How To Build an AI Agent: Development Guide & Tips

Growing data volumes, customers who expect instant responses, competitors who offer more resource-efficient solutions… Yes, modern businesses have a lot to keep up with. And while generative AI and chatbots can help to some extent, you can get even better results if you know how to build an AI agent.

What sets AI agents apart from large language models and robotic automation tools? Do they provide measurable value to businesses today? Our guide answers these questions and lays out each step of AI agent development.

An artificial intelligence (AI) agent is autonomous software that continuously learns and performs tasks based on user input.

An AI agent is similar to context-aware chatbots but more complex. Building AI agents requires an understanding of key concepts:

• AI agents interpret various user commands. They process typed and written text, data tables, spoken words, images, and videos to understand tasks and retrieve information.
• They can adapt and improve. Machine learning helps the agent refine its performance over time. They rely on fundamental AI concepts like data labeling and generative AI (GenAI) to recognize patterns and make informed suggestions.
• They are autonomous. AI agents interpret queries, retrieve data, and process information independently, learning without manual intervention. They decide which tools to use, in what sequence, and how to interpret results.
• They solve advanced tasks. Unlike simple bots, AI agents execute complex tasks such as data entry, code generation, and workflow automation. They interact with external resources and call tools (function calling), such as databases and application programming interfaces (APIs).

AI agents combine advanced automation, adaptable algorithms, and user-friendly interactions in a single system. Business-wise, they provide a wealth of benefits that you shouldn’t overlook.

Creating an AI agent is more complex than rule-based chatbots or basic robotic process automation (RPA) tools. AI agents contextualize tasks, handle multiple inputs, make dynamic decisions, and adapt to new scenarios they weren’t explicitly programmed for.

Organizations of any size may build an AI agent from scratch or adopt an existing solution for the following reasons:

• Accelerated reasoning. AI agents process real-time data and respond quickly to user demands. They identify anomalies, risk factors, and emerging patterns and suggest data-driven actions instead of just presenting raw information.
• Enhanced customer satisfaction. AI-powered agents provide instant responses and personalized service 24/7. According to Gartner, 25% of organizations will rely on chatbots as their primary customer service channel by 2027.
• Automated manual labor. AI agents excel at carrying out repetitive tasks, such as basic data entry, email responses, and basic answers. The cost of developing and maintaining AI agents is typically far lower than staffing the same job function with multiple employees.
• Increased productivity. Customer service teams, engineers, and managers can use AI agents to boost efficiency. Deloitte reports that AI-powered copilots increase developer productivity, benefiting both experienced engineers and junior coders.
• Scalability and flexibility. Companies can build AI agents with adjustable workloads based on server capacity and user demand. A modular, containerized architecture allows businesses to add features and integrations easily.
• Competitive edge. AI enhances productivity for both customers and internal teams. It reduces manual oversight, allowing companies to focus on other areas (like research and development).
• Revenue growth. According to Deloitte’s 2024 Generative AI report, nearly all companies using generative AI agents report measurable ROI, with cybersecurity applications delivering above expectations.

These benefits are possible thanks to multiple components that work together in the AI agent.

AI agents rely on the following essential components: perception, reasoning, action, and learning.

• Perception

  Text-based AI agents are trained on vast amounts of text data to predict the most likely response and sequence of words. Meanwhile, physical agents and robots also use cameras, microphones, and other sensors.

• Reasoning

  Uses rule-based systems, machine learning algorithms, and neural networks. This allows the AI to analyze the collected information, identify patterns, and come up with conclusions.

• Action

  Executed based on perception and reasoning. Depending on the type of the AI agent, this could be a textual response, a generated image, a certain script (like sending an email), or a physical movement (for robotic agents).

• Learning

  Refines over time as agents refine their decision-making processes. Learning can be supervised (labeled datasets), unsupervised (unlabeled data), and reinforced (unsupervised learning with manual feedback).

