How to Build an AI Agent

AI agents have transitioned from research lab experiments to everyday business tools. According to Gartner, the average global Fortune 500 enterprise is set to increase AI agents from 15 in 2025 to 150,000 by 2028. And to best benefit from the AI agents, you should understand how to build one.

Together with the Cybernews research team, I reviewed some of the most popular AI agent-building frameworks. In this article, I take a closer look at the foundational agent components, a step-by-step guide, as well as the most common mistakes. I also cover which frameworks to use, regardless if you have coding knowledge or not.

What is an AI agent, and when do you actually need one?

An AI agent is software capable of acting autonomously, even if it’s sometimes limited. Once given a goal, an AI agent can use multi-step actions to achieve it, and you don’t have to initiate each individual step.

AI agents have become an integral part of many businesses and industries. However, in the current landscape, users also have to deal with agent washing – when basic tools, such as chatbots or other basic software, are presented as an AI agent. Chatbots work in a manner that’s more closely aligned to LLM apps like ChatGPT, since a human provides an inquiry and then receives an output based on that. An AI agent, on the other hand, is given a goal to follow, and then it uses several steps and initiates its own inquiries to provide sufficient results.

It’s important to know when you need a simpler tool and when an AI agent is more beneficial. If you have a task that requires several steps, such as obtaining information from LLMs, drawing from the external systems data, and even making decisions mid-process, an AI agent is better suited for it because you won’t have to give all the smaller inquiries manually.

That said, if you have a task that, let's say, relies on your calendar, an AI agent can easily be replaced by a workflow tool, like Zapier. It’s a less flexible and simpler option.

4 core components of every AI agent

AI agents can be coded from scratch or built with no-code builders. But despite the method you use, all AI agents are based on the same four foundational principles. In order to create an efficient AI agent, you should understand these building blocks.

Planning and reasoning. Choose an LLM to serve as the foundation of the AI agent. This LLM will receive your input and interpret the goal you set by breaking it into manageable subtasks. Then, it decides which steps to take to produce the best output. If your goal is to consolidate a large amount of data into small reports, an analytics-based LLM will interpret each step from a research perspective.

Memory. Next, you’ll need to decide what type of memory the AI agent should utilize: short-term and long-term. Short-term memory relies on the ongoing session to produce results, but once the session ends, its data is lost. Long-term memory involves past interactions, so the AI agent can recall previous inputs and tasks for broader results.

Tool use. You will also need to determine what tools you’d like the AI agent to have access to, since that will help the agent to produce solid results, instead of just text. The tools can include databases, CRM updates, web search, or email. For example, if an AI agent is working with scheduling tasks, it might be a good idea to have it use a calendar to keep up to date with time availability.

Action and output. Last but not least, you should decide on the output you expect and what the agent should do once it completes its initial tasks. It can either produce results for a human to review, send an email based on its findings, or trigger another AI agent. An AI agent is rarely universal, since it follows a specific pattern, so you should always keep the expected output in mind. To illustrate, if you set up an agent to find and research new clients for your sales team, the output could include detailed information on potential collaborations and their contact details.

How to build an AI agent: step by step

Now that we’ve discussed the main cornerstones of an AI agent, let’s take a look at how to build an AI agent.

Step 1: define the goal and scope

It’s important to have a concrete goal when designing an AI agent. If the expected output is too vague, the AI agent will produce subpar results due to too many variables. Let’s say you’re dealing with extensive reports; an AI agent can compile those reports and give a concise summary without your manual input, but for that, it needs a set of rules to follow.

Step 2: choose a code or no-code method

There are two main ways to build an AI agent. If you have coding experience or if you’re working with a developer on the team, you may want to use frameworks such as LangChain or CrewAI, or platforms like n8n, as they offer extensive customization options for your AI agent.

On the other hand, if you have no coding experience or simply want to avoid that process, you can choose platforms with a visual builder, such as Relevance AI, Lindy, and n8n. Usually, these platforms will have a drag-and-drop structure, so you can build an agent with only a basic understanding.

Step 3: choose and configure your LLM

LLM is the driving force behind an AI agent, so you should choose the one that best fits your needs best. When choosing an LLM, you should also consider the task complexity, cost efficiency, context window, and whether you’ll use Bring Your Own Key (BYOK) encryption.

Step 4: define the agent's instructions

Defining the agent’s instructions is a crucial step, especially for beginners. You need to set up prompts for your agent so it has clear instructions for every step. It’s important to be specific, since the prompts will push the agent to act one way or the other. It should know where to extract needed information, what to do with it afterward, or even what to do when there’s no usable relevant information.

Step 5: connect tools and data sources

An AI agent needs to have suitable tools for its task, so you should also give the agent access to everything that’s relevant for its task, which can include web search, a knowledge base, CRM access, or even email. For example, if your goal is to schedule tasks according to the team’s seniority, your agent will need to access personnel files and calendars, as well as the task database. But keep in mind it’s best to avoid over-tooling.

Step 6: add memory (if needed)

This step is only relevant when you’re building an agent for repeated tasks that need to access past prompts or informational context. The memory feature also helps use the platform's built-in memory or connect to a vector store, such as Pinecone, Qdrant, or Weaviate.

Step 7: test with real inputs

Test your AI agent before you use it in actual projects. I’d advise testing with real-life examples or cases that you will need the agent to solve once it’s officially launched. Check whether the agent's results are unambiguous, or what happens when an API call fails. You can also add manual checkpoints for your AI agents to ensure all parts are working correctly.

