AI Trends · 15 October 2025 · Updated 19 April 2026
Agentic AI: How autonomous AI agents are reshaping the workplace
Agentic AI combines perception, planning and execution in intelligent agents. Learn how companies are already deploying this technology in production and which jobs are changing.
Author
ai-edu Team
AI Training Experts
From chatbot to autonomous agent
While classic chatbots mostly react to predefined dialogues, the term Agentic AI refers to a new generation of autonomous systems. These agents combine perception, goal-setting, planning, execution and learning to complete tasks on their own.
Agentic AI is much more than a conversation partner: these are AI units that act with a certain “will”. They define goals, build plans, access tools and data, and dynamically adapt to changing conditions.
“The difference between a chatbot and an agent is like the difference between a calculator and an accountant.”
Three principles of the agentic mindset
For autonomous agents to work reliably, they need to master certain capabilities:
1. Initiative
Agents make their own decisions and pursue goals, rather than just reacting to inputs. When an agent is tasked with creating a report, it searches for data sources on its own instead of waiting for further instructions.
2. Adaptability
They adjust plans to unexpected events and use feedback to optimize. If one approach fails, they try alternatives, just like an experienced employee.
3. Multi-step reasoning
Complex tasks are broken into sub-tasks and worked through step by step. The agent plans ahead, prioritizes steps and coordinates different tools and data sources.
How agents work in practice: an example
Imagine giving an agent the following task:
“Analyze our sales figures for the last 12 months, compare them with the previous year and create a presentation for the management meeting.”
A classic chatbot would respond: “Please upload the data and describe which analyses you want.”
An agentic system, on the other hand:
- Accesses the CRM and exports the relevant data
- Cleans the data and identifies outliers
- Performs the analysis (trends, comparisons, forecasts)
- Creates visualizations (charts, tables)
- Generates a PowerPoint presentation with executive summary
- Sends an email with the finished document
The entire process runs autonomously. The human receives the finished result.
Practical examples from companies
Early adopters are already running Agentic AI in production. For teams looking for a no-code entry point, the OpenAI Agent Builder offers a low barrier to entry.
| Company | Application | Result |
|---|---|---|
| PwC | Automating audit workflows | 15-40% efficiency gain |
| NielsenIQ | Data cleaning and quality | Over 90% data quality |
| Palo Alto Networks | HR service agent | 60% less ticket handling time |
| H&M | Customer service automation | 70% of requests resolved autonomously |
| Klarna | Customer service | Replaced work of 700 agents |
These examples show that agentic systems are no longer futuristic. They are being deployed in critical business processes today.
Which jobs will change?
According to the World Economic Forum, agentic systems could create around 78 million net new jobs by 2030. At the same time, many existing job profiles will change fundamentally.
Jobs with high automation potential
- Data entry and capture: Agents can fill forms, transfer data and validate entries
- Research and reporting: Gathering, structuring and preparing information
- Scheduling: Managing calendars, planning meetings, resolving conflicts
- Initial customer contact: Answering FAQs, categorizing requests, resolving standard cases
Jobs that will grow in importance
- AI orchestration: People who configure, monitor and optimize agents
- Quality assurance: Checking AI outputs for accuracy and relevance
- Strategic work: Creative and strategic tasks that require human judgment
- Customer relationships: Complex, emotional interactions that require empathy
The new job profile: the “Agent Operator”
A new role is emerging: the Agent Operator. This function combines technical understanding with process knowledge:
- Defines tasks and goals for AI agents
- Monitors execution and intervenes when problems arise
- Continuously optimizes prompts and workflows
- Ensures quality and compliance
What does this mean for Swiss SMEs?
For Swiss companies, specific areas of action emerge:
Short term (2025)
- Launch pilot projects: Identify a repetitive process and test an agent
- Train employees: Build prompt engineering and AI literacy
- Establish governance: Define guidelines for AI usage
Medium term (2026-2027)
- Scale successful pilots: Expand proven applications
- Redesign processes: Optimize workflows for human-agent collaboration
- Develop talent: Upskill employees into “Agent Operators”
Long term (2028+)
- Secure competitive advantage: Those who use agents effectively will be more efficient
- New business models: Agents enable services that were previously uneconomical
- Address labor shortages: Agents can fill gaps left by the skills shortage
The human role remains central
Despite all automation, the human role is indispensable. Agents need:
- Clear goals: Humans define what should be achieved
- Quality control: Humans review results and correct mistakes
- Ethical guardrails: Humans decide what may be automated
- Creativity: Humans develop new ideas and strategies
The most productive teams will be those that combine human strengths (creativity, empathy, judgment) with machine strengths (speed, endurance, consistency).
Swiss legal framework for autonomous agents
When agents make decisions that affect individual people, such as applicant pre-screening, tariff classification or automated complaint handling, Art. 21 DSG (Swiss Data Protection Act) applies: those affected have the right to a statement and to review by a natural person. For sensitive personal data (health, religion, biometric data), Art. 22 DSG additionally requires a Data Protection Impact Assessment (DPIA) before productive use.
The EDÖB (Swiss FDPIC) clearly signals in its opinions that human-in-the-loop is not just a design principle under Swiss law but a concrete obligation in workflows involving personal data. Before any productive agent deployment affecting people, the compliance review belongs in the sprint plan. More detail in the DSG guide for Swiss SMEs.
Conclusion
Agentic AI is still at the beginning, but it will rearrange the division of labor between humans and machines. The change is not a threat but an opportunity, provided companies actively shape it instead of waiting passively.
In our training programs we show you:
- How to understand the agentic mindset and put it to use
- Which processes are suitable for automation
- How to prepare your employees for the change
- How to integrate agents into your existing workflows
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