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What is Agentic AI Architecture?
Agentic systems are empowered by artificial intelligence to operate, perform choice-based actions, and complete goals independently without human intervention. When compared with traditional AI systems, these agentic solutions observe their environment, learn from the gathered data, and alter their activities as per given objectives. These systems are nowadays performing different types of complex tasks and resolving customer queries in various industries such as healthcare, manufacturing, retail, finance, etc. and driving automation efficiently. Unlike classic AI models, agentic systems react in real-time instead of responding and providing feedback passively to inputs.
The term Agentic AI may at times be misinterpreted with AI agents, where the former are advanced systems that execute tasks and complex problems through reasoning and continuous planning, while the latter may perform simpler, predefined tasks with limited autonomy. Agentic AI solutions demonstrate multi-step adaptive behavior using multiple agents and aggregates data from multiple sources to increase operational efficiency, whereas AI agents lack decision-making capability and can handle only specific repetitive tasks like responding to emails.
The agent architecture in AI utilizes memory in an efficient manner through multiple adaptive methods and reasoning-based actions over multiple steps. They assess currently occurring events and make sound predictions with respect to the level of difficulty in achieving the tasks. Through a combination of reinforcement learning, natural language processing, and machine learning models, they can optimize outcomes in dynamic scenarios. Their approximate market size is currently valued at US $7.28 billion in 2025 and is rising at a CAGR of about 41.48% to reach US $41.32 billion by 2030 during this period. Let us take a deep dive into the architecture, categories and elements of these intelligent systems.
Source: Dimension Market Research
Growing market size of Agentic AI architecture by application during the 2025 to 2034 forecast period
Components of AI Agent Architecture
The various components of Agentic AI systems play an important role by acting in sync for the independent functioning of the same. Together they enable these systems to think and adapt to dynamic environments and to learn from them while performing wise decision making as per the gained experience.
Perceptual Analysis
These AI systems analyze and integrate input text, images, sensor data, user, and databases. They are powered by natural language processing, speech recognition, and computer vision for effective examination and comprehension. An example may include a customer service scenario where a chatbot deployed for an ecommerce website answers the user or potential client’s queries through careful evaluation, processing and handling of the input text.
Cognitive Learning
AI development services empower these systems to feature learning and memory systems that can store important points, environment-related information, patterns, and previous user interactions in real-case scenarios. The AI agent architecture analyzes key and relevant stored data and improves their decision-making process with the help of long-term and short-term memory strategies over time to deliver desirable outputs. A use case may include virtual AI assistant in a financial application or institutional website that retains the customer’s preferences in terms of their investments such as fixed deposits, recurring deposits, savings with interest, mutual funds, government bonds, public provident funds, national pension system, sovereign gold bonds, equity trading, insurance products or unit linked insurance plans, initial public offering investments, stock trading and more. Various types of unsupervised learning can assist them to make personalized recommendations for stocks and other types of investment plans as per the user’s previous selections.
Sample workflow and components of AI Agent architecture
Strategic Decision-Making
The input is examined by AI models including rule-based systems, large language models that power generative AI services, and reinforcement learning systems for applying reasoning and selecting the required actionable path. The incoming data is evaluated for various possible use cases or optimal outputs for choosing the most preferable or applicable one. An example may include autonomous financial trading bots that are powered by decision-making engines to take in real-time input information and analyze current market and trade pattern to buy or sell stocks and orders.
Automated Execution
After proper decision-making is carried out by the above component of the agent architecture in AI, the choices are practically realized through automated action execution. This is ensured through seamless integration with existing online databases, external framework systems, and application programming interfaces. For example, choices made as per gathered information related to user preferences, purchase history, and market trends to execute messaging actions for input user query in a retail application or other AI solutions in retail; similarly for financial transactions and related processing, automating workflows as per input database or user manual, physical system control, meeting plan execution, travel itinerary arrangements, and automated email responses.
Feedback & Adaptation
These systems ensure that proper decision-making is followed by required automated actions and store the feedback received on requesting the user for the implemented actions. With time, they analyze feedback upon execution and refine their strategies such as self-learning, pattern recognition and machine learning, and time-data analysis to empower further actions, maximize results, efficiency of the model, and improve decision-making. A use case may include a customer service chatbot that improves its responses, timing and accuracy through continuous refinement as per the user’s feedback scores for subsequent assured satisfaction.
Types of Agentic AI Architecture
There are a few types of architectures that form the basis of these systems that are categorized on the basis of differences in structures, strategies for decision-making, and collaborative models. Selecting the right type of agent architecture is necessary for avoiding complications, inefficiencies and failure in attaining the desired objectives. Let us explore these different types of architecture in this section.
1. Horizontal AI Agent Architecture: Horizontal AI are versatile and generalized systems powered by natural language processing, predictive analytics such as for predictive maintenance in manufacturing, and computer vision that cater to business functions across cross-industry applications including marketing, human resources, sales, customer service etc. In these systems, all agents operate equally and cooperate with each other openly in a decentralized cooperative architecture. Due to a lack of a particular hierarchical framework, all agents in these systems perform decision-making, problem-solving and task completion in a distributed and equal manner. Therefore, creative and inventive tasks that require environmental adaptability can easily fit this peer-based model that features parallel processing and simultaneous task completion.
