How To Build Your Own AI Agent

Intro 

"An AI agent can respond to your messages 24/7, when a person is still asleep at 2AM," according to Nancy Xu, Chief Executive Officer of AI recruitment company Moonhub. What else autonomous AI agent can do? 

This was the subject of a highly anticipated panel session – What Can AI Assistants Do? – at the World Economic Forum's Annual Meeting of the New Champions in Dalian, China in June. One of the speakers of the forum was: Xi Kang - Assistant Professor, at Owen Graduate School of Management, Vanderbilt University.

For Kang, an AI agent or assistant is "any algorithm or models powered by AI or related technology that can help people make predictions about the future or make decisions, if it is approachable enough for laymen to interact with it, to get some insights from it." In her view, this encompasses a wide range of AI development solutions created to assist users in navigating complex information and making informed choices based on accessible and user-friendly interactions.

So, let us try to find out the basics of custom AI agents, their key capabilities, and, most importantly, how to build them step by step. We'll explore the fundamental concepts behind AI agents, diving deeper into their architecture and core functionalities. By the end, you'll have a clear understanding of the agent development process, enabling you to create your own custom AI agent platform.

Read more about AI Agents in our previous article: Agents in Artificial Intelligence: What Are They And How They Are Used.

Understanding of AI Agents

One of the easiest ways to understand AI Agents is to start with their well-known versions - AI Chatbots. You may find a lot of articles about this type of autonomous AI agent in our blog, however, in this one, we are going to remind you shortly about its key features.

So, the key features of an AI chatbot as an AI agent may include:

• Natural Language Processing (NLP)
• Contextual Understanding
• Self-Learning Capabilities
• 24/7 Availability
• Task Automation
• Scalability

A chatbot could be a great example of an agent as custom AI Agents are usually developed to automate tasks such as answering customers, research, data analysis, and content creation. 

Autonomous AI agents stand out due to their proactive nature and decision-making abilities. Unlike passive tools, these agents actively interact with their environment, making choices and executing actions to achieve their specified objectives. Their dynamic engagement ensures that they are not just reacting to inputs but are also strategically working towards their goals.

A necessary feature of AI agents is their ability to learn and adapt. Using modern technologies such as large language models, these agents continually update their performance based on previous interactions. When we talk about autonomous AI agent systems, multiple agents can play roles similar to members of a professional team. Obviously, autonomous agents are becoming game-changer tools in the new tech era.

By using an autonomous AI agent, you can develop custom generative AI solutions that seamlessly integrate into your projects, significantly saving time and enhancing productivity. 

Moreover, the implementation of custom AI Agents allows you to accomplish more tasks efficiently and intelligently.

Architecture of AI Agent

In the modern world of AI agent development, the architecture of agents plays a significant role in determining their behavior and capabilities. Understanding these architectures is key to understanding how AI agents operate, make decisions, and solve business problems. 

So, let us determine three major types of agent architectures that form the foundation of AI agents: Reactive Agents, Deliberative Agents, and Hybrid Agents.

Reactive Agents

Reactive agents operate based on the current state of their environment, instantly reacting to changes without the use of internal models, like LLMs, or historical data. These agents are created to respond in real time, making them highly efficient in dynamic and unpredictable environments. For example, a reactive agent might be integrated into an automated customer support agent, which needs to adjust its path immediately due to the replies of the customer.

Technically, reactive agents rely on a set of condition-action rules, sometimes referred to as production rules. These rules are usually straightforward and understandable: "If condition A is true, then perform action B." The simplicity of this architecture enables fast processing and execution, making reactive agents ideal for tasks that require immediate responses.

Deliberative Agents

Deliberative agents, on the other hand, are the strategists of the custom AI agents. They use internal models to plan actions by considering the future consequences of their actions. This approach allows them to perform complex strategies and make informed decisions based on a comprehensive understanding of their environment.

Such an autonomous AI agent is equipped with a rich knowledge base and advanced reasoning technologies. A usual example of a deliberative agent is an autonomous vehicle, that is able to plan its route, anticipate traffic conditions, and make decisions based on a multitude of factors to ensure safe and efficient road travel.

