What are AI agents?

1. Introduction & Overview

1.1 What are AI Agents?

AI agents are advanced software systems that, with the help of modern artificial intelligence, are capable of acting independently and in a task-oriented manner. They go far beyond the familiar "chatbots" or simple process automation: They gather information from their environment, analyze it, derive steps for action, plan complex procedures, and execute them purposefully—often without direct human guidance.

An AI agent is not just a tool—it is an "actor": capable of performing tasks that traditionally require human decision-making, creativity, or complex problem-solving. This includes, for example, autonomously controlling business processes, individually responding to customer inquiries, coordinating entire supply chains, analyzing large data sets, or even developing creative solutions in marketing.

1.2 Origins and Evolution of Autonomous AI Systems

The development of AI agents is a logical progression from two fundamental trends in software development: the advancing automation of business processes on the one hand, and the enormous increase in the performance of machine learning—especially through so-called large language models (LLMs) such as GPT, BERT, or Claude—on the other.

Whereas simple automation in the past was limited to replicating human workflows step by step, AI agents today enable genuine agency: they can make decisions independently, pursue their own objectives, and adapt their strategies in real-time to changing conditions. This ability has only become possible thanks to a new composition of powerful models, large-scale data stores, access to external tools, and continuous learning from experience.

1.3 Significance for Business & Society

AI agents are more than just another tool in the arsenal of digital value creation. They are poised to fundamentally change entire industries—comparable to the introduction of the internet or the smartphone. Businesses are already reporting radical increases in productivity, cost reductions, and entirely new business models. At the same time, requirements for transparency, security, and ethical guidelines are growing.

The ability of AI agents to operate independently inspires hope for faster addressing of skilled labor shortages, individualized 24/7 customer support, and optimized, resource-saving production. However, these capabilities also come with new challenges, such as issues of control, responsibility, and the protection of sensitive data. Understanding and consciously handling AI agents thus becomes a key factor for competitiveness and societal acceptance.

2. Key Properties & Functional Principles

2.1 Autonomy, Goal-Orientation & Proactiveness

The central difference between AI agents and earlier generations of AI systems lies in their autonomy. While classical systems have always relied on specific user input and usually operated reactively, an AI agent acts independently and proactively. Autonomy here means the ability to make decisions, initiate actions, and pursue goals in dynamic, partly unknown environments.

AI agents are explicitly designed to achieve objectives assigned to them by humans, other systems, or dictated by environmental conditions. They are not limited to single steps or rigid rule-sets; rather, they can formulate sub-goals themselves and break down complex undertakings into effective actions. This goal orientation makes them valuable actors in virtually any context that requires planning, decision-making, and implementation.

2.2 Perception, Reasoning, and Action

The "agent cycle" consists of three essential basic functions:

• Perception: AI agents perceive their (digital or physical) environment purposefully. This may occur through analysis of text, speech, sensor data, images, videos, or software interfaces. The collected information forms the data basis for further decisions.
• Reasoning: Here, the agent processes the collected information, applies learning techniques and logic, compares alternatives, detects patterns, and draws conclusions. This often occurs over several iterations and is supported by short- and long-term memory structures.
• Acting: Based on the insights obtained, the agent implements concrete actions. This may include controlling a process, sending messages, updating databases, or—if physical—carrying out mechanical actions.

By repeating this perception-reasoning-action cycle, the agent positions itself as a true actor within the system.

2.3 Planning: Goal Definition, Task Decomposition & Workflow Control

Generally, an AI agent starts with a clear objective—either set by a human, a higher-level system, or through prior analysis. After setting the goal comes a phase of strategic planning. Here the agent breaks down larger objectives into smaller, more manageable sub-tasks ("Task Decomposition"). These sub-tasks are then prioritized and ordered according to necessity.

Not every task requires explicit planning; simple challenges can be solved by reflective, iterative action. For complex undertakings, however, the agent does multi-stage planning and often uses advanced methods such as Chain-of-Thought or ReAct, designing its workflows independently based on available tools and resources.

2.4 Reasoning: Logic, Iterative Improvement & Reflection Mechanisms

A central success factor for AI agents is their capacity for reasoning—applying logic, experience, and feedback to optimize actions and decision processes. While many classical systems always follow fixed rules, a modern AI agent can constantly question its own decisions, optimize them, and even learn from mistakes.