Allow us to demonstrate how an AI agent might process a simple task step-by-step:

1. Input: The agent receives a request in the form of text, voice (converted to text), or an external trigger (like an API call). The perception helps it identify key phrases and the intent.
2. Reasoning: It decides how to achieve the request, formulating a plan of action. To attain the best results, it evaluates available options and asks the user for clarification.
3. Action: The agent executes the necessary steps to fulfill the request. For example, to provide sales figures, it may decide to retrieve the “Sales” table from the database, apply date filters for a specified period, and summarize the results in a human-readable format.
4. Observing: It assesses the results, making sure the query returned valid data and that the API worked as expected. If it gets an error, it may refine the approach or consider another option. This “check and correct” mechanism is crucial for complex requests.
5. Answer: The final response is compiled if the agent is satisfied with the results. Additionally, the agent may log new knowledge or mistakes (pointed out by the user) for continuous learning.

An AI agent isn’t just another code or a role-based model, which makes the development more complicated than simple automated solutions.

It’s not easy to create an AI agent from scratch and make it past the pilot stage. The whole process takes a lot of resources and time to start generating value (sometimes well over a year). Below, we outline the process and offer tips covering most development facets.

Step 1: Define the purpose and use cases

Initially, you should pinpoint the business problem and needs you want the AI agent to solve. For instance, you may want the agent to help your employees in decision-making or tasks like coding and testing. AI agents can automate responses to frequently asked questions, which is a must-have for most customer service departments. 

Visualize how end-users will interact with the AI agent through user journey mapping. To do this, collect user feedback and map customer journeys. When building an AI agent for a shopping site, for example, you might deconstruct the process of making a purchase to highlight where the AI can offer suggestions. 

Key practices:

• Conduct workshops, surveys, or interviews with key stakeholders from the bottom up to prioritize features for the agent.
• Create detailed documentation (outlining all intended use cases, required functionalities, and excluded features). This should be regularly updated as the project evolves.
• Set SMART criteria (Specific, Measurable, Achievable, Relevant, Time-bound) for the business goals of the agent development (for example, to reduce customer response times by 30%).
• Develop a route to market to map every requirement to corresponding functionalities, ensuring all needs are met during the AI agent development.
• Determine the boundaries of what your AI agent can and can’t do (to prevent gradual “scope creep” and avoid ethical problems).

Step 2: Prepare the training data

Collect a diverse dataset for your AI agent training. The data can be found in conversational logs, support tickets, emails, voice recordings, and other datasets. Make sure the datasets accurately represent the types of interaction the agent will handle.

Raw data is often noisy, redundant, or inconsistent. So, you should establish a data-cleaning mechanism to prevent duplicates, errors, irrelevant information, unnecessary punctuation, and gaps in records.

To create your own AI agent that handles complex tasks, you should supply it with datasets that support multi-step reasoning. For instance, it may need examples of failed queries and error messages to learn from mistakes and generate self-corrections.

Key practices:

• Set up automated ETL (Extract, Transform, Load) workflows to clean and preprocess data continuously.
• Maintain snapshots of datasets used for training to retrain or fine-tune if needed.
• Label or tag data for intent recognition, sentiment analysis, specific domain entities, and contextual relevance (like urgency or complexity).
• Use data augmentation or synthetic data generation to diversify the training dataset in an effort to avoid biases in AI output.
• Anonymize personally identifiable information in training datasets to comply with data privacy regulations (like GDPR, CCPA, and HIPAA).

Step 3: Choose the AI model

The model depends on the specific use case. You should account for the type of data the agent interacts with and how much reasoning or decision-making is required. 

The most common agent model types are:

• Simple reflex Reacts to current conditions using predefined rules, ignoring past interactions
• Model-based reflex An internal model with a simplified version of the environment anticipates future states and improves decision-making
• Goal-based Evaluates and selects from multiple actions based on long-term goals rather than immediate reactions
• Utility-based Chooses actions based on their overall benefit or “utility” rather than just reaching a goal
• Learning Uses feedback loops to refine decision-making and update models dynamically
• Multi-agent Multiple models communicate to solve complex problems
• Hierarchical Structured in layers, where higher-level agents oversee and direct lower-level agents (top-tier agents set objectives, and lower-tier models execute tasks)

You can go with a pre-trained AI model (that uses one of the types above) that uses massive datasets collected before deployment. Fine-tuning an existing model may boost its accuracy, but this could lead to overfitting (the model becomes too reliant on training data and struggles to learn from new inputs).

