What agentic AI is, how AI changes business, what challenges you should address on the way to successful implementation of agentic AI — the answers are in our new long read.
The implementation of AI has been astonishing in recent years, with estimated value between $11 trillion and $18 trillion worldwide. However, the rise of Agentic AI has marked a new frontier in the development of traditional artificial intelligence, and this innovation wave doesn’t seem to cease.
Thus, in 2025, the global agentic AI market is estimated at $7.55 billion, and this number is projected to expand at a compound average growth rate of 43.84%, hitting the $199.05 billion mark by 2034. Besides, according to a new McKinsey report, agentic AI has the potential to uncover $2.6 trillion to $4.4 trillion of additional value on top of the traditional AI value, powering primarily enterprise fields like marketing and sales, customer service, and supply chain.
Read on to delve into the ins and outs of this tech and understand how agentic AI adoption will help you get a competitive edge.
What Is Agentic AI?
Agentic AI is an advanced artificial intelligence system that executes complex, multi-step tasks as well as makes independent decisions with limited human supervision / intervention. As opposed to traditional AI systems that function according to predefined rules, agentic AI has goal-driven behavior and adaptability that allow it to proactively initiate actions.
To better understand the difference, here’s a case in point. In traditional commerce, to return an item, the customer has to manually fill out a form, print a label, and physically ship the product, because a simple AI bot fails to resolve the issue, escalating it to a human assistant. Conversely, in agentic commerce, the AI agent will detect the problem, start the return process, email a return label, update the inventory system, and schedule a refund without any human intervention.
To perform such complicated actions automatically, agentic AI systems consist of AI agents — separate ML models that mimic human decision-making to perform specific tasks in real time. Single specialized AI agents that can be for example in the form of chatbots answering FAQs operate as the building blocks of the larger, multi-agents system. They collaborate and communicate with one another to reach goals that no single agent could manage alone.
The workflow of how agentic AI systems operate can be explained through the following steps:
- Perception and sensing means the system collects data from the environment through data inputs, sensors, and cameras.
- Understanding and interpretation includes processing the gathered data via natural language processing, computer vision, and other ML mechanisms.
- Decision-making presupposes planning the optimal course of actions (through reasoning, planning, task decomposition, and predicting) to reach the assigned goals.
- Action and execution. The agentic AI system executes the plan by interacting with the external world, i.e. using APIs, running code, sending emails, controlling physical assets, etc.
- Learning and adaptation comprises evaluating the outcome against the expected goal, learning from failures, updating the internal knowledge base, and refining strategies for future performance.
More detailed information on how AI agents work through coordinated AI orchestration can be found in our recent blog post.
How AI Changes Business: Key Benefits
According to McKinsey, 23% of the respondents report leveraging agentic AI at least in one business function, and additional 39% start experimenting with AI agents. The growing adoption of the technology is no surprise considering great value it brings to business leaders:
- Business automation and enhanced efficiency In contrast to traditional AI that mainly focuses on performing isolated tasks, agentic AI has the power to manage entire workflows (from initiation to completion). Such business process automation leads to significant productivity gains, as human workers can focus on higher-value and creative tasks.
- Strategic agility and innovation. Agentic AI systems offer trailblazing analytics that improves over time as agentic AI learns and adapts to new experiences and situations. This results in proactive, more informed decision-making, while the overall business risk is reduced. Also, by enabling completely new ways of working, business leaders are empowered to create innovative products and services, getting a competitive advantage.
- Wow customer experience. As AI agents work 24/7 autonomously and without fatigue, your customers receive uninterrupted service and proactive issue resolution. On top of that, agentic AI technologies provide context-aware interactions across multiple channels and hyper-personalized recommendations, increasing customer satisfaction and loyalty.
Learn more about AI
implementation
How AI Changes Business: Real-Word Use Cases
AI agents can be tailored to automate specific workflows across an array of industries and business domains.
Financial Sphere
- Fraud detection. Underpinned by machine learning algorithms, AI agents monitor and analyze various data points (including transaction data, behavioral biometrics, device / network data, and employee behavior) for unusual patterns. Once potential fraud is indicated, AI agents communicate with banking systems to automatically freeze accounts. Companies like Mastercard leverage AI agents that transact on behalf of customers.
- Insurance claim processing can also be easily automated with AI technology. AI agents use OCR, NLP, and LLM tools to extract data from documents and cross-referencing policy coverage to automatically approve low-risk claims and flag more complex cases for human oversight. Among the companies that use agentic AI to automate the process are Lemonade, GEICO, ZurichInsurance Group, etc.