Hybrid Agents

Hybrid agents are the most powerful machines in terms of AI agent platforms, as they combine the architecture of both previous models. In other words, hybrid custom AI agents can respond immediately to environmental changes while also engaging in long-term planning based on modeled outcomes. Such agents can switch between reactive behavior and deliberative planning as the situation demands, making them exceptionally adaptable and robust.

From a technical perspective, hybrid agents are often powered by layered architectures. One of the most popular types of architecture is the three-layer architecture, consisting of a reactive layer for immediate response, a deliberative layer for planning, and a coordination layer to manage the interaction between the two. This structure enables agents to perform a wide range of tasks, from real-time responses in fast-paced environments to strategic planning in complex scenarios.

From the schema above, we may see that AI agent architecture is usually presented by the following key elements:

• User Input, where the user can ask an agent to perform some action (process data, answer the question, etc.)
• Frontend Application, where we can interact with an agent via a user interface and use all its developed functionality.
• Backend Application, where the agent processes all the actions and connects with other services or applications, interacts with the frontend app.
• AI Application, where “all the magic” happens. In other words, the machine learning models or other AI algorithms process the data and provide the output we expect to receive at the end.
• Additional Elements: admin panel, 3rd party services connected via API (like Hubspot, Mailgun or ChatGPT-4o, embedder, etc.
• Cloud Environment: AWS, Google Cloud, or any other service can be used to host AI agents and make it possible to run smoothly.
• Vector Databases: MyScale or Pinecone, which stores and retrieves high-dimensional data like text embeddings, enhancing search capabilities and improving the performance of AI models.

Overall, the architecture of AI agents shows their style and potential effectiveness in various use cases. As AI technologies continue to grow, these architectures will play an increasingly important role in developing intelligent systems capable of tackling a diverse array of challenges. Whether it's for instantaneous reactions, strategic planning, or a blend of both, understanding these core architectures is essential for anyone interested in the field of artificial intelligence.

How We Use Agents in IONI

At IONI, we leverage AI-powered agents to transform compliance management into an automated, intelligent, and scalable process. Our platform is designed to serve a wide range of industries, including:

• Regulatory Compliance & Agencies – Streamlining monitoring, reporting, and audit readiness.
• Life Sciences & Pharma – Managing complex regulations, clinical trial documentation, and quality management systems.
• Food Safety & HACCP Plan Builders – Ensuring food production standards and safety compliance are continuously met.

Instead of relying on static compliance tools, IONI deploys specialized AI agents, each designed to handle a specific aspect of regulatory operations. These agents enable:

• Faster decision-making – by analyzing large volumes of regulations and documents in real time.
• Higher accuracy – by automatically filtering applicable requirements and performing impact assessments.
• Seamless integration – connecting regulatory intelligence, document management, gap analysis, and compliance workflows into a single ecosystem.

By orchestrating multiple agents, IONI provides organizations with a multi-agent compliance platform that ensures teams stay audit-ready, up-to-date, and fully aligned with industry standards.

Regulatory Intelligence Agent

(Monitoring • Filtering • Impact Assessment)

At the heart of IONI’s compliance ecosystem is the Regulatory Intelligence Agent - an AI-driven module designed to monitor regulations, filter relevant requirements, and assess their impact on your organization.

How It Works

1. Regulation Sources Setup
   Each company defines its own sources of regulatory data based on industry, geography, and compliance priorities. IONI integrates seamlessly with a wide range of sources — from official APIs like the EU’s Lex database to custom standards and guidelines.
   • Clients can add as many sources as needed.
   • Complex sources may require manual configuration, while others are fully automated.
2. Continuous Monitoring & Alerts
   Once the sources are connected, the agent continuously scans for regulatory updates. Any new regulation, guideline, or standard detected across your sources appears instantly in the Regulatory Monitoring Dashboard, where compliance teams can review alerts in real time.
3. Smart Filtering with AI
   To prevent information overload, the agent automatically checks whether each detected regulation is relevant to your business:
   • Applicable regulations → Automatically added to your library.
   • Non-applicable regulations → Skipped by default but can be reviewed and added later if needed.
4. Impact Assessment
   After a regulation is marked applicable, the agent performs a requirement-level applicability check:
   • It analyzes the regulation, breaks it down into specific obligations, and evaluates which ones directly apply to your organization.
   • Compliance teams can review and manually adjust these results if needed, ensuring accuracy and full control.
5. Centralized Regulation Library
   All relevant regulations and extracted requirements are stored in a personalized regulatory library, where teams can:
   • Track updates in one place
   • Manage applicability status
   • Collaborate on compliance actions

Why It Matters

The Regulatory Intelligence Agent replaces hours of manual research and document review with real-time regulatory intelligence. By combining monitoring, AI-driven filtering, and impact assessment, IONI ensures that your compliance teams stay ahead of changes — without drowning in irrelevant data.