Key practices:

• Test models on real-world examples (e.g., financial agents should handle real transaction data) based on accuracy, latency, and cost per request.
• Consider OpenAI for general-purpose LLMs, TensorFlow or PyTorch for custom architectures, and Hugging Face for pre-trained models.
• Choose the right architecture for your model (cloud-based models are more scalable, but on-premises models ensure better privacy due to isolated architecture).

Step 4: Train the AI agent

You may choose different training methodologies when building an AI agent:

• Supervised learning is trained on datasets with clearly marked correct answers, which can improve accuracy but affect creative reasoning.
• Unsupervised learning doesn’t use labeled answers, allowing the model to discover hidden patterns by itself. 
• Reinforcement learning enables the AI to learn from trial and error by receiving feedback.

Once the datasets and training methods are selected, you should conduct multiple training iterations. Compare results with expectations, tracking accuracy, loss function, and response times. Then, gradually adjust the weights (what the AI agent considers the most important data), learning rates, batch sizes, and training datasets. 

Key practices:

• Divide the dataset into multiple sections and train the model on different segments (cross-validation) to prevent overfitting.
• Run training on a small dataset first to identify errors before scaling up to reduce the need for massive retraining.
• Don’t overuse fine-tuning to avoid over-reliance on static, memorized responses.

Step 5: Test and validate output

AI doesn’t always fail in obvious ways—sometimes, it takes incorrect reasoning paths or avoids making necessary tool calls. It can invent facts when the data is missing. What makes it worse is that the AI doesn’t understand when it’s wrong.

Building AI agents requires comprehensive testing against real-world scenarios, including misleading inputs, incomplete datasets, and vague questions. Errors need to be caught through pattern analysis and real-world testing.

Key practices:

• Include unexpected queries, sarcasm, incomplete information, and multi-intent inputs in the dataset to improve the agent’s interpretation ability.
• Conduct A/B testing with different user groups for unbiased feedback about the agent’s usability.
• Create a user feedback mechanism (like giving a thumbs up and thumbs down for responses) to improve AI through user interactions.

Step 6: Deploy, monitor, and refine

After you create an AI agent, the next step is to deploy it with your web and mobile applications, enterprise systems, and APIs. Even if an AI agent performs well in testing, poor integration can lead to failures and security gaps.

An AI agent degrades over time due to changes in user behavior (new trends, terminology, or even viral memes), external API modifications (for example, financial data API changes format), and model drift (when the training data doesn’t align with the incoming information). Real-time monitoring and audits are required to identify these problems.

Key Practices & Tips:

• Implement load-balancing solutions (compute resource distribution, edge computing, asynchronous processing, and caching) to handle growing computational demands.
• Incorporate real-time error logging to handle unexpected user queries or system issues.
• Set alert thresholds (e.g., accuracy drops below 90%) to trigger immediate retraining.
• The agent’s “interface” to external data and tools demands ongoing refinement, as even small syntax shifts or format changes can vastly impact performance.
• Design consistent input/output schemas (like JSON) so the agent easily locates required fields.
• Provide clear error messages when a query fails. Let the agent see what went wrong, then try a corrected approach.

Building an AI agent is just as much about knowledge as it is about trial and error. Give your agent room to “think” by providing error outputs, letting it retry actions, and equipping it with structured prompts or tools.

Companies need to have clear use case scenarios in mind to plan how to create an AI agent from scratch. So, let’s outline the modern applications for AI agents.

Customer support

AI agents provide 24/7 service across industries. The best part is that they can answer most queries without constant human oversight, freeing human support staff for other tasks.

Applications:

• AI and voice assistants recognize recurring questions and instantly provide accurate, context-aware responses.
• If tasks exceed their scope, agents can hand off cases to human employees (after complaining about relevant information to help resolve them).

Software development

According to Deloitte, code writing and testing are the most user-ready and compelling use cases for generative AI adoption.

