According to McKinsey's State of AI Report, a whopping 88 percent of companies worldwide now use artificial intelligence to optimize at least one business process in the long term – in other words, to make it more efficient, secure, and ultimately more productive. In some places, however, companies are already going several steps further and deploying AI agents as team members. They orchestrate entire workflows independently and can be organized in a similar way to human departments. Instead of employing three or five people in one area, all of whom operate their respective AI tools, only one so-called human-in-the-loop is needed to coordinate the AI agents. But how exactly does this work? You can find out in this article, which includes practical examples.
What are AI Agents (Agentic AI)?
At their core, AI agents are autonomous software systems that independently execute tasks, but also pursue concrete goals and can even make decisions. They do not just react to single inputs (prompts), but plan multi-stage actions based on context or relevant data and defined goals. This is precisely what distinguishes them from classic automations or simple chatbots.
A modern AI agent does not act purely reactively. It evaluates situations, adapts its behavior to new information, and interacts with other agents or people to achieve the ideal result. You can indeed think of it as a digital colleague who knows what needs to be done, when to act, and whom to involve if uncertainties arise.
The foundation for Agentic AI is Large Language Models (LLMs), as they enable the simultaneous and human-like processing of language, logic, and context. This allows them to understand instructions, interpret information from various sources, and derive meaningful actions from it.
- All this only works with the right data foundation: AI agents require structured company data, knowledge bases, and/or input from past interactions to establish the necessary context. After each action, they store new information and process feedback. This gradually improves their performance. When multiple AI agents work towards one goal, their feedback also flows into this learning process.
- Furthermore, humans are still needed: The so-called Human-in-the-Loop reviews the results, intervenes in case of problems, and makes the final decisions. Especially in complex tasks or workflows involving multiple AI agents, and in sensitive areas such as customer communication, financial assessments, or even medical diagnoses, the human supervisor remains indispensable. This interaction reduces errors, prevents biases, and generally increases the reliability of the system.
AI Agent Examples – Where Can Agentic AI Be Used in a Company?
The prerequisites for deploying AI agents are in place in many European companies. In recent years, data strategies have been built up, data silos reduced, systems modernized, interfaces created, and measurable successes through artificial intelligence have already been achieved. In Germany alone, according to figures from the Federal Network Agency, 30 percent of companies already use AI today, and 19 percent are planning to start.
The energy sector is pioneering in this regard. AI agents analyze network utilization at German utility companies and control optimal distribution in real-time. They prevent outages and, not least, help to comply with regulatory requirements. In the telecommunications environment, Agentic AI evaluates service processes, detects bottlenecks, and automatically initiates measures to improve customer support. It prioritizes inquiries, assesses technical faults, and supports service teams with concrete recommendations for action.
In industry, AI agents primarily support quality assurance. They detect deviations in production data, compare them with historical values, and report anomalies early. This provides human specialists with targeted information instead of having to check everything manually. Especially in complex manufacturing processes, this creates a clear gain in efficiency, as the AI monitors all steps and can interpret them reliably.
In addition to these large application areas, AI agents as team members also play an increasingly important role in medium-sized businesses. Here, concrete added value is created, often with manageable effort.
- A common entry point is corporate communication: A so-called Inbox Agent analyzes incoming emails, categorizes them thematically, and creates several response suggestions based on existing knowledge. Employees decide which draft to use and retain control. The difference to classic customer service automation is that the agent considers context, sender history, and objectives, instead of merely processing fixed rules or keywords.
- Typical processes around meetings are also relatively easy to map: Agentic AI evaluates meeting transcripts, summarizes results, and automatically creates structured follow-ups that can even reflect certain moods. Relevant decisions, tasks, and responsibilities land directly in the right place. Teams save time and also reduce the risk of misunderstandings. In contrast to simple scripts, the agent recognizes connections, priorities, and implicit statements, instead of just linearly documenting content.
- In the commercial sector, AI agents take over tasks such as invoice verification: An Invoice Agent can quickly compare incoming receipts with stored project data, correctly assign costs, reliably detect any deviations, and communicate them directly if necessary. Here, the added value compared to conventional accounting automation is that the agent evaluates exceptions and reacts situationally, instead of simply aborting processes in case of deviations.
- In marketing, there are enormous opportunities: Agentic AI, for example, analyzes existing content in campaign planning, evaluates performance data, and creates concrete suggestions for topics, publication times, and formats. This forms the basis for purposeful (because data-based) drafts for newsletters, landing pages, or social media posts. In comparison to "mere" marketing automation, the agent understands content semantically and can thus almost human-like develop it further. Furthermore, learning processes and conflicting goals are incorporated, leading to adaptive rather than static recommendations.
These examples clearly show that AI agents can unfold their benefits not only in large corporations, but truly everywhere where recurring routine processes, suitable data, and clear goals meet. They do not replace specialists but significantly expand their scope of action.
How Can AI Agent Automation Actually Be Implemented?
AI agent orchestration sounds complex at first – and indeed, the matter is by no means self-running. However, if planning, know-how, data, technology, and mindset sensibly interlock, a system emerges that grows step by step and remains controllable at all times. It is essentially a clearly structured change process that often follows the scheme below.
1. Establish Openness and a Suitable Mindset
The implementation of Agentic AI is not a purely IT decision. Employees must understand the role the agents assume and where human responsibility begins. The crucial acceptance is only created through transparency. Training, clear communication, and managing realistic expectations are therefore indispensable.