An agent continually checks whether its results align with the overarching objective, gathers feedback from users, other agents, or via self-evaluation, and adjusts its approach accordingly. This iterative self-improvement—known as "reflection"—is the foundation for ongoing accuracy, quality, and adaptability.

3. Components & Architecture

3.1 Large Language Models (LLMs) as the "Brain"

At the core of almost every modern AI agent are so-called Large Language Models (LLMs) such as GPT, Llama, or Claude. They act as the agent’s "brain," enabling both the understanding of natural language and processing of complex data structures. LLMs orchestrate decision-making, guide planning processes, select appropriate tools, and manage access to data sources and external interfaces.

Thanks to their ability to understand, generate text, and make logical connections, LLMs become the nerve center for all other components. They can rephrase tasks, explain their own steps, and flexibly adjust the frame for actions.

3.2 Memory Types: Short-term, Long-term and Episodic Memory

To act contextually and proactively, AI agents access various memory modules:

• Short-term memory: Stores current contexts, ongoing workflow history, and relevant short-term information.
• Long-term memory: Archives historical data, interactions, acquired knowledge, and lessons from past tasks.
• Episodic memory: Remembers key experiences from individual episodes or interactions (e.g., special customer cases or solutions).
• Consensus memory: In multi-agent systems: exchange of knowledge between agents for integration and synthesis.

These memory structures enable an agent to maintain context over long periods, reuse experiences, and adapt to users and changing environments.

3.3 Planning Modules: Task Decomposition, Chain-of-Thought, ReAct & ReWOO

Strategic task planning and management is facilitated by specialized modules:

• Task Decomposition: Breaks down complex problems into individual steps.
• Chain-of-Thought: Makes the agent’s trains of thought visible; every step and the underlying considerations are made transparent.
• ReAct: Stands for "Reasoning and Acting." Each thought and action step is followed by explicit evaluation, influencing the next step.
• ReWOO: "Reasoning Without Observation"—pre-planning the necessary tools and resources before taking action. This allows the agent to design efficient strategies in advance.

In multi-agent systems, extra modules for synchronization, coordination, and conflict resolution are often used.

3.4 Tools & Tool Integration (APIs, Databases, External Systems)

AI agents are not limited in their capabilities to internal data and functions. They can purposely integrate external tools, APIs, databases, and software services to collect information, control processes, or even collaborate with other agents.

• Interfaces to CRM, ERP, or accounting systems
• Access to internet sources, document databases, or sensor data
• Interaction with external systems, e.g., sending emails, using external analytics services
• Collaboration with other agents via standardized or custom-developed protocols

This greatly expands the agent’s scope beyond traditional AI solutions.

3.5 Security, Access Control, and Entitlements

A capable agent should only access clearly defined resources. Entitlements and authorization systems ensure that AI agents only use data and tools as intended. This oversight includes whether agents can automatically initiate orders, access personal data, or are limited to making suggestions.

Distinct agent IDs, access logs, audit trails, and (if necessary) human approval ("human-in-the-loop") are crucial components for a secure and controllable agent ecosystem—particularly relevant in regulated industries or when dealing with sensitive data.

4. Types & Classifications of AI Agents

4.1 Simple Reflex Agents

Simple reflex agents work on the principle "If condition A, then do B." They respond solely to current perception and require no recollection of past situations. A typical example is a thermostat that switches heating on or off based on the current temperature. Such agents are very lightweight but are limited to totally predictable and clearly bounded scenarios.

4.2 Model-based Reflex Agents

This enhanced form remembers past perceptions, builds an internal model of the environment, and can thus respond appropriately even in partially observable or unclear situations. For example, modern vacuum robots remember where they have already cleaned or adapt to changes such as moved furniture—a clear advance over rigid simple reflex agents.

4.3 Goal-based and Utility-based Agents

Goal-based agents always relate their actions to a desired objective—such as choosing the shortest route in a navigation system or purposefully optimizing production processes. They plan ahead and are much more flexible and effective than simple reflex agents.

Utility-based agents also evaluate how "good" one option is compared to alternatives. They use utility functions to calculate which path, solution, or behavior promises the highest benefit (e.g., efficiency, cost, customer satisfaction). Typical use cases are dynamic pricing, supply chain optimization, or intelligent resource planning.