Step 8: deploy and monitor

The final step is to launch the AI agent. You can deploy it through a platform's cloud, a self-hosted server, or an API endpoint. It’s a good idea to start monitoring the agents, because their behavior can change when the LMM is updated.

Code vs no-code: which path is right for you?

As previously mentioned, it’s possible to build AI agents from the ground up by leveraging coding knowledge to create a functional system. However, if you’re wondering how to create an AI agent without coding, there’s a method for that too.

Code-first path:

• Tools. The best tools for coding-based AI agent creation are services specialized for this type of project, like LangChain, CrewAI, or n8n (Python or JavaScript).
• Best for. This method best suits developers who need custom logic or full control over the project. If you’re planning to juggle multiple agents, it might also be beneficial to build them from scratch.
• Perks. When coding by yourself, you have more flexibility over the process and more control over the end result. Your agent also doesn’t depend on any vendor.
• Drawbacks. You need to have substantial knowledge of coding languages to be able to build an AI agent. You also need advanced debugging and infrastructure management skills.

No-code path:

• Tools. Without coding skills, you can rely on a service that will give you pre-set tools to create a functional AI agent. These services include Zapier AI, Gumloop, n8n's visual builder, Lindy, and others.
• Best for. No surprise that the no-code method works best for people with limited coding knowledge. Building an agent with these services is often faster than coding.
• Perks. You can deploy your AI agent sooner due to fast setup and pre-built integrations. A visual builder and managed infrastructure give more clarity throughout the process.
• Drawbacks. The main drawback is the reliance on a vendor, which can lead to scaling costs that rise with usage. No-code systems are also less flexible.

You can also take a hybrid approach by first creating an AI agent using no-code services to see how it behaves, understand its processes, and verify its use cases. If you think the agent will benefit you further, you can migrate to a code framework to have more production flexibility.

AI agent frameworks and platforms worth knowing

As with many services online, not all AI agent builders are the same. Together with the Cybernews research team, I tested various AI agent-building tools on the market, and here are my recommendations.

Code frameworks

• LangChain. A software framework that helps with LLM integration. It is most used to create working product models. It has a rich ecosystem, but it works better for quick builds rather than complex multi-agent systems.
• CrewAI. A multi-agent framework built on Python. It’s best suited for multi-agent collaboration with role-based crews. You can monitor, manage, and scale your AI agents using a centralized platform.
• LangGraph. A graph-based AI agent orchestration framework, which works best with long-running agents and complex, stateful workflows with conditional branching. This framework is affiliated with LangChain.
• Microsoft Agent Framework (MAF). An open, multi-language framework, allowing developers to build production-grade AI agents in two coding languages – .NET and Python. It also supports multi-agent orchestration.

No-code or low-code platforms

• n8n. A visual workflow builder with an AI Agent node, employing 1,100+ native integrations. It is self-hostable, and, after reviewing n8n, I recommend it to teams who want to have more control over AI agents without coding knowledge.
• Relevance AI. Low-code agent builder. According to Relevance AI evaluation, it seems best suited for GTM or ops teams. It utilizes Workforce Canvas for multi-agent pipelines and has the Invent feature, which turns a description into a fully-functioning AI agent.
• Lindy. A beginner-friendly AI assistant. No coding is required to use it, and it’s best for SaaS workflow automation. You can learn more in the Lindy AI review.
• Gumloop. An AI automation framework that supports no-code agentic workflows. After reviewing Gumloop, I noticed it has a strong integration with workplace apps, like Slack or Microsoft Teams.
• nexos.ai. An all-in-one AI platform that supports the creation of custom AI agents. No coding knowledge is required, and agents can be built using specialized templates.

Common mistakes when building AI agents

Whether you’re coding an AI agent from scratch or using a no-code method, there are certain things you should pay attention to. Here are common mistakes users make when building their own agents:

• Vague goals. One of the more common reasons why AI agents fail is the lack of goal precision. You shouldn’t be vague about what you want the agent to do; be clear and concise.
• Over-tooling. If you provide the AI agent with too many tools, it gets more opportunities to make mistakes by picking the wrong tool. I’d advise choosing only the most crucial tools
• No fallback logic. Consider what the agent should do when it can’t provide a result. If there’s no fallback logic, the agent might end up in an indefinite loop, or simply stop working without you knowing.
• Skipping human-in-the-loop on high-stakes actions. While automation is a good time-saver, you should still add a step for human verification to avoid possible mistakes. This is especially relevant if you’re working with a new AI agent.
• Not monitoring after deployment. Once you launch an AI agent, you should periodically check on it, since LLM model updates or API changes can affect the agent’s performance.
• Agent washing. An AI agent is capable of planning, using tools, and adapting to situations. If your creation can’t do that, it’s not an agent and shouldn’t be called that.

Bottom line

Before you start building an AI agent, consider whether there are patterned tasks that take up your time and can be automated by AI. If so, there are several methods for building an agent, depending on your skills and experience.

If you’re a developer, you can use CrewAI or n8n and create an agent by cloning a template or making a custom AI agent. You should probably start small and ensure the agent works as intended, with working fallback logic and workflow. You can deliberately break it to see how the agent reacts and to iron out any issues.

If you don’t have technical experience, Relevance AI or Lindy can help you build an AI agent using templates or the Invent feature. They will provide you with sufficient components to test the agent on an actual task and determine its viability.

And if you’re somewhere between a beginner and a developer, n8n is a good option due to its visual builder. n8n is suitable for most tasks, has code nodes for edge cases, and offers 1,100+ integrations.

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