Many companies fail not because of the technology, but because of a lack of team involvement. Those who recognize early on that AI agents are intended to relieve, not replace, significantly reduce resistance. It is also advisable to deliberately keep initial applications small. A single agent supporting a sub-process is often enough to build trust.
Last but not least, a step-by-step introduction lowers risks and enables learning during ongoing operation. External support from an AI consultant or AI agency can be useful, provided that knowledge is built up internally in the long term.
2. Identify Needs
Before technical solutions are discussed, it should be clear where AI agents bring real benefit. Not every process is suitable for Agentic AI. Classics are tasks with a high degree of repetition, clear rules, and measurable effort, which would also be considered for conventional automation.
Discussions with specialist departments provide valuable insights here, as it is ultimately the employees who best know bottlenecks, media breaks, sources of error, or other deficits that can be compensated for by AI in their daily work. This is often where the greatest efficiency losses occur. A sensible starting point is a use case that can be implemented within a few weeks and ideally provides a large, immediately noticeable relief.
Key questions include:
- Which tasks regularly cost a lot of time?
- Where do manual errors or other deficits frequently occur?
- Which activities can be technically structured?
The focus should always be on measurable benefits. Technology without a clear effect ties up resources and creates frustration.
3. Check Technical Requirements
AI agents require a certain basis to work truly effectively. A fragmented data landscape makes deployment considerably more difficult. Relevant information should therefore be structured, up-to-date, and accessible. Clear interfaces between the systems to be integrated are equally important.
A so-called Data Readiness Check helps to realistically assess the status. It examines whether data quality, access rights, and all security requirements are met and where adjustments should be made. Data protection and compliance must be considered from the outset.
Typically important checkpoints are:
- Can the AI access a central data basis?
- Are interfaces clearly defined – where might adjustments be necessary?
- Which systems should/can be connected at all?
- Who holds internal responsibility?
4. Start with a Pilot Project
A pilot project forms the controlled entry point, whereby the chosen process should be manageable and easy to expand upon success. The goal is primarily to learn – not perfection.
Transparent communication continues to play a major role. Teams should know what is being tested and why. Errors are part of the process – the key is to make them visible and thereby grow.
A typical process is as follows:
- Selection of a clear use case
- Definition of measurable goals
- Setup of a test environment
- Training of participants
- Implementation over a limited period
Metrics such as time savings, error reduction, or relief provide an objective basis for decision-making. Thus, comprehensive monitoring is appropriate even at this stage.
5. Evaluation and Rollout
After the pilot phase, a thorough evaluation follows, which is not only about numbers but also about acceptance and everyday suitability. Where were there friction points? What adjustments were necessary?
Based on this, the decision is made whether and how the AI agent will be further rolled out. A step-by-step approach has proven effective. Only when the added value is clear should further processes follow.
6. Continuous Monitoring and Optimization
The work does not end with the rollout. Agentic AI must be continuously reviewed and, if necessary, adapted to new market conditions or customer expectations, technological developments, etc. Data changes, processes evolve – without maintenance, the quality of the results inevitably decreases against this background.
Monitoring also continues to create transparency. Ideally, everything should be viewable in central dashboards: performance, reliability, and costs should be kept in view. Through fixed feedback loops, valuable practical information flows into the analyses. Regular reviews/reports, for example quarterly, create important reference points for continuous improvement. This keeps the system agile and increasingly establishes it within the team structure.
Conclusion
Agentic AI offers enormous potential – and this can/should definitely be leveraged even on a small scale. What is decisive is not the size of the entry, but making it happen at all. It is important to recognize suitable use cases, further develop technical foundations, build up know-how, and foster a culture that allows AI agents to be team members in the first place.
Many German companies bring good prerequisites for this – maybe yours too. Strong professional competence, pronounced process knowledge, and increasing data orientation form a resilient basis. What is often still missing is less the technology and more the courage to consistently take the first steps.
In the long term, organizations that do not view humans and AI agents as opposites will primarily benefit. The greatest added value arises where human experience, judgment, and responsibility interact with the speed and scalability of AI.
If you need support with planning, classification, or implementation, we at bakedwith will be happy to accompany you on this path.
FAQ
What types of AI agents are there?
AI agents differ in function and complexity. There are simple, rather reactive systems that only respond to specific signals, goal-based solutions with clear specifications, and finally, learning Agentic AI that can continuously adapt its behavior.
Where are AI agents used in practice?
AI agents are used, among others, in the energy sector, industry, healthcare, customer service, and marketing. They support analysis, coordination, documentation, and decision-making. Measurable added value arises particularly where large amounts of data and clear processes converge.
For which companies is Agentic AI useful?
AI agents primarily play to their strengths when multi-layered processes need to be coordinated, information from various sources flows together for this purpose, and decisions are to be automatedly prepared. This requires a certain number of comparable processes, digital data, and clearly defined procedures. Where rigid rules are no longer sufficient and automated coordination becomes necessary, Agentic AI is the next logical step.
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Jens ist Mitgründer von bakedwith, einer Boutique-Beratung für smarte Automatisierung und KI. Er unterstützt mittelständische Unternehmen und Konzerne dabei, Prozesse zu optimieren, manuelle Arbeit zu reduzieren und durch effiziente Workflows Wachstum zu erzielen. Zuvor war Jens Growth Lead bei OMR und begleitete als Freelancer zahlreiche Unternehmen bei der Optimierung ihrer CRM- und Automationssysteme. Seine Leidenschaft gilt der Verbindung von Wachstum und Effizienz – damit Teams sich auf das Wesentliche konzentrieren können.
