Predictive and generative AI models have captured significant attention in the business world for their ability to create unique content and enhance decision-making with data-driven predictions. However, at Creatio, we believe that the greatest benefits for businesses come from the seamless integration of generative, predictive, and agentic AI - a combination that redefines business productivity and efficiency on an entirely new level.
This article explores what agentic AI is and its potential to transform the way businesses operate.
What is Agentic AI?
Agentic AI is an advanced form of artificial intelligence designed to make autonomous decisions and execute complex tasks without constant human oversight. AI agents are capable of planning, automating tasks, evaluating performance, and optimizing workflows.
AI agents work autonomously by analyzing vast amounts of data to understand user or customer context and make decisions to execute tailored workflows. Agents can also learn from new scenarios, handle exceptions, and adapt to changing conditions to optimize workflows and improve processes.
Agentic AI is characterized by these key aspects:
- Autonomy - AI agents can execute and improve tasks independently without constant human intervention.
- Reasoning - AI agents can reason through complex scenarios and plan next steps.
- Complex decision-making - agents can make crucial decisions based on multiple factors and long-term goals.
- High adaptability - AI agents continuously learn and adapt to new data and changing conditions.
- Comprehension - AI agents can navigate and execute complex, multi-step processes by combining various models, data sources, and systems.
Agentic AI can think, plan, and act independently but can still benefit from users' guidance to align with business goals, ethical standards, and compliance regulations.
How Exactly Does Agentic AI Work?
Agentic AI operates through a combination of machine learning, natural language processing (NLP), and large language models (LLMs).
AI agents go through 5 steps to execute tasks:
- Perception - gather and process data from all available data sources.
- Reasoning - understand tasks and generate solutions.
- Action - integrate external tools and software to perform a task based on the formulated plan.
- Feedback and adjustment - monitor task execution, analyze outcomes, and refine strategies.
- Learning - continuously learn from each interaction to improve accuracy and efficiency in real-time.
This is a more detailed explanation of each step and its processes:
Step | What happens | How it happens |
| Perception and data gathering | The AI agent gathers and processes data from available databases and other digital interfaces using machine learning technology. | The agent identifies relevant features and recognizes objects or key entities from the business environment (customer data, performance metrics, product specifications). This step aims to understand the context and current state of the environment so that agentic AI can act accordingly. |
| Understanding tasks | The agent interprets instructions in natural language using Natural Language Processing (NLP) technology. | Large language models (LLMs) convert human language into structured, actionable representations using prompt engineering and template matching. The goal of this step is to break down complex tasks into more digestible ones, which agentic AI can work with. |
| Reasoning | The agent uses an LLM to understand tasks, generate potential solutions, and coordinate specialized models for specific tasks, such as content creation and recommendation systems. | The AI agent uses techniques like retrieval-augmented generation (RAG) to access the right information from diverse data sources to ensure accurate and relevant outputs. The goal of this task is to make informed decisions based on available data. |
| Task planning | The agent breaks down tasks and creates multiple action plans. | It generates task sequences and maps out the necessary steps while respecting dependencies (like prioritizing tasks and deadlines). The goal of this step is to ensure that tasks are performed in a logical order and that there are no errors. |
| Task execution | The agent starts executing tasks. | The agent integrates with various systems (such as CRM software, an e-commerce platform, or analytics tools) to complete actions based on its plans. The goal of this step is to accomplish the task(s) as effectively as possible. |
| Task monitoring | While the agent executes tasks, it monitors for success or failure signals. | Pre-programmed rules help the agent assess the outcomes and adjust if something goes wrong. The main goal of this step is to adapt the approach and avoid unnecessary errors. |
| Fallback options | If the agent faces obstacles or unexpected errors, it refers to fallback options. | The agent uses success/failure metrics to choose alternative paths, ensuring it can still complete tasks even when errors or changes occur. The main goal of this step is to maintain flexibility and ensure there’s progress despite challenges. |
| Learning | The agent logs each action, decision point, and outcome. | Data generated during interactions with users and other systems is stored in the system and used by AI agents to learn and improve. The main goal of this step is to learn from past experiences, refine processes, and improve decision-making over time. |
Benefits of Working with Agentic AI
Agentic AI is a powerful tool that helps businesses streamline operations, enhance decision-making, and improve productivity.
In this section, we explore the key advantages of integrating agentic AI into business operations:
Increased efficiency and productivity
Agentic AI enhances efficiency and productivity by automating complex processes that traditionally required human attention. Unlike conventional automation, AI agents don’t just follow predefined rules, they can autonomously make decisions to execute tasks and optimize workflows, freeing up employees to focus on creative and strategic work.