Comparing Regulations & Researcher Agents

(Version Tracking • Cross-Jurisdiction Comparison • Advanced Research)

Managing compliance often requires understanding how regulations evolve over time and comparing requirements across different jurisdictions. To make this process faster and more accurate, IONI provides two powerful AI-driven tools: the Comparing Regulations Agent and the Researcher Agent.

1. Comparing Regulations Agent

This agent automates the process of analyzing differences between regulations — whether it’s comparing a document to its previous versions or evaluating similar rules across multiple jurisdictions.

Key Use Cases

• Version Comparison – Automatically compare the latest regulation against its earlier versions to identify what has changed.
• Cross-Jurisdiction Analysis – Compare requirements between different countries, regions, or industry-specific frameworks.
• Standards & Directives Matching – Compare regulations with ISO standards, internal company policies, or other compliance frameworks.

How It Works

1. Select the regulation you want to analyze.
2. The agent automatically detects related previous versions or allows you to choose a comparison document.
3. It compares each requirement in your primary regulation with the matching requirements in the other regulation.
4. The agent generates a detailed report with status labels for every requirement:
   • New → A requirement introduced for the first time.
   • Changed → An existing requirement modified in wording, scope, or obligations.
   • Repealed → A requirement removed or replaced in the new version.

Additionally, for every Changed requirement, the agent provides direct references to the original documents and highlights the exact differences, allowing teams to quickly review legal details without manual searches.

2. Researcher Agent

While the Comparing Regulations Agent focuses on requirement-level comparisons, the Researcher Agent is designed for broader legal analysis. Instead of checking individual obligations one by one, this agent scans entire regulations at once and answers both high-level and specific compliance questions.

Example Use Cases

• “Summarize all the changes introduced in this new directive.”
• “List all GDPR requirements that affect personal data transfers.”
• “Show me where ISO 13485 overlaps with EU medical device regulations.”

The Researcher Agent delivers citations with every response, pointing directly to the relevant sections and clauses in the documents. This makes it ideal for fast legal research and deep-dive analyses, while the Comparing Regulations Agent remains the better choice when you need a detailed, requirement-by-requirement comparison.

Why These Agents Matter

Regulations evolve constantly, and keeping up manually is nearly impossible. By combining version tracking, cross-jurisdiction comparison, and intelligent research capabilities, IONI enables compliance teams to:

• Detect critical changes instantly
• Avoid missing new or repealed requirements
• Streamline global regulatory intelligence

Gap Analysis Agent

(Compliance Coverage • Policy Alignment • Automated Recommendations)

The Gap Analysis Agent in IONI helps organizations identify missing compliance coverage by comparing their existing policies, procedures, and documentation against specific regulatory requirements. Instead of manually reviewing hundreds of pages, this agent automates the process, highlights what’s missing, and even assists in updating or creating documents to close the gaps.

How It Works

1. Document Integration
   You start by uploading your existing compliance documents - policies, SOPs, quality manuals, or any other regulatory materials — into the system.
2. Gap Detection
   When the agent runs, it compares your documentation against selected regulations and identifies:
   • Requirements that are fully implemented
   • Requirements that are partially implemented
   • Requirements that are not covered at all
3. Impact-Aware Analysis
   Using your company context (industry, jurisdiction, operational scope, etc.), the agent also performs an impact assessment.
   • If a requirement does not apply to your business, the agent marks it as non-applicable and excludes it from the gap list.
   • If it does apply, it’s highlighted for review with suggested actions.
4. Smart Recommendations
   For every missing or partially covered requirement, the agent provides actionable guidance on how to achieve compliance:
   • Update an existing document - The agent highlights which sections need modifications.
   • Create a new document - If no relevant document exists, the agent generates a detailed draft description of what the new policy or SOP should include.
5. AI-Powered Document Generation
   When creating new documents, the agent uses your company-specific data to ensure that the generated document aligns with your organization’s operations and regulatory needs. You can then review, edit, and finalize the document directly within IONI.
6. Real-Time Re-Validation
   After updating or adding documents, you can rerun the gap analysis to verify if the requirement is now fully covered. The agent rechecks your policies and updates the compliance status automatically.

