The topic of Agentic AI has gained significant popularity over the past year. Many major tech companies have announced or already launched new products based on this technology. In this blog post, you'll discover what autonomous AI agents are all about.
AI is evolving at a rapid pace. Recently, this development has reached a new milestone: AI systems can now make independent decisions and execute tasks largely autonomously without human intervention. These self-acting AI systems are referred to as “agents,” which is why terms like Agentic AI or Agentive AI are becoming more common.
From Recommendation to Action: The Shift from Generative AI to Agentic AI
These terms are used in various contexts. The key differentiator is that unlike generative AI models, Agentic AI can independently initiate and execute tasks, make real-time decisions, and dynamically perform actions with minimal or no human assistance. The prerequisite is that Agentic AI must be "trained" like a human employee and then, just like them, be able to independently handle corresponding tasks.
Increased Productivity Through Autonomous Systems
Generative AI models (GenAI) use large language models (LLMs) to generate functional code and assist with creating high-quality content and operating chatbots. However, GenAI lacks the ability to act proactively without prompts, draw independent conclusions, and take corresponding actions.
A recent report by Gartner highlights the challenges. It estimates that at least 30% of GenAI projects will not progress beyond the proof-of-concept stage by the end of 2025. Key factors include poor data quality, inadequate risk controls, high costs, and unclear business objectives.
Autonomous AI Agents as Innovation Drivers for CRM and Business Processes
Autonomous AI agents enable significantly more efficient business processes with less personnel effort than before. This gives them disruptive potential, particularly in fields like customer relationship management (CRM). For example, Salesforce’s Agentforce offers AI-powered conversation agents for CRM, marketing, and data management. These agents autonomously personalize customer interactions, process inquiries in real time, and proactively suggest solutions. Salesforce CEO Marc Benioff predicts that there will be one billion AI agents in Salesforce by 2026.
Additionally, Agentic AI takes on tasks such as data analysis, report generation, and the automation of recurring workflows. This helps relieve employees and optimize resource allocation.
How Does Agentic AI Work in Practice?
If a suitable use case for Agentic AI exists, the implementation should be based on an AI-powered architecture. Seamless integration into existing frameworks, automation, and orchestration tools is essential. Without proper integration, there is a risk that the result will be just another conventional GenAI solution—lacking autonomy, depth, and versatility, which are the key characteristics of Agentic AI.
Thinking About Agentic AI Holistically
An ideal agent-based AI system should be capable of connecting with a variety of enterprise systems and applications. Ideally, it should span multiple business areas rather than being limited to a single function. In Salesforce, for example, different clouds such as Sales and Service Cloud can centrally access data via Data Cloud, increasing cross-departmental process efficiency.
Another crucial aspect of autonomous AI systems is their ability to draw meaningful conclusions. This can be envisioned as LLMs working within multi-agent systems, where specialized agents collaborate to complete tasks, ensuring instructions are understood and autonomously executed.
AI Agents Must Be Custom-Tailored
There is no universal technical approach for implementing Agentic AI. Each system must be adapted with domain-specific LLMs, whose effectiveness depends on how well business-critical data is integrated—whether in finance, IT, HR, or customer service.
Current Status: How Far Have Autonomous AI Agents Progressed?
Advancements in natural language processing and machine learning suggest vast potential for Agentic AI. Despite significant progress in AI agent development, the technology is still in a relatively early stage.
Challenges and Limitations of Autonomous AI Systems
Despite its high potential, deploying autonomous AI systems presents challenges. Since agents often handle sensitive information, data protection measures must be in place. Security risks such as misuse or system manipulation also need to be addressed. Transparent algorithms and robust security mechanisms are crucial to building trust and ensuring safe deployment in critical environments.
Data Quality Determines the Viability of Agentic AI
Some autonomous AI systems are already performing complex tasks in customer service, process automation, and decision-making. However, their application in dynamic, unstructured environments is still limited. Key challenges include integration into existing systems and ensuring high-quality, consistent data availability.
The Growing Importance of Autonomous AI
AI remains one of the most critical tech topics for businesses in 2025. Within this landscape, Agentic AI stands out as a key trend, ushering in a new era of automation and efficiency. Despite existing limitations, many companies have identified potential use cases or are close to leveraging the rapid evolution of autonomous AI systems.
Why Companies Must Prepare for Autonomous AI Now
Agentic AI offers new perspectives for digital transformation. When all necessary conditions are met, these AI systems provide significant advantages over traditional GenAI models. Their core capabilities – autonomy, deep comprehension, enhanced learning, and integration across diverse data sources – allow them to initiate, execute, and optimize complex workflows with little to no human intervention.
This opens up innovative use cases across nearly all industries. In healthcare, AI agents can support diagnoses and provide personalized patient guidance. In logistics, they can optimize route planning and inventory management. In finance, they enhance fraud detection and risk assessment. In marketing, AI agents drive better campaigns through personalized content, precise audience segmentation, and automated briefings.
Ultimately, businesses across all industries will see a new form of automation emerge, as AI agents can independently make informed decisions even in increasingly complex scenarios.
Act Now and Pave the Way for Autonomous AI
The rapid development of autonomous AI agents is unlocking entirely new opportunities for businesses to optimize processes and innovate. Although fully realizing the technology’s potential remains challenging, companies should begin exploring Agentic AI today.
Early investments in the development and integration of Agentic AI solutions will secure competitive advantages through efficiency gains, cost savings, and the creation of new value chains.