Applications:

• Automated AI agents can test code for consistency and provide suggestions for improvement.
• Developers can use AI copilots to convert their ideas into executable code through a prompt.

Healthcare

AI agents streamline patient care, administration, and diagnostics while ensuring compliance with privacy laws.

Applications:

• Agents can handle mundane tasks like updating Electronic Health Records (EHRs) or notifying patients and doctors about upcoming visits.
• AI can reference a medical database to suggest conditions and potential treatment plans to healthcare professionals based on patient symptoms.
• Generative AI can analyze corporate documents and processes to determine compliance with laws.

Finance

AI agents ensure tasks like fraud detection and wealth management are handled swiftly in financial companies.

Applications:

• AI can flag unusual account activity (such as atypical login locations or sudden large withdrawals), largely reducing the number of alerts that require human intervention (according to Deloitte’s case study about GenAI banking).
• Inbound queries (such as balance updates and loan information retrieval) can be delegated to AI agents.

Marketing

Marketing-focused AI agents help segment audiences, personalize content, and optimize campaigns.

Applications:

• AI categorizes users into groups (based on clicks, purchases, browsing history, etc.) to target them more accurately with suggestions.
• Generative tools can help boost social media presence by helping produce social media posts.
• Agents assist in researching leads, updating prospect databases, and generating outreach emails, enhancing the speed and size of closed sales. 

Education

AI agents customize learning, automate grading, and assist educators with administrative tasks.

Applications:

• An AI tutor can analyze a student’s past quizzes and assignments to identify knowledge gaps, offering personalized lessons or supplementary exercises.
• The agent can help create and grade entire interdisciplinary programs and certification courses.

Supply chain management

AI agents improve quality control, demand forecasting, and logistics coordination across the supply chain.

Applications:

• Notify operators if the agent detects disturbances in sensor data, such as temperature shifts, vibrations, or visual anomalies that indicate mechanical issues.
• AI systems can optimize inventory stock levels based on demand forecasts and real-time information.
• An agent can adjust orders and delivery routes based on real-time demand, supplier deliveries, and production capacity.

As you can see, building an AI agent can automate or enhance various processes, be it customer support, targeted campaigns, or predictive analytics. This is not an exhaustive list, as technology evolves rapidly.

It’s hard to predict where AI agents will be in a month, let alone in a few years. However, we think some positive trends are set to continue.

• Market growth. More companies will develop AI agents or adapt existing tools, as the market is expected to reach $47.1 billion in 2030 from $5.1 billion in 2024 (per the Research and Markets 2024 AI agents research report).
• Increased adoption of generative AI. Nearly half of the companies that currently use GenAI are predicted to launch generative AI pilots or proofs of concepts in 2027 to automate multi-step business functions and enhance workers’ productivity (per Deloitte’s 2024 Autonomous Generative AI Agents report).
• Emphasis on ethical guardrails. Emerging technologies, such as explainable AI frameworks, will help identify output errors and biases, promoting compliance and improving output accuracy.
• More on-premises solutions. More organizations will opt to learn how to build an AI agent from scratch and deploy it within their data centers. This will be speared on by the development becoming easier, cheaper, and more secure thanks to the rise of open-source models.
• Multi-agent systems. The market is experiencing the growth of multi-agent systems that can coordinate complex, decentralized tasks across industries. They will work in environments that require collaborative efforts from several parties, such as transportation and healthcare.

Finally, the AI community will also expand. As more developers contribute to AI libraries and frameworks, it will become simpler to build your own AI agent. But, for now, you might want to consider partnering with a professional developer.

AI agents enhance various business aspects and learn to improve with time. However, what sets them apart from regular AI tools is the level of autonomy and complexity of the tasks they perform. 

That said, you need extensive skills to build your own AI agent and train it to get more accurate. Luckily, DevCom offers a tailored approach and best practices to design and integrate solutions that will improve your efficiency and reduce overhead.

We can handle AI agent development from concept creation and data preparation to deployment and fine-tuning. To learn more about our services, contact us for a consultation.