AI agents mark a new stage in the application of artificial intelligence. They go far beyond the reactive capabilities of previous AI assistants like Siri, Alexa, or Google Assistant, offering proactive, autonomous completion of digital tasks. 

Read more about the capabilities in following article (click on more) or in the long version with a brief comparison of ecosystems by downloading the full article (pdf): Rise of AI Agents

From AI Assistants to AI Agents

AI Assistants are reactive systems that perform isolated tasks:

• Examples: Voice assistants like Alexa, customer service chatbots, text suggestions when typing.
• Features: They wait for commands, execute single tasks, and have no overarching goal.

AI Agents, on the other hand, act proactively, plan complex workflows, and pursue overarching goals:

• Examples: Financial agents optimizing portfolios, generating regular reports or analyses, programming of apps/interfaces etc., IT helpdesk, services and assistance as an product expert, Automating Social Media Management from idea generation to excecution incl. continious posts
• Features: They analyze data, make decisions, and implement them independently.

Advantages:

• Efficiency: Automation of complex tasks
• Optimization: Continuous data analysis and learning sequences
• Scalability: Adaptable for various use cases

Challenges:

• Data Quality: Inaccurate data can lead to flawed decisions
• Security: Protecting sensitive data and systems is crucial
• Ethics: Decisions must remain transparent and understandable

All acivities of AI agents shold have the option to integrate human oversight and approval at various stages, if desired. Explainable AI (XAI) provides transparency, explains AI decisions, builds trust, and identifies biases or errors. This link refers to a detailed article from IBM: IBM Think – AI Agents

Methods and scalability

Choosing the right model or combination depends on the application. Some examples of model types:

• Machine Learning (ML): Enables systems to learn from data without explicit programming.
• Deep Learning (DL): Uses neural networks to identify complex patterns.
• Natural Language Processing (NLP): Processes human language for understanding and generation.
• Transformer Models: Excellent for NLP tasks using attention mechanisms.
• Gradient Boosting: A machine learning technique for regression and classification, combining weak models into a strong one

Scalability and Context Windows: Expanded context windows allow agents to consider vast information for better decisions, such as historical pricing, weather trends, or social media analytics in travel planning.

• Super SLM: Specialized small models for simple tasks, e.g., keyword recognition.
• SLM: For limited-context tasks like sentiment analysis or simple chatbots.
• LLM: Large models for complex tasks like text generation or advanced chatbots.
• Super LLM: Extremely large models capable of multimodal tasks (e.g., text and image analysis) and advanced reasoning.

AI agents has high complexity in decision making. Therefore LLM repectively SLLM like Open AI’s o3 is the best decision. The Generative Pre-trained Transformer model (“GPT”) has been specifically designed to perform significantly better than previous models in tasks that require step-by-step logical thinking. It can analyze complex problems, generate various solution approaches, and systematically evaluate them to arrive at the best solution.

Increased “Thinking Time”: Unlike traditional AI models, o3 takes more time and computational power to process tasks. This process is similar to human thinking, where one takes time to ponder over a problem before providing an answer.

Outstanding Performance in Benchmarks: o3 has achieved impressive results in various challenging AI benchmarks. Notably, its performance in the fields of software development (SWE-bench Verified) and answering expert questions in natural sciences (GPQA Diamond) stands out.

Focus on Safety and Ethics: OpenAI places great importance on the safety and ethical implications of o3. Extensive testing is conducted, and researchers are invited to scrutinize the system for potential risks before it is made available to the public or deployed in robotics platforms, for which it is fundamentally well-suited.

Conclusion

AI agents have the potential to fundamentally transform industries. However, their success depends on clear goal definitions, high-quality data bases, an AI ecosystem tailored to the environment, and a secure and application-appropriate model and deployment. Transparency through Explainable AI (XAI) and the use of large context windows are crucial to harnessing the full potential of this technology and minimizing risks. 

This is just a selection in January 2025, researched with the support of AI search, verified and partially tested. It is important to understand that the respective models are rapidly evolving and can surpass each other with each update. There are other leading providers such as Anthropic Claude (used in AWS), Gemini (used in Google), Llama (used in Meta), o3 (used in OpenAI), Tongyi Qianwen (used in Alibaba), … to name a few. However, AI performance is only one factor that can contribute to the selection decision depending on individual weighting. A current comparison of compleate ecosystems like AWS should always be carried out. A look at the AI Report from Stanford University shows, that certain information should be viewed as a snapshot. General market developments are very well reflected: [Link to Website AI Index Report]

Download here the long version with a brief comparison of ecosystems (can be seen as an result of AI search and optimized promting – it’s worthwhile!): Rise of AI Agents

The information is provided without warranty and does not claim to be complete – it only reflects my personal view and does not constitute advice. Feel free to get in touch for an exchange – or as MS Copilot sometimes responds: “If you need further assistance or have any questions, feel free to ask!”