E-Commerce
- Customer service. AI agents can interact with customers in real time to perform tasks like ‘buying a bag’ or ‘booking a flight’. In this case, Agentic AI tools use the context given by the users and pair us with external platforms and APIs (e-commerce websites, payment providers, booking systems, etc.) to process the query — from choosing the optimal item / service and securely handling a transaction to automating delivery and performing post-transaction activities.
In our blog post about Agentic Commerce, you can find more details on how the process looks like.
- Inventory tracking. Thanks to seamless integrations with ERPs, WMSes, and POS systems (often using IoT sensors and computer vision), AI agents continuously monitor stock levels across multiple warehouses, stores, and sales channels. Based on this up-to-date view of stock levels as well as a deep analysis of sales history, market trends, seasonal patterns, promotions, social media buzz, and weather conditions, AI agents can also predict the demand, autonomously generate and place purchase orders with suppliers, thus preventing both stockouts and overstocks.
Manufacturing
- Quality control and predictive maintenance. Empowered by ML and computer vision capabilities, AI agents perform real-time inspection of products to find microscopic defects and anomalies, thus automatically taking corrective action or flagging the product for review. AI operators can also connect with IoT sensors to assess machine health and performance, autonomously adjusting necessary parameters, scheduling maintenance works, and connecting with third-party systems to order spare parts. All this helps increase, reduce waste, and minimize downtime.
- Supply chain optimization. Through a sophisticated ML analysis of historical sales, market demand, customer behavior patterns, and external factors (for example, weather or geopolitical events), AI agents forecast demand, optimize inventory levels, and manage logistics. They can autonomously adjust production schedules, reroute shipments, or engage with backup suppliers during disruptions, as a result, enabling supply chain resilience.
Challenges When Implementing Agentic AI And How to Address Them
Creating an overarching agentic AI system that would orchestrate an array of AI agents working smoothly is quite an uphill battle that requires deep technical knowledge. Here are some of the obstacles you might face during the implementation:
- Complexity of integrations. So that your agentic AI system could function without a hitch, you should make sure your AI agents are smoothly interconnected with one another, with your internal software, as well as third-party systems. For that to happen, introduce advanced shared knowledge bases and leverage robust messaging protocols (e.g., REST APIs, message queues, pub/sub systems). If you need pairing up with legacy systems that use proprietary interfaces or outdated data formats, be ready to deal with significant custom integration work, middleware solutions, or complete workflow re-architecting.
- Security issues. AI agents might be prone to data breaches, unauthorized access, adversarial attacks (that cause harmful decisions through input manipulation), model theft, and system vulnerabilities. To prevent data leaks or misuse in this regard, implement multi-layered security measures (encryption, firewalls, intrusion detection, regular security audits), multi-factor authentication, and role-based permissions. Also, regularly conduct vulnerability scanning, penetration testing, and security code reviews in controlled environments.
- Ethical issues. AI agents constantly work with personally identifiable information (PII), health records, and financial data, which might raise privacy concerns. To protect individual identities, anonymize or pseudonymize data, establish data governance policies, comply with the required standards (GDPR, CCPA, HIPAA), conduct data protection impact assessments, as well as focus on data minimization and purpose limitation.
- Unpredictability and bias. AI agents don’t rely on predefined rules as traditional AI, which might lead to unexpected decisions. To overcome this hurdle, build robust error detection, fallback, and ‘undo’ functionality. Besides, be aware of bias in Retrieval-Augmented Generation (RAG). Regularly update and curate knowledge bases (especially in dynamic domains), implement advanced retrieval techniques (for example, context-aware retrieval and embeddings with semantic search), incorporate data validation and filtering, as well as access data sources for credibility.
Agentic AI Implementation With Aetsoft
In the era where time is money, business process automation that additionally increases efficiencies and enhances customer experiences, is a must-have, not a luxury. However, it’s not that easy. To effectively operate as a business leader during the rise of agentic AI, you need to set up a foundation. This includes adaptation to the technology infrastructure, deploying agent-specific governance mechanisms, and even upskilling the workforce.
An expert in artificial intelligence and machine learning, Aetsoft is here to assist, from comprehensive consulting to AI agents customization / integrations to full agentic AI system implementation. You can rely on our expertise to ensure rock-solid system security, reliability, and adaptability (through various learning mechanisms) in your specific niche. Schedule a call with our specialists to discuss your project details.
