What is Agentic AI and How Can It Transform Business Workflows?

The McKinsey US CSO survey reveals that more than 90% of companies are using generative AI, but only 1% have reached the level of maturity where artificial intelligence changes their workflows fundamentally.

Although generative AI development holds great potential for automating and enhancing various workflows, it still serves as a supporting tool with minimal to no levels of autonomy.

To ensure a complete AI transformation, companies should use autonomous AI systems that can proactively handle workflows. This is where agentic AI comes into play, serving as a powerful tool to enhance processes across a wide range of domains, particularly in advanced industries. For example, in logistics, the use of agentic AI can lead to a more than 20% drop in inventory and logistics costs.

In this article, we will explore agentic AI, along with its key properties, standout benefits, and possible use cases.

Agentic AI refers to artificial intelligence systems that can autonomously work toward achieving goals. Unlike generative AI, which passively follows predefined instructions, agentic AI can independently analyze the environment or inputs, develop autonomous reasoning, take action on its own, and proactively adapt to the changing conditions.

The very basic workflow for agentic AI looks as follows:

• Input/goal
  • User request, task definition, or business objective
  • Perception and understanding
  • Interprets the input
  • Analyzes available context, data, and environment
• Planning
  • Breaks the goal into smaller tasks
  • Chooses the best strategy or workflow
• Action execution
  • Uses tools, APIs, or applications to perform tasks
  • Can trigger multi-step processes (e.g., fetch data → analyze → create report)
• Feedback and adaptation
  • Evaluates results against the goal
  • Adjusts actions or retries if needed
• Output/result
  • Delivers final outcome to the user (e.g., decision, document, analysis, completed workflow)

Examples of agentic AI solutions that follow this or similar workflow principles include:

• AutoGPT. It is an open-source autonomous AI agent that receives a goal delivered in natural language, decomposes it, and uses different tools and workflows (e.g., web browsing, API requests to third-party services) to execute actions. It uses feedback loops and iterates until the goal is achieved.
• BabyAGI. A modular agentic framework that can be applied across domains like market research or social media management. BabyAGI receives a high-level goal, generates a task queue, prioritizes these tasks, performs them, and generates new subtasks if needed.

Agentic AI solutions significantly differ in their architecture, as they often incorporate experimental approaches. Still, many of them share a layered structure that typically includes:

Foundation:

• Generative AI and/or large language model (LLM). It can answer prompts, generate documents, write code, or summarize texts

Agency layers added around an LLM:

• Goal-setting layer. It takes a goal and breaks it into steps, often using techniques like chain-of-thought reasoning, task decomposition, or external planners.
• Memory layer. It involves short-term scratchpads for reasoning steps and long-term memory based on vector stores, knowledge graphs, and databases.
• External tooling. The LLM determines when and how to invoke tools such as search engines, databases, or external software via API calls.
• AI loops. Agents evaluate their own outputs and decide whether to refine, retry, or move forward.

The autonomy of agentic artificial intelligence is often achieved by combining multiple specialized agents that collaborate, each with its own role. In particular, the researcher agent gathers and drafts the information to the critic agent, which reviews and refines it. The output is delivered to the executor agent that carries out the action. The sequence repeats in loops until the system achieves the defined goal.

Thanks to their smart use of diverse artificial intelligence capabilities, agentic AI solutions can deliver significant business value, which is illustrated in the image below.

Overall, agentic AI takes capabilities of generative AI one step further. In the next chapter, we will discuss the difference between generative AI and agentic AI in more detail.

While traditional LLMs and generative AI can be compared to factories producing predefined outputs, agentic AI functions more like an autonomous employee with specialized skills, able to handle specific workflows with minimal human involvement. The table below shows the difference between generative AI and agentic AI in more detail.

Agentic AI systems significantly outperform traditional generative AI with 34.% reduction in task completion time. There are many other notable benefits that agentic AI systems can bring to a business. Let’s explore them in more detail.

Improved workflow efficiency

According to McKinsey, banks that have implemented agentic AI have already achieved productivity gains of up to 60%. By helping employees handle routine activities more efficiently, artificial intelligence frees up their focus and resources for more complex tasks that require human creativity and critical analysis. Although agentic AI may not be suitable for making strategic business decisions, it is well-positioned to handle routine workflows, such as document management and compliance checks with limited human oversight.

This is particularly relevant in processes where speed and activity volume are criticalFor instance, using agentic AI allows sales teams to double prospecting efforts, and lead to a 40% increase in order intake.