4.4 Learning Agents

Learning agents are highly adaptive. They can learn from the past and from feedback, and independently adjust future behavior to new circumstances. The learning processes include:

• Experience-based learning from completed tasks
• Critical evaluation via internal or external review ("Critic")
• Problem-solving by simulating and exploring new approaches
• Ongoing adaptation and performance improvement via self-optimization

A typical example is recommender systems in e-commerce, which make more accurate predictions with every user interaction and provide individualized product recommendations.

4.5 Proactive, Hybrid, and Hierarchical Agents

Proactive agents anticipate future developments, act in advance, and can independently initiate actions—for example, to prevent operational disruptions or by proactively sending customer follow-up requests.
Hybrid agents combine various principles (e.g., reactive and proactive elements). They respond to events but can also pursue long-term strategies to achieve optimal results.
Hierarchical agent systems consist of several specialized sub-agents. Overarching controllers split tasks, delegate steps, and collect progress—like a modern company organizational chart.

4.6 Multi-Agent Systems and Collaboration

In multi-agent systems, multiple AI agents interact—cooperating or competing—to automate complex tasks or entire value chains. This creates emergent behavior: depending on role allocation, knowledge exchange, and objectives, enormous flexibility arises, which is essential for applications like supply chain optimization, cooperative robotics, or dynamic pricing.

5. Distinction: AI Agents, AI Assistants & Chatbots

5.1 Differences in Complexity & Autonomy

A key distinguishing feature between traditional chatbots, modern AI assistants, and AI agents is the degree of autonomy and complexity:

• Chatbots: Usually follow simple rules and patterns, answer frequently asked questions, recognize keywords, and are limited to linear, pre-defined flows—without their own context or memory.
• AI Assistants: Already act more intelligently—supporting users via natural language, taking instructions, making suggestions, and also covering multiple steps in an interaction. However, they often still require active control by the user and do not operate completely independently.
• AI Agents: Independently handle complex, multi-step goals, plan, analyze, and carry out tasks without constant instructions. They continually learn, take responsibility for their output, and can be considered proactive, intelligent actors.

5.2 Reactive vs. Proactive Systems

Many traditional systems act purely reactively—they wait for user input and respond contextually. Modern AI agents, in contrast, can act proactively: they recognize when action is needed, initiate actions themselves, and dynamically adapt their behavior.

5.3 Comparison to Classical Assistants and Bots

It is crucial to distinguish by:

• Degree of Independence: Only AI agents can execute complex, multi-step processes without ongoing input.
• Learning Ability: AI agents have explicit memory and self-optimizing mechanisms. Chatbots generally cannot learn from previous interactions.
• Depth of Access: AI agents integrate and orchestrate external tools and databases, while chatbots usually remain within limited dialog structures.

5.4 Interaction with AI Copilots

While AI agents operate as independent actors, so-called "copilots" are often designed as collaborative companions, supporting users in operating software and processes. They orchestrate other agents, help manage complex workflows, and create more intuitive interfaces between humans and machines. Communication interfaces between copilots and AI agents enable a dynamic, situationally adapted interplay.

6. Practical Implementation & Development of AI Agents

6.1 Frameworks & Development Platforms

The development of high-performance AI agents is based on a variety of frameworks and platforms. The most popular tools include:

• LangChain: Framework for orchestrating LLMs, planning, tool integration, and memory handling.
• LlamaIndex, LlamaStack: Specially designed for data-intensive applications and retrieval-augmented generation.
• Copilot Studio (Microsoft): Low- and no-code platform for developing and integrating agents into business applications.
• Amazon SageMaker & Bedrock: Comprehensive ML/AI platforms for the development, training, and large-scale operation of AI agents.
• Python with specialized libraries: For custom and experimental agent solutions.

Many frameworks offer integration of telemetry, logging, evaluation/auditing, and multi-agent coordination out of the box.