2. Vertical AI Agent Architecture: Vertical AI expert systems are highly specialized to resolve particular industry or domain-specific problems and challenges occurring around niche areas such as manufacturing, healthcare and medtech solutions, finance, generative AI for cybersecurity etc. by understanding the workflows and regulations. They are trained on specific datasets for accurate and relevant results such as in the case of medical diagnostics, legal document analysis, risk assessment in financial investments, optimization of supply chain routes, etc. In this type of agent architecture in AI, proper hierarchy can be observed between the centrally authorized controlling and supervising leader agent and the subordinate agents. The former performs high-level decision-making, assigns tasks, and assures that the latter perform effectively and accountably as per the clearly given and defined duties, framework, and responsibilities in a top-down structured execution.
Various types of Agentic AI architecture
3. Multi-Agent AI Architecture: This type of architecture consists of multiple specialized agents that work within a collaborative system through effective and real-time inter-agent communication. They may function independently for certain tasks as per their expertise or coordinate and interact with other agents to attain a specific shared goal whenever necessary. Thus, it is characterized by flexible, distributed decision-making, adaptive knowledge exchange, and scalability due to parallel processing of multiple tasks or large-scale operations in a dynamic context or shifting circumstances to resolve complicated issues swiftly.
4. Single Agent AI Architecture: This type of agent architecture in AI, an autonomous and artificially intelligent unit, acts independently as per its environment without involving, consulting other agents or external assistance. It solely interprets incoming information, performs decision-making, and acts accordingly through centralized execution and direct command over the workflow and outcomes.
5. Hybrid AI Architecture: This type of architecture involves a combination of vertical and horizontal AI systems that is centralized top-down structured approach for decision-making and decentralized cooperation between multiple agents. This is necessary for achieving equilibrium between efficiency and innovation as in real-world scenarios leader agent responsibilities may alter as per dynamic tasks, but the actions need to be implemented through organized workflows.
Taking an example of a research institute that continuously updates its website articles and recent study literature as per the latest drug safety updates, citations, pharmacovigilance databases, abstracts, biomedical references published online, etc. Multiple literature monitoring agents can perform distributed tasks independently and communicate through specific predefined communication protocols such as agent-to-agent for interoperation across ecosystems and model context protocol for seamless connection between tools, data and real-world workflows. Multiple agents perform various distinguished tasks in this case such as fetching new articles, utilizing natural language processing for safety signal analysis, comparing regulatory databases with the recently extracted data, summarization and report generation of key findings.
At the same time, a single-agent system may have access to various application programming interfaces and tools that perform these tasks, instead of inter-agent communication. It strives to ensure a streamlined workflow between appropriate tools such as natural language processing models for extraction of key information like types of reactions to drugs, web-scraping tools for obtaining articles, concise summary and reporting tool etc. by deciding and orchestrating centrally, flexibly, sequentially and dynamically as per the problem.
Functions in AI Agent Architecture
Here, functions refer to executable units of programming logic that can be embedded in multimodal large language model or called upon by agentic systems to attain certain objectives. Intrinsic functions are inbuilt in large language models for text processing which includes conversion of model text to structured query language, data tokenization, tagging certain speech, named entity recognition for identifying events, dates and names. These are also useful for natural language understanding for query context comprehension, sentiment and emotional tone analysis, and semantic parsing for converting natural language into structured commands. Moreover, intrinsic functions are useful for retrieval augmented generation or natural language generation including human-like text creation through prompt engineering such as for powering generative AI in retail, paraphrasing, and summarization.
Extrinsic functions can interact with other systems such as database queries which are structured query language functions to collect data from databases, service integration of application programming interface for sending hypertext transfer protocol requests to external interfaces like financial market websites etc., and customized logic that uses predefined rule-based systems for decision-making and specialized algorithms for recommendations, sort actions etc.
Furthermore, there are hybrid approaches that combine both extrinsic and intrinsic functions for use cases like workflow automation that may involve intrinsic data extraction from text and extrinsic update of database, or dialogue management for controlling conversations using both types of functions. It is of utmost importance to consider secure protocols to safeguard sensitive data when dealing with external functions and databases, optimize latency in conversations and compute overheads, handle loads of varying sizes and scalable interactions during large language model function calling.
Get the most out of Agentic AI with KritiKal
We discussed in this blog about the essentials of Agentic AI, its architecture and autonomy to decide, perform tasks, achieve goals, learn, and on its own. These systems have come far from the repetitive functioning of traditional AI and can acclimatize to diverse circumstances without any directive. Their key components include perception of the real-world, reasoning for understanding the input context, actions, and learning to refine over time. Agentic AI architecture enables flexibility, precision, scalability and wide reach through their intuitive nature and can be powered by a few types of architecture such as vertical, horizontal, multi-agent, single agent, and hybrid, while external and intrinsic function calling is necessary in all these types.
KritiKal can assist you in designing, developing and embedding agentic architectures as per your business requirements. We can guide you through step-by-step processes on the way towards intelligent automation and AI model development that can future-proof your overall operational efficiency. Please get in touch with us at sales@kritikalsolutions.com to know more about our Agentic AI solutions and realize your innovative thoughts and ideas for improving business efficacy by kickstarting the AI journey.
Manav Vyas currently works as a Software Engineer at KritiKal Solutions. He is proficiently skilled in data structures, C++, Python, web development, full stack development, and more. With his ability to work efficiently in teams he has assisted KritiKal in delivering various projects to some major clients.