Proactive problem-solving
Agentic AI doesn’t just automate tasks and follow users' instructions. Unlike traditional systems that require humans to react to problems after they occur, AI agents continuously monitor operations, analyze patterns, and take proactive actions to mitigate issues before they occur.
For example, agentic AI can make a decision to adjust transportation routing by analyzing real-time traffic and weather data, without waiting for a human employee to notice that there might be an issue.
Enhanced customer experiences
Agentic AI transforms customer interactions by making them more timely and personalized. Agentic AI analyzes user messages, preferences, and situational context to provide tailored and relevant help without engaging customer support specialists. AI agents can engage with customers 24/7 in natural language, providing instant support, answering questions, and even performing tasks on their behalf, like filing a complaint.
Implementing agentic AI systems reduces wait times, improves resolution rates, and ensures a smoother, more engaging customer experience - ultimately driving higher customer satisfaction and loyalty.
Flexibility and adaptability
One of the biggest advantages of agentic AI is its ability to adapt to changing conditions without human involvement. Agentic AI systems learn from new data in real time and can independently adjust to shifting business needs, changing conditions, and new challenges.
Data-driven decision-making
Agentic AI empowers businesses with smarter, faster decision-making by analyzing vast amounts of data in real time. Instead of relying on static reports or manual analysis, AI agents continuously process and interpret data, uncovering patterns, trends, and insights that might otherwise go unnoticed. With this capability, businesses can make more informed, data-driven decisions.
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Agentic AI Use Cases
Users can partner with AI agents tailored to their role and work together to fulfill certain tasks. Here are some use cases across different business functions and operations, including marketing, finance, and sales:
Marketing
AI agents support marketing professionals by autonomously managing and optimizing marketing campaigns. By analyzing audience engagement and performance metrics, AI agents can adjust bidding and refine targeting strategies in real time to increase campaign ROI.
Agentic AI systems for marketing can automate:
- Audience segmentation - automatically group customers into similar segments and update segmentation based on new data
- Campaign execution - automatically execute marketing campaigns by analyzing customer behavior, such as abandoning shopping carts, downloading resources, browsing certain product categories, viewing pricing page, etc.
- Campaign optimization - analyzes campaign performance and adjusts ad bidding, ad placement, channels, ad variation, etc.
For example, an e-commerce company can implement agentic AI to manage a multi-channel advertising campaign to promote a new line of products. An AI marketing agent monitors ad performance across Facebook, Google, and Instagram in real time and notices that a particular segment of users prefers video ads rather than static images. Instead of waiting for a marketing specialist to make changes to the ad format, the AI agent independently shifts it to video for those users, further improving their engagement.
Sales
Agentic AI acts as an intelligent assistant, helping sales professionals focus on establishing relationships with clients by automating a significant portion of sales agents’ everyday workload.
For example, AI agents can autonomously update prospect data based on calls and chats, schedule meetings with the most valuable prospects, and automatically send follow-up messages.
Additionally, agentic AI for sales can automate:
- Lead and opportunity scoring - automatically assess leads and opportunities to determine how likely they are to become customers
- Lead distribution - assign the best sales representative based on skills, experience, location, etc.
- Pipeline management - monitors the sales pipeline and takes actions to keep deals moving, such as sending automated emails, scheduling calls, etc.
- Territory optimization - assigns sales representatives working in the field based on their availability, experience, and location.
Agentic AI can autonomously execute sales workflows based on a potential client's behavior. For example, when someone fills out the contact form, an AI agent can analyze their data, look for additional information in various databases to create a complete profile, automatically send a welcoming message, schedule a meeting with the rights sales agent, and support sales professionals during meetings by providing valuable insights that will help them close the deal faster.
Customer Service
Traditional chatbots help improve customer service by answering common questions and providing timely support 24/7, however, agentic AI transforms customer service by autonomously making decisions and executing tasks on behalf of customers.
Businesses that leverage AI-powered customer service agents can resolve issues faster, reduce operational costs, and deliver proactive support, without increasing human workload. Gartner predicts, that by 2029, agentic AI will autonomously resolve 80% of common customer support issues, which will help businesses reduce operational costs by up to 30%.
The biggest advantage of agentic AI for customer service is that AI agents can analyze customer requests, make real-time decisions, and take actions on their behalf, such as canceling subscriptions, rescheduling appointments, processing refunds, etc.
For example, an AI agent can analyze a customer’s request to return a product. Instead of sending them generic instructions, an AI agent can autonomously verify order details, assess their eligibility for a refund, fill out the return form with customer information from the company’s database, prepare a return label, and send all the information to the customer, all within seconds and without engaging customer service agents.