Key Benefits

• Faster Compliance Audits → No more manual mapping of regulations to internal policies.
• Actionable Insights → Know exactly what’s missing and how to fix it.
• Adaptive to Your Business → Uses your company’s structure, products, and operational scope to tailor results.
• Continuous Improvement → After filling gaps, rerun the check anytime to maintain full compliance.

Example Scenario

Imagine your organization needs to comply with a new EU medical device regulation:

• The agent scans your existing documentation.
• It identifies four missing requirements.
• It recommends updating two SOPs and generates a draft policy for the other two.
• You review and finalize the documents, then rerun the agent.
• The system confirms that all four gaps are now fully closed - ensuring audit readiness.

This makes the Gap Analysis Agent a core part of IONI’s compliance automation workflow, connecting seamlessly with Regulatory Intelligence and Document Management to keep your organization compliant at all times.

Document Drafting & Management Agent

(Document Creation • Editing • Version Control • Integration with Compliance Agents)

Managing compliance documents manually can be complex, especially when dealing with hundreds of policies, SOPs, and regulatory reports. The Document Drafting & Management Agent in IONI simplifies the entire process - enabling organizations to create, upload, edit, and track documents seamlessly while keeping them directly connected to compliance activities like gap analysis and regulatory monitoring.

Key Capabilities

1. Upload & Digitize Existing Documents
   • Upload files in PDF, DOC, DOCX, and other business formats.
   • IONI automatically converts them into structured digital documents for analysis and integration across other agents, including Gap Analysis and Regulatory Intelligence.
2. Generate Documents from Scratch
   • Using AI-powered drafting, you can create new policies, SOPs, reports, and regulatory documents directly in IONI.
   • Simply provide a short description or follow recommendations from the Gap Analysis Agent, and the system will generate a company-specific draft.
   • These drafts are context-aware, meaning they’re automatically aligned with your company’s operations, industry, and regulatory requirements.
3. AI-Powered Document Editing
   • Modify, extend, or rewrite documents using IONI’s intelligent drafting agent.
   • Add missing sections recommended by Gap Analysis or regulatory changes.
   • Get a preview of suggested changes before applying them:
     • Accept all changes in one click.
     • Or selectively integrate specific parts into your existing documents.
4. Version Control & Change Tracking
   • Every document is automatically versioned to ensure full traceability.
   • You can:
     • Review the history of changes.
     • Restore previous versions at any time.
     • See who edited the document and exactly what was changed.
5. Metadata, Tagging & Search
   • Tag documents by type, regulation, or department.
   • Filter, sort, and organize files effortlessly within the platform.
6. Publishing & Export Options
   • Save documents as drafts or mark them as active when finalized.
   • Export documents in PDF or other supported formats when needed.
   • For DOCX-origin documents, you can switch between the editable AI mode and your original file version — though AI-powered updates require the editable mode.

How It Integrates with Gap Analysis

The Document Drafting Agent works closely with the Gap Analysis Agent to speed up compliance workflows:

• When the system detects missing requirements, it suggests new documents or updates existing ones.
• It generates draft policies and procedures tailored to your company’s regulatory needs.
• Once the document is created or updated, you can rerun the gap analysis to confirm compliance coverage.

Key Benefits

• Faster Policy Updates → Generate and edit documents instantly.
• Full Audit Readiness → Maintain version history and track changes seamlessly.
• End-to-End Integration → Connects document creation directly to compliance checks.
• Tailored to Your Business → AI-generated documents are context-aware and industry-specific.

By combining document creation, AI-powered editing, and automated version tracking, IONI turns regulatory document management into a streamlined, audit-ready process. It ensures that your policies and procedures are always up-to-date and aligned with the latest regulations.

Customization

Customization is one of the biggest benefits for AI agent - IONI that can be offered by Springs team. Let's try to find out how this customization can be implemented as the next step after AI agent development.

The world of AI technologies is unlimited but not all of them can be used to fine-tune and tailor AI agents. Customizing AI agents involves a variety of AI and ML frameworks, tools, and services, depending on business goals, such as building NLP chatbots, recommendation systems, computer vision models, or other AI agents. 