6.2 Integration into Existing IT Landscapes

A decisive success factor is seamless integration of AI agents into a company’s business processes and data structures:

• Creating interfaces to ERP, CRM, DMS, and other core systems
• Establishing secure authentication, role, and rights management
• Integrating monitoring and logging functions
• Setting up transparency and approval polices via human-in-the-loop mechanisms

6.3 Training, Adaptation and Continuous Optimization

The best technical foundation only realizes its full potential with ongoing training and adaptation to the company’s reality. This includes:

• Training on company-specific documents and data
• Continuous user feedback integration for fine-tuning responses and processes
• Dynamic extension of capabilities by integrating new tools, workflows, and use cases
• Regular quality control and review of results based on KPIs

6.4 Access to Internal/External Data and Safeguards

Effective AI agents require access to high-quality, up-to-date, and reliable data. Companies must develop clear strategies for this purpose:

• Integrating databases, document libraries, APIs, and streaming interfaces
• Anonymizing and controlling personal data for privacy and compliance
• Implementing access and protection policies according to company and industry standards

7. Typical Use Cases & Fields of Application

7.1 Customer Service & Support

AI agents are revolutionizing customer service by independently handling complex issues, providing personalized responses, operating across multiple channels, and proactively initiating support processes. From ticket processing and callback/request management to the automation of complaints and service agreements, agents are transforming classic call centers and help desks.

7.2 Sales, Marketing & Commerce

In sales, AI agents enable automated qualification of leads, individualized product recommendations, or the proactive sending of personalized offers. In marketing, campaign creation, performance tracking, and customer journey optimization are automated and continuously improved. In e-commerce, agents conduct customer conversations, provide consultations, and accompany users from product search to successful purchase.

7.3 Production, Supply Chain & Manufacturing

Especially in dynamic, global supply chains, AI agents demonstrate their strengths in real-time monitoring, inventory optimization, demand forecasting, and control of critical logistics processes. In manufacturing, they enable predictive maintenance, analysis of machine data, and automatic coordination of production orders.

7.4 Finance, Controlling & Accounting

Autonomous agents in finance handle tasks such as invoice verification, fraud detection, liquidity management, and automated data preparation for audits. They analyze financial data, simulate scenarios, and suggest optimizations based on internal and external data sources.

7.5 IT, Development & Security

In IT, agents show their capabilities in identifying and resolving support issues, proactive incident and patch management, and in automating development processes (CI/CD). In cybersecurity, they monitor systems, detect anomalies early, and can promptly initiate countermeasures.

7.6 Human Resources and Knowledge Management

From automated applicant management and onboarding processes to continuous skill development, AI agents support HR teams and knowledge managers alike in identifying skill gaps, developing individualized learning paths, or matching employees with suitable training and projects.

7.7 Healthcare & Social Interaction

In the healthcare sector, AI agents assist with appointment scheduling, patient consultation, recruiting study participants, or proactively identifying critical health signals. In particularly sensitive domains, however, the limits of machine empathy and ethical responsibility must always be considered.

7.8 Data Analysis and Decision Support

Data agents assist companies in extracting insights from large datasets, conducting analyses, generating automated reports, and providing decision support to management and experts. They help track down, organize, and make use of relevant information across departments or sources—for example, in evaluating market data, research findings, or internal archives.

In practice, specialized solutions like Researchico can efficiently support this process. Agent-based software is able to manage a wide variety of file formats centrally, analyze content, and tag it, as well as reliably answer individual queries in natural language. By combining modern AI analysis with secure storage and deletion concepts, knowledge management is noticeably accelerated and simplified. This enables well-founded decisions based on always-available, verified information, and sustainably reduces the effort required for research and documentation.

7.9 Creativity and Design Support

In the creative field, AI agents generate content, support brainstorming, design marketing materials, or assist designers in developing cross-industry campaigns. Complex multimodal projects (text, image, speech, video) are increasingly accompanied by AI agents as well.

8. Benefits and Potential Value

8.1 Efficiency Gains and Cost Reduction

AI agents take automation to a new level: they relieve specialists from routine tasks, shorten lead times, optimize resource-intensive processes, and reduce costs previously caused by human error, delays, or inefficient procedures.

8.2 Accuracy, Quality Improvement & Error Avoidance

Through continuous self-reflection and integration of feedback, AI agents achieve above-average accuracy and avoid repetitive errors. They document each step, can cross-check suggestions, and use feedback cycles to improve results.

8.3 Scalability, 24/7 Availability, and Flexibility

Unlike human teams, AI agents are available regardless of time or location, scale with increasing requests without lowering service levels, and flexibly adapt to changing requirements or seasonal fluctuations.

8.4 Personalization and Improved User Experience

By analyzing preferences, history, and context, both business and consumer clients can expect individualized, consistent services and interactions, which increases satisfaction, loyalty, and conversion rates.