Cost-effectiveness

Agentic AI helps businesses reduce labor costs and make more informed decisions by automating repetitive tasks, enabling proactive business forecasting, and supporting a wide range of operations. By automating repetitive tasks, enabling proactive business forecasting, and supporting a wide range of operations, agentic AI helps businesses reduce labor costs and make more informed decisions. For example, in advanced industries, the use of agentic AI can lead to cost savings that range from 30 to 50%. The image below illustrates the forecasted impact of agentic AI on the revenues of companies in the automotive industry.

Improved workflow quality

The support from agentic AI can enhance the accuracy of task completion by 7.7% across industries. This can be largely attributed to the agentic AI systems’ monitoring and testing capabilities. An agentic AI system typically consists of multiple specialized agents, each tailored to perform distinct yet interconnected tasks. For instance, they can include verifier, or “critic,” agents able to validate the work of research agents for accuracy, reliability, and actionable quality.

When it comes to managing software, such solutions ensure 24/7 test coverage that efficiently complements the efforts of human specialists. For instance, the use of agentic AI for generating test descriptions can help quality assurance engineers save 50% of their time.

Boosted innovation and flexibility

While agentic AI platforms enhance productivity across industries, their greatest impact is seen in the technology sector, where they deliver up to a 45% increase in productivity. One reason is that this domain thrives on flexibility and innovation — the very areas where agentic AI shines.

Unlike generative AI systems that rely on reactive decision-making, agentic artificial intelligence enables real-time optimization. From enhancing R&D workflows to conducting independent research, agentic AI provides solid support in developing and adopting new technologies that fuel innovation across industries.

Agentic AI supports a wide range of use cases, so listing them all would require an entire dissertation. Instead, here we highlight the most common applications across different industries.

Email management and prioritization

Agentic AI can help companies enhance email campaigns and customer communication by analyzing incoming emails, categorizing them by their urgency, and automatically delivering informative responses. While generative AI can also cover such workflows, agentic AI brings greater flexibility and independence to email communication thanks to its proactive reasoning. Such a principle is applied in Leobit’s AI-powered email auto-response solution that filters and scores leads, as well as provides informed and personalized responses to requests from potential clients. Such an approach can significantly reduce human involvement in customer relationships.

Calendar scheduling and meeting coordination

Scheduling meetings is a persistent challenge for remote teams operating across multiple time zones. Research shows that 32% of remote employees in countries without strict working-hour regulations often work beyond their scheduled hours. Finding mutually convenient times can be complex, but AI can help ease the burden. Agentic artificial intelligence can integrate with calendars and communication platforms to schedule meetings, send reminders, and even prepare agendas.

Customer support

A Salesforce survey on sales, service, and generative AI reveals that 63% of service professionals believe that the technology will help them serve their customers faster. Agentic AI can take customer support capabilities of generative artificial intelligence one step further. The technology can efficiently prioritize tickets and auto-generate informative answers that go beyond the capabilities of a regular LLM. This allows businesses to minimize human involvement in customer support, cutting labor costs and significantly reducing response times.

Lead generation and follow-up automation

Agentic AI can use advanced analytics to find leads and categorize them based on industry and priority. It can handle research across multiple channels, covering diverse workflows, such as simultaneously enriching CRM data and initiating follow-up sequences. Similar functionality is implemented in Leona, or AI-powered sales assistant. Leona scans job platforms for promising cooperation possibilities, scores leads, and automatically generates business proposals aligned with specific industries and case studies. It significantly boosts the efficiency of our sales team, allowing the specialists to focus on high-value prospects.

Market analysis and business intelligence

Gartner predicts that by 2027, up to 20% analytical processes will be fully managed and executed by autonomous analytics platforms. Such autonomy can be enabled by agentic AI, responsible for continuous market research and business intelligence workflows. In highly dynamic and competitive industries like finance, companies can gain significant value from autonomous analytical tools that continuously monitor investment trends, deliver regular insights, and provide detailed recommendations on investment and trading opportunities.

Social media content planning and posting

Thanks to their cross-functional capabilities, agentic AI solutions can efficiently manage social media campaigns and routine marketing workflows. They can plan content calendars, generate posts, monitor engagement, and recommend improvements based on audience response. For example, Starbucks increased social media engagement by 30% through Deep Brew, its AI-driven marketing platform that powers personalization and market research.

Report generation and data summarization

Agentic AI can run autonomous research to consolidate data from multiple sources and generate detailed reports supported by actionable recommendations. In retail, for instance, an agentic AI solution can pull data from various sales channels to produce accurate dashboards and financial summaries. Such an agentic AI workflow will help data analysts save time for more complex and strategic tasks.

