8 Things to Keep in Mind while Building AI Agents

Are you fascinated by the evolving world of AI agents? Today, we can see AI agents almost everywhere, making our lives easier. Almost every field benefits from it, whether it is your last-minute ticket booking or your coding companion. AI agents have effectively tapped into every market. Everyone wants to build them to optimize their workflows. This guide explores the top 8 things that you should keep in mind while building your AI agent.

After reading this guide, you will understand the requirements to build a top-notch AI agent that is ever ready to do your work without any complaints.

What are AI Agents?

AI agents are programs that utilize artificial intelligence to interact with a particular environment and perform predefined tasks independently. It can perceive its surroundings and make decisions based on that with minimal human intervention.

Let’s dive one by one into the things to consider while making AI agents.

1. Define the Agent’s Goal Clearly

The foundation of any successful AI agent is a clearly defined goal. People often create objectives that are vague and not detailed, which leads to very generic results and hallucinations. Think of it like ordering a human to do a task with an unclear objective; for sure, he will mess up. So, the agent needs to know in detail which task it needs to perform and how to perform it. Otherwise, it cannot work efficiently. For building AI agents that deliver, being specific is mandatory.

Using S.M.A.R.T. criteria (Specific, Measurable, Achievable, Relevant, Time-bound) can be a wise move. For example, rather than defining a generic goal like “Improve the customer service,” a very specific goal would be: “The AI agent will resolve 80% of common customer queries regarding product returns within two minutes. It should do this without needing human escalation.” This is the most specific goal a user can define to influence an AI agent’s ability to think. It also impacts the choice of tools the agent would make to access external information. A well-defined goal is the first step in AI agent development.

2. Choose the Right Framework

Building the AI agents from scratch can be a complex task. Luckily, several frameworks simplify this process. Making AI agents using them feels like a cakewalk. LangChain, LangGraph, AutoGen, or CrewAI provide a very structured way to build, deploy as well as manage AI agents. They contain pre-defined classes, tools, as well as boilerplate code that speeds up the development in a very efficient manner.

While selecting a framework for building your AI agents, you should consider several factors. Ease of use can lower the learning curve, scalability plays a major role if your agent will be handling more than one user or task. Available integrations with LLMs, tools, and data sources are also crucial. For example, LangChain provides integration with every LLM, data source, and tool. LangGraph helps build stateful, multi-step agents. Choosing the right and robust framework can speed up your development process significantly and make it smoother.

3. Select the Appropriate LLM

Selecting the right Large Language Model (LLM) is a crucial step. LLM acts as the “brain” of your AI agent. The LLM’s capabilities directly affect your agent’s performance in the production environment. It determines how intelligent and smart your agent will be. In 2025, the market offers several LLMs, each having different advantages and strengths. Top LLMs in the market include OpenAI’s GPT series, Meta’s Llama models, Anthropic’s Claude, or Google’s Gemini.

While selecting the LLM for your AI agent, you should consider its reasoning capabilities, how well the LLM is instruction-tuned (how well it follows instructions). Coding abilities can be considered if you are having a use case of generating or understanding code. LLM needs to understand multiple languages if your agent is for a global use case. Context window also plays a crucial role, which means how much data can be fed into the LLM or it can process at once. Cost per token and latency, i.e, response speed, are practical considerations for real-time applications. 

4. Choose the Right Agent Architecture

The design pattern and specific architecture of the AI agent are critical. This defines how a particular agent processes information, executes its tasks, and makes desired decisions accordingly. There are various architectures suited to different types of problems.

A popularly used architecture is ReAct, which means first reason and then act. Agents using this architecture first reason over the user’s query and then decide the next best action based on their reasoning, and then execute it. This continuous process allows the agents to handle complex and multi-step problems. Another architecture is planning, where the AI agent first creates a very detailed plan and then follows the steps one by one to achieve the desired result. For the tasks that require additional attention, i.e, highly complex tasks, multi-agent systems can be employed. In these systems, more than one specialized agents are employed for a particular task, and they collaborate while working. A well-designed architecture means AI agents can work effectively and efficiently. 