Agentic AI for customer service can also automate:
- Case routing and prioritization – analyze the sentiment, urgency, and complexity of the case to prioritize and route cases to the best-suited agent.
- Process service request – analyze the service request, logs it into the system, assigns service agent based on location and availability, and updates customer profile based on service documentation.
- Case resolution recommendations - search the knowledge base, previously resolved cases, and other reliable sources to provide insights on how to best resolve a specific case.
- Knowledge base updates - analyze resolved cases and suggest updates to the knowledge base.
Finance
Agentic AI supports finance departments by autonomously handling transactions, monitoring risks, and processing invoices. Unlike predictive AI, which only provides actionable insights, agentic AI can take action to improve efficiency, security, and accuracy.
AI agents can autonomously:
- Monitor transactions and prevent fraud - continuously scan financial transactions, detect anomalies, and take immediate action to prevent fraud before it impacts the company and its customers.
- Process invoices – process invoices and match them with purchase orders, check invoices for errors, and streamline approvals.
- Process loans - financial institutions can use agentic AI to evaluate loan applications, check creditworthiness, and automatically approve or adjust terms based on risk assessment.
- Manage portfolios - wealth management and investment firms can implement an AI agent to monitor market conditions and adjust portfolios to align with clients' goals and risk tolerance.
Agentic AI systems can analyze transaction patterns to flag suspicious activity, helping businesses fight with cyber-security threats and money-laundering activities. For example, if a bank’s AI agent detects an unusual transaction from a customer’s account in a foreign country, it can freeze the transaction, alert the customer via a mobile app, and request verification, preventing potential fraud.
Agentic AI in the Enterprise - Driving Innovation and Business Transformation
Agentic AI transforms how enterprises operate, compete, and deliver value by enabling intelligent, autonomous, and self-optimizing process automation. A great example of agentic AI for enterprise needs is Creatio - an AI-native no-code platform that combines key AI aspects, including generative, predictive, and agentic AI to automate end-to-end business processes, enhance customer engagement, and optimize business operations.
Creatio.ai is a virtual AI assistant that executes workflows and performs tasks on behalf of users with minimal human oversight. Thanks to native AI capabilities integrated directly into the system, users can benefit from all AI capabilities by using natural language and a simple interface. With built-in awareness of all objects, relationships, data, and processes, Creatio.ai can act upon all the information available on the platform.
AI agents are an integral component of the Creatio platform, delivering insights, prioritizing work, and assisting in employees' daily tasks. They monitor the business environment, automate actions, and recommend next steps to enhance employee productivity.
Employees can tailor agents to fit their preferences and unique tasks or create new AI agents to better answer their requirements. By taking advantage of Creatio composable no-code architecture, users can create new AI agents without writing any code and easily integrate them into existing workflows.
Future of Agentic AI
Artificial intelligence rapidly evolves from automating repetitive tasks to intelligent, autonomous systems. As organizations adopt modern agentic AI systems, the workplace will evolve, with AI agents taking on more complex responsibilities, collaborating with employees, and making decisions fully independently.
In this section, we explore how agentic AI is expected to develop in the coming years and the transformative impact it will have:
Advancing adaptation of agentic AI technology
As we look ahead, the integration of agentic AI will transform business operations, leading to more autonomous workflows and enhanced decision-making capabilities. Gartner forecasts that by 2028, 33% of enterprise software applications will incorporate agentic AI, a significant increase from less than 1% in 2024. According to Forrester’s research, within the next three years, agentic AI will gain trust, and enterprises will use AI agents to control and manage a significant portion of their processes.
Gartner predicts that by 2028, as much as 15% of daily work decisions will be made autonomously, and organizations that proactively embrace agentic AI will be the ones reaping the fruits.
Evolution of agentic AI
According to Forrester, over the next few years, agentic AI will evolve from role-specific agents to multi-agent systems capable of coordinating multiple use cases at once. As technology evolves, multiple agents will be able to communicate and collaborate to execute increasingly complex and multi-layered processes.
Forbes predicts that in the near future, AI agents will evolve from simple tools into partners. Employees will develop more collaborative relationships with agentic AI that will become a crucial part of everyday operations, enhancing problem-solving capabilities and business innovation.
At Creatio, we believe agentic AI enhances human potential, which is why our platform enables seamless collaboration between human and digital talent. AI agents seamlessly integrate into daily workflows, adapting to individual tasks and preferences to enhance users’ productivity. By taking over time-consuming and repetitive tasks, they allow employees to focus on more creative and strategic work. Businesses that embrace this combination of digital and human talent can reach new heights in productivity, efficiency, and agility, reshaping the future of business operations.
