The agent understands the customer request in natural language
Agentic Automation - The next evolution of process automation
Content:
In the previous parts of our blog series, we looked at the basics of classic RPA solutions and showed how the integration of AI components leads to intelligent automation. In the fourth and final part, we look to the future:
Agentic Automation - a new dimension of process automation made possible by the rapid development of Large Language Models (LLMs) and autonomous AI agents.
From automation to autonomy - a paradigm shift
The previous stages of process automation followed a clear pattern:
- Classic RPA: Automation of rule-based, structured processes according to the "if-then" principle.
- Intelligent Automation: Expansion with AI components for processing unstructured data and limited decision-making.
Agentic automation now goes a decisive step further: instead of simply executing pre-programmed instructions or making decisions within a narrowly defined framework, these systems act as autonomous, intelligent agents. They understand tasks in natural language, plan solutions independently, make complex decisions and interact seamlessly with people and other systems.
What is Agentic Automation?
Agentic automation refers to the use of AI-controlled, autonomous agents, that can independently execute, monitor and optimize complex business processes. These agents are based on advanced Large Language Models (LLMs) and are capable of
- Understand and interpret tasks in natural language
- Plan largely independently how best to complete a task
- Access different systems and information sources
- Making complex decisions under uncertainty
- Evaluate results and improve your own approach
- Communicate naturally with human team members
Unlike classic RPA or intelligent automation, agentic automation does not require all steps and decision paths to be explicitly programmed. Instead, the agent gains an understanding of the goal and independently develops strategies to achieve it.
The technological foundations: LLMs, tool use and reasoning
The rapid development of Agentic Automation is made possible by several technological breakthroughs:
Large Language Models (LLMs)
LLMs such as GPT-4o, Claude Sonnet 3.7 and Llama 4 have the ability to deeply understand and generate natural language. They can interpret complex instructions, process contextual information on a large scale, use knowledge from their pre-training phase and communicate consistently and coherently.
Tool Using and Function Calling
Modern LLMs can interact independently with external tools and systems. They are able to call APIs to retrieve information or trigger actions, query databases and interpret the results. They can also perform calculations, process data and create, read and analyze documents.
Chain-of-Thought Reasoning
This technology enables LLMs to solve complex problems step by step. They break problems down into sub-steps, draw logical conclusions and weigh up options. They are also able to check and correct errors in reasoning themselves and justify decisions in a comprehensible manner.
Multimodal processing
Advanced agents can process various forms of information: Text in different languages and formats, images and visual information, tables and structured data and, in the future, audio and video content.
Agentic automation in practice: areas of application
The possibilities of Agentic Automation are diverse and are growing continuously. Here are some particularly promising areas of application:
1. autonomous customer service management
Scenario:
An AI agent takes over the complete processing of customer inquiries - from the initial contact to the solution.
Functionality:
It accesses relevant customer data, contract details and knowledge databases
In standard cases, it solves the problem independently (e.g. password reset, status update)
In more complex cases, he plans the necessary steps and carries them out (e.g. cancelation, tariff change)
He proactively keeps the customer up to date and communicates in natural language
If necessary, it escalates to human specialists, but prepares all the information
Added value:
The agent is available 24/7, has no waiting times and can process thousands of requests in parallel. It delivers consistent quality and continuously learns from new cases. Human customer advisors are freed up for complex, emotional or strategic interactions.
2. autonomous document management
Scenario:
An agent manages the entire life cycle of documents - from receipt to processing and archiving.
Functionality:
The agent monitors various inbox channels (e-mail, portal, inbox)
It classifies incoming documents and extracts relevant information
It initiates the appropriate workflow based on the document type
It independently researches missing information in various systems
Prepares draft responses or generates standardized documents
He monitors deadlines and ensures timely processing
Added value:
documents are processed around the clock without delay. Consistency is higher than with human processing, and the agent can recognize complex relationships across different documents.
Challenges and limitations
Despite the enormous potential, Agentic Automation also brings with it specific challenges.
Technological challenges
- LLM hallucinations: LLMs can occasionally generate incorrect or inconsistent information. Robust validation mechanisms are required for business-critical applications.
- Context limitations: The amount of context that an LLM can process is limited. This can be a challenge for complex processes with extensive information.
- Currency of knowledge: LLMs are based on training data up to a certain point in time. For up-to-date information, they must be linked to external information sources.
Regulatory challenges
- Transparency and explainability: In many industries, decisions must be comprehensible and explainable - a challenge for complex AI systems.
- Data protection: Access to extensive data sources raises questions about data protection and data security.
Organizational challenges
- Governance and control: How do you ensure that autonomous agents act in the interests of the company and comply with all guidelines?
- Accountability: Who is responsible for the decisions and actions of autonomous agents?
- Change management: The introduction of autonomous agents requires a profound rethink in the organization and new forms of human-AI collaboration.
The future of work: human-AI collaboration
Agentic automation will fundamentally change the world of work - not by replacing human workers, but through a new form of collaboration:
From execution to supervision
While agents take on repetitive and rule-based tasks, the role of humans is shifting to monitoring, controlling and continuously improving AI systems.
Focus on human strengths
Humans can focus on tasks where they are superior to AI, such as emotional intelligence and empathy, creativity and innovation, and ethical and social evaluation.
Your partner for digital transformation
The journey from classic RPA to Intelligent Automation and Agentic Automation requires an experienced partner. abat offers you:
- Holistic consulting: from strategy to implementation
- Cross-technology expertise: RPA, AI, LLMs and integration
- Agile implementation: fast results through an iterative approach
- Sustainable transformation: empowering your organization for the digital future
Conclusion: The evolution of process automation
In this four-part blog series, we have traced the evolution of process automation:
Part 1: Basics of robotic process automation- rule-based automation of structured processes
Part 2: Classic RPA solutions in practice - concrete implementation examples and best practices
Part 3: Intelligent Automation – the addition of AI components for unstructured data and limited decision-making
Part 4: Agentic automation – the next evolution with autonomous AI agents for complex processes and decisions
The future does not belong to a single automation approach, but to the interplay of their strengths. While RPA, AI and agents each cover specific task areas, their greatest added value comes from integration. Modern platforms combine classic process automation with intelligent and agent-based control - for end-to-end, scalable solutions that grow with the requirements.
Start your automation journey and become a digital champion!
Whether you are at the beginning of your automation journey or already using advanced RPA solutions - we will accompany you on the path to digital transformation. Contact us for a no-obligation initial consultation and find out how modern automation technologies can make your company fit for the future.
Are you ready for the next level of process automation? We look forward to hearing from you!
FAQs
1. What is Agentic Automation?
Agentic automation refers to the use of autonomous AI agents that understand, execute and optimize complex business processes independently - without explicit programming of each individual action.
2. How does Agentic Automation differ from classic RPA?
3. Which technologies enable agentic automation?
4. Where is Agentic Automation used in practice?
5. What challenges are there when introducing Agentic Automation?
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