Also Read: Top 4 Agentic AI Design Patterns

LLMs on their own are very powerful and efficient, but they operate in a very isolated environment, unaware of the real world. Tool integration provides these agents the ability to interact with the real world and perform meaningful actions. Tools allow the AI agents to access the external information or trigger actions in other systems. This is like providing your LLM superpowers so that it can do several tasks on its own, which often requires human intervention.

Examples of tools integration include math tools, APIs for accessing external data like weather updates or stock market prices, and triggering some event, like sending emails. Agents need these tools to query the databases and perform web searches for updated information. Code execution tools allow agents to execute the scripts. These tools must be reliable for use in real life. AI agents decide which tool to use while running. Equipping your agent with the power of tools can make AI agents go from a simple conversational bot to a working assistant who will be able to execute tasks using a simple command. These intelligent automation tools become much more powerful with external capabilities.

6. Memory and State Management

AI agents can be truly useful for long-running tasks or conversations. It needs memory for this. By utilizing the memory, the agent can recall the past conversations and maintain the context of the conversation while answering questions. Without memory, each interaction is new, and it limits the agent’s ability to learn or build from previous interactions. State management is key to building AI agents that feel coherent.

There are two types of memory that AI agents can have. Short-term memory helps the agent remember relevant information about the current ongoing conversation or task. Whereas Long-term memory allows the agent to store information across different interactions or sessions. This includes the user behaviour, preferences, and completed tasks. For storing long-term memory, vector databases are often utilized. Frameworks like LangGraph provide mechanisms for state management. It ensures that the AI agent can track its progress and data. 

7. Prompt Engineering and Few-Shot Examples

One of the primary ways to communicate with AI agents and their “brain” LLMs is through Prompts. The accuracy as well as the behaviour of the agent is heavily dependent on the quality of prompts you pass to the agent. Defining clear, non-ambiguous prompts is a necessary skill. Effective prompt engineering is vital when building AI agents.

Overall personality of the agent, its objectives, and behavior are extensively defined by the System prompt. For achieving a specific task, the agent must be provided with a few-shot example with the prompt so that the agent can understand the intention of the question and the expected format of the answer. This, in turn, can significantly improve the performance. Providing LLMs with certain examples can help them understand the task very well. These careful instruction helps align the agent with your expectations.

8. Evaluation and Feedback Loop

Once you are done building your AI agent, you need to evaluate how it’s performing. Hence, continuous evaluation is essential. You have to set some metrics from the very first step to carefully evaluate the performance. These metrics should be aligned with the agent’s defined goals. Testing should be performed to evaluate the agent. This can include various end-to-end tests for evaluating the agent’s behaviour in different scenarios. Performing unit tests on individual tools is an important aspect of Automated testing. However, for highly complex tasks with complex reasoning, human evaluation is mandatory. Human feedback can identify the unexpected failures in the behaviour of the AI agent. 

LangSmith is a wonderful tool that can assist in logging the interactions and collecting feedback on the outputs. A sophisticated feedback system, where the outputs are used to refine the prompts, tools, or even architecture of the agent, is vital. This iterative process helps improve your AI agent over time and ensures it consistently meets user needs. AI agent development is an ongoing cycle of build, test, and refine.

Also Read: What is the Difference Between AI Agents and Agentic AI?

Conclusion

Building a truly effective AI agent involves more than just binding up an LLM to some tools. It requires careful planning and consideration of multiple factors. By carefully considering these eight aspects: from clear goal definition and framework selection to robust memory and continuous evaluation, you can create powerful intelligent automation tools. Remember, crafting a sophisticated autonomous AI system is an iterative journey. These principles will guide you toward success in building AI agents that are both capable and reliable. 

Frequently Asked Questions

Harsh Mishra is an AI/ML Engineer who spends more time talking to Large Language Models than actual humans. Passionate about GenAI, NLP, and making machines smarter (so they don’t replace him just yet). When not optimizing models, he’s probably optimizing his coffee intake. 🚀☕