AI Frameworks and Libraries

• TensorFlow. A popular open-source library by Google for building and training deep learning models. It is usually used for neural networks, computer vision, and NLP.
• PyTorch. An open-source ML library developed by Facebook’s AI Research lab. It’s known for its flexibility and ease of use, particularly for research and development of deep learning models.
• Keras. A high-level neural networks API, coded in Python, capable of running on top of TensorFlow, CNTK, or Theano. It significantly simplifies building deep learning models.
• Hugging Face. A huge library of AI tools that provides a collection of pre-trained models for different business tasks such as text classification, translation, summarization, and more. It supports both PyTorch and TensorFlow.
• Scikit-learn. ML-based library in Python that provides simple and efficient tools for data analysis, built on NumPy, SciPy, and Matplotlib.

AI Cloud Services

• Google Cloud AI. Google AI Cloud offers a suite of tools such as AutoML, TensorFlow Extended (TFX), Vertex AI, and pre-trained models for a variety of AI tasks.
• AWS AI. AI-powered AWS provides services like Amazon SageMaker for building, training, and deploying machine learning models, Rekognition for image analysis, and Lex for conversational interfaces.
• Microsoft Azure AI. MS Azure AI provides services like Azure Machine Learning, Cognitive Services, and Bot Service, which allow you to integrate AI into agents easily.
• IBM Watson. IBM company provides a range of AI services like Watson Assistant for chatbots, Watson Studio for model building, and Watson Natural Language Understanding for text analysis.

Conversational AI Platforms

• Rasa. An open-source framework for building conversational AI, including chatbots and AI assistants. It offers tools for intent recognition, dialogue management, and custom NLU components.
• Dialogflow. Dialogflow is a Google-based platform for natural conversational AI development. It uses NLP to understand and respond to user input and can integrate with multiple channels.
• Microsoft Bot Framework (MBF). MDF provides a set of services for building bots that can integrate with various messaging platforms. It includes Azure Bot Service and the Language Understanding (LUIS) model for intent recognition.

Computer Vision Tools

• OpenCV. OpenCV is one of the most popular open-source computer vision libraries that provides tools for image and video analysis, including object detection, face recognition, and more. It is usually used with Python programming language.
• Detectron2. This is Meta’s AI Research library providing state-of-the-art detection and segmentation algorithms.
• YOLO (You Only Look Once). YOLO is a real-time object detection AI service that uses a single neural network to the full image, dividing the image into regions and predicting bounding boxes.

Data Processing AI Tools

• Labelbox. This is an AI platform for training data labeling and management, useful for computer vision, NLP, and other AI/ML tasks.
• Amazon SageMaker. ASM is a data labeling service that provides highly accurate training datasets for machine learning models.
• SuperAnnotate. This is an AI data platform tool for image and video annotation that accelerates the creation of training data for multiple computer vision tasks.

Overall, these tools and technologies provide a great opportunity for professionals to customize AI agents to fit specific use cases, whether you're working on AI chatbots, computer vision solutions, reinforcement learning, or other business apps.

Conclusion

The growth of AI agents is expected to accelerate significantly in the coming years, driven by advancements in machine learning, natural language processing, and autonomous AI technologies. These agents are being increasingly integrated across industries - from customer service chatbots and AI assistants to complex compliance, regulatory, and document management systems in sectors like life sciences, pharma, food safety, manufacturing.

As AI technologies evolve, agents are becoming more sophisticated, capable of understanding context, responding naturally to human inputs, and performing specialized tasks. This progress is fueled by large datasets, more powerful computing resources, and domain-specific AI models, allowing agents to learn, adapt, and provide actionable insights with greater accuracy.

In the compliance and regulatory space, AI agents like those in IONI are transforming the way organizations monitor regulations, assess impacts, manage documentation, and ensure audit readiness. They go beyond automating routine tasks - they analyze complex regulatory frameworks, generate or update documents, identify gaps, and support decision-making in real time.

As AI agents become more practically useful in everyday business operations, we are entering a new era of human-AI collaboration. In this era, AI agents are integrated “AI employees” working alongside human teams - enhancing efficiency, reducing risk, and enabling organizations to navigate regulatory environments with unprecedented speed and precision.