8.5 Promoting Innovation, Collaboration & Expert Focus

As routine and standard tasks are outsourced, teams can focus on creative, strategic, and people-centric activities, becoming innovation drivers for their companies. At the same time, multi-agent frameworks enable unprecedented collaboration across teams and departments.

9. Challenges & Risks

9.1 Data Protection, Security & Compliance

As autonomy increases, so does the need for data protection and secure data processing. AI agents often operate with sensitive personal, financial, or production data—a misuse or data breach can have severe consequences. Clear privacy policies, encryption, access restrictions, and regular audits are essential.

9.2 Technical Complexity and Resource Requirements

The development, training, and scaling of industry-grade agents may require significant computing resources and an interdisciplinary development team. Especially smaller firms must carefully weigh the effort, cost, and benefits.

9.3 Sources of Error, Feedback Loops & System Failures

Poorly orchestrated multi-agent systems can end up in infinite loops, conflicting tasks, or data divergences. System errors, faulty training data, or authorization problems pose a risk of substantial process disruptions.

9.4 Ethical Risks and Accountability

Delegating complex decisions to autonomous systems is particularly challenging where human judgment, empathy, or ethical considerations are required (e.g., healthcare, law, etc.). Companies are called upon to implement control mechanisms and clearly document responsibilities.

9.5 Limits on Empathy, Social Interaction & Real Adaptability

Genuine, empathetic communication, recognizing non-verbal cues, or dealing with highly dynamic, unpredictable environments currently remain beyond the reach of machine agents. Companies should therefore always focus on complementary human-machine teams.

10. Best Practices for Development and Operation

10.1 Clear Objectives and Application Definition

The beginning of every project should feature a precise definition of the AI agent’s objectives. What problem should be solved? What benefit is expected? What does successful deployment look like in practice?

10.2 High-Quality Data Basis and Data Management

The better and more carefully curated the data base, the stronger the performance of the AI agent. Uniform standards, deduplication, continuous maintenance, and integration of external sources ensure a decisive competitive advantage.

10.3 Human-in-the-Loop / Human Oversight

Critical steps, highly sensitive decisions, or radical process changes should always be supervised or finally approved by experienced experts (human-in-the-loop principle)—this strengthens transparency, traceability, and security.

10.4 Monitoring & Performance Measurement

Regular measurement of performance, accuracy, and user satisfaction helps to identify sources of error early, set priorities, and make corrections in case of deviation from objectives. Automated quality assurance and logging are mandatory.

10.5 Transparency, Logging & Traceability

Logs of all actions, decision points, and tools used are indispensable not only for legal and ethical documentation, but also for troubleshooting and optimization.

10.6 Continuous Training & Change Management

Companies should invest specifically in staff training and in actively dealing with AI agents. Only then does a culture of constructive collaboration, open feedback loops, and sustainable adaptation of new technologies emerge.

10.7 Security, Role Management & Access Control

Clear roles, finely graduated access rights, agent identification, and well-considered security mechanisms help prevent misuse and unwanted side effects and keep control over critical resources.

11. Outlook & Future Trends

11.1 Further Automation and Agent Ecosystems

AI agents will become a permanent fixture in company-wide software environments over the next few years. Building and maintaining “agent ecosystems,” where specialized agents are orchestrated as needed, will become a key value-creation competency.

11.2 Advances in LLMs, Representation & Multimodality

With the continuous development of LLMs and multimodal models, AI agents will become even more powerful: soon, they will not only understand and generate text but also speech, images, video, and code at the highest level—exponentially increasing the spectrum of possible tasks.

11.3 New Markets, Applications, and Business Models

In particular, fields previously shaped by high manpower requirements, complex regulations, or high heterogeneity will see new business opportunities, services, and ideas emerge—from dynamic platform economies to individualized consulting solutions for consumers and companies.

11.4 Paradigm Shifts in Organization and Work

The advance of autonomous agents marks a fundamental transformation in organizations: established structures are broken up, classical hierarchies are relativized, decision-making processes are accelerated, and human work focuses on creative, strategic, or social tasks. Companies stand at the threshold of a new era of collaborative intelligence.

11.5 Prospects for Human-Machine Collaboration

In future, it will not be machines replacing humans, but rather humans and machines jointly creating value in a coordinated way. AI agents will be team members, process orchestrators, and knowledge brokers in hybrid work teams. The development of responsible, transparent, and adaptive collaboration is the central key to sustainable progress in a digital world.