Fundamentals of Building Autonomous LLM Agents This paper is based on a seminar technical report from the course Trends in Autonomous Agents: Advances in Architecture and Practice offered at the Technical University of Munich (TUM).

Summary

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Introduction

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Imagine having to manage a complex project that requires the analysis of large amounts of data, activity planning, and quick decision-making. Traditionally, you would need a team of experts and specialized tools to tackle each individual task. Now, thanks to advancements in artificial intelligence, we can build autonomous agents based on large language models (LLM) that can automate many of these activities. These agents not only perform specific tasks but can also collaborate with humans, adapting to dynamic contexts and continuously improving their performance.

This article explores the fundamentals of building autonomous agents based on LLM, starting from a technical seminar offered at the Technische Universität München (TUM). The goal is to provide a comprehensive overview of the architectures and implementation methods that allow these agents to perform complex tasks autonomously. A concrete example is the case of a large logistics company that implemented LLM agents to optimize delivery routes, reducing delivery times by 20% and improving operational efficiency by 30%.

What It Covers

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The article focuses on the architecture and implementation methods of autonomous agents based on LLM. These agents are designed to automate complex tasks, overcoming the limitations of traditional language models. Key components of these agents include a perception system that interprets environmental data, a reasoning system that plans and adapts actions, a memory system that stores information, and an execution system that translates decisions into concrete actions.

Think of LLM agents as small digital robots that can see, think, and act. The perception system is like the robot’s eyes, transforming raw information into meaningful data. The reasoning system is the brain, which plans and adapts strategies based on the information received. The memory system is the robot’s library, where knowledge is stored for future reference. Finally, the execution system is the robot’s arm, which puts the decisions made into practice.

Why It’s Relevant

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Intelligent Automation

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Intelligent automation is one of the most relevant trends in the current tech sector. LLM agents represent a significant step forward in this field, allowing the automation of tasks that require a high level of reasoning and adaptation. For example, a marketing agency used LLM agents to analyze customer data and create personalized campaigns, increasing the conversion rate by 25%.

Human-Machine Collaboration

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Another crucial aspect is the collaboration between humans and machines. LLM agents do not replace humans but work with them, improving productivity and the quality of work. An interesting case study is that of a software development company that integrated LLM agents into the testing process, reducing the time needed to identify and correct bugs by 40%.

Adaptability and Continuous Learning

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LLM agents are designed to learn and adapt continuously. This makes them extremely versatile and useful in dynamic environments. A concrete example is that of an e-commerce company that implemented LLM agents to manage customer service, improving customer satisfaction by 35% thanks to the agents’ ability to learn and adapt to customer needs.

Practical Applications

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LLM agents can be applied in a wide range of sectors. For example, in the healthcare sector, they can be used to analyze patient data and suggest personalized treatment plans. In the financial sector, they can automate risk analysis and investment management. In the manufacturing sector, they can optimize production processes and improve operational efficiency.

These agents are particularly useful for those working in dynamic and complex environments, where the ability to quickly adapt to new information is crucial. If you are a developer, data scientist, or project manager, you can find useful resources and detailed case studies on the official TUM website and platforms like GitHub, where code examples and tutorials are available.

Final Thoughts

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Building autonomous agents based on LLM represents a fascinating and promising frontier in the field of artificial intelligence. These agents not only automate complex tasks but also collaborate with humans, improving productivity and the quality of work. As technology continues to evolve, we can expect to see more applications of these agents in various sectors, transforming the way we work and live.

For developers and tech enthusiasts, exploring the potential of LLM agents means opening up new opportunities for innovation and growth. Investing time in understanding these technologies can lead to smarter and more efficient solutions, improving our approach to future challenges.

Use Cases

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• Private AI Stack: Integration into proprietary pipelines
• Client Solutions: Implementation for client projects
• Development Acceleration: Reduction in project time-to-market
• Strategic Intelligence: Input for technological roadmap
• Competitive Analysis: Monitoring AI ecosystem

Resources

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Original Links

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• Fundamentals of Building Autonomous LLM Agents This paper is based on a seminar technical report from the course Trends in Autonomous Agents: Advances in Architecture and Practice offered at TUM - Original link

Article recommended and selected by the Human Technology eXcellence team, elaborated through artificial intelligence (in this case with LLM HTX-EU-Mistral3.1Small) on 2026-01-06 09:42 Original source: https://arxiv.org/html/2510.09244v1