Budgeting assistance and financial forecasting

Agentic AI can increase productivity in the finance sector by up to 42%. Its advanced research and workflow orchestration capabilities are very effective in retrieving and analyzing metrics from diverse channels. By layering different functions, agentic AI can simultaneously assess past performance, market conditions, and current financial data to generate accurate forecasts and simulate budget scenarios. These capabilities help businesses anticipate risks and develop effective cost-optimization strategies.

Procurement and resource management

With its ability to improve resource utilization by 13.6%, agentic AI represents a strong opportunity for advancing procurement and resource management. It can request quotes, facilitate communication with vendors, monitor business inventory, and provide suggestions on resource allocation across industries, ranging from e-commerce to manufacturing.

Project management task assignment and tracking

Many project management methodologies are shifting towards hybrid models, which ensure greater flexibility. However, this shift also introduces new risks, such as poor coordination, communication gaps, and challenges for project managers adapting to more dynamic approaches.

Agentic AI can help mitigate these risks by handling workflows such as assigning tasks based on availability and skillsets, tracking progress, sending reminders, and escalating issues when deadlines are at risk. As a proactive tool that automates multiple processes simultaneously, agentic AI can become an essential ally for project managers in maintaining control.

HR onboarding and employee workflow automation

Agentic AI can also provide strong support in the HR management domain. Such solutions can cover a variety of HR management processes, ranging from processing and organizing employee documents to sharing onboarding materials or responding to employee requests. This will help HR teams provide a smoother employee onboarding and enhance the overall workplace experience. Some of the mentioned features are implemented in Leonardo, our AI-powered HR assistant. The solution accesses the company’s curated database to provide smart responses to employee questions on coworkers, vacations, and company policies. It also facilitates the search for dynamic corporate documentation.

Compliance and security

Agentic AI solutions can provide support with regular compliance checks and security audits. They can scan suspicious activity, review logs, flag potential risks, and generate audit-ready reports. Such solutions are especially useful for industries bound by strict regulations. For instance, they can play a vital role in healthcare, where a violation of HIPAA regulations for patient data protection may cost an organization $1,500,000 annually.

With its innovative nature, agentic AI can create many business opportunities. However, developing and adopting such solutions might be a very challenging task.

Despite all the promises that agentic AI holds, Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027. While building solutions powered with generative AI is a challenging task, agentic AI development requires even more investment and transformative effort. Companies shifting towards agentic AI should adopt a strategic transformation approach rather than pursuing scattered initiatives. Siloed AI development teams should be replaced with cross-functional transformation squads, while ingenious and inventive approaches to AI development need to be prioritized.

Such an approach requires considerable skill, which becomes a significant challenge considering that 55% of organizations report a shortage of skilled professionals capable of developing and maintaining agentic AI software.

Many companies rely on outsourcing to compensate for such shortage of in-house talent and expertise. In fact, the global IT outsourcing market is projected to grow at a compound annual rate of 6.51% between 2025 and 2030, underscoring its increasing relevance.

Leobit, a team with a strong AI development and adoption expertise, is ready to help fill the skill gap. As a Microsoft Solutions Partner for Digital and App Innovation and a company that has successfully applied several AI agents to support our internal processes, we have a deep understanding of AI technologies and their promises. Our specialists have experience of working with:

• Public AI services and models like ChatGPT or OpenAI API, Google Gemini​, and GitHub Copilot
• Privately hosted AI models and services like Llama 3, Mistral 7BGPT-J,​ GPT-NeoXPhi-2​ NVIDIA NeMo

We are ready to use these solutions, the most efficient AI development tools like ReAct agent, and custom AI algorithms to help you transform your business with agentic AI.

With its independent reasoning, proactive nature, and ability to autonomously switch between multiple workflows, agentic AI can handle complex workflows with minimum human involvement. Unlike generative AI, which excels in producing content or outputs based on prompts, agentic AI systems can manage end-to-end processes.

Such solutions shine in scenarios that require decision-making, continuous research, and coordination across tools or systems. In such cases, agentic AI platforms boost businesses’ workflow productivity, cost-efficiency, and process quality, as well as drive flexibility and innovation.

Meanwhile, developing and adopting agentic AI systems requires strong AI development expertise. At Leobit, we maintain a strong focus on artificial intelligence, backed by a team of skilled software engineers with hands-on expertise in agentic AI systems.

Contact us to explore the benefits of agentic AI.