Introduction

In a world where artificial intelligence is increasingly integrated into our daily lives, agents based on large language models (LLM) play a crucial role in automating and optimizing various processes. But what are agents, and how can we build them effectively? This article aims to outline the basics of creating agents, explain the difference between workflows and agents, and provide practical advice for developers and project managers.

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Key Concepts

Agents based on LLM are systems capable of performing tasks autonomously, making decisions about tool and method usage in real-time. They can range from fully autonomous to those operating within a set script.

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Workflows are more structured systems where LLMs and tools are coordinated via predefined algorithms. They ensure predictability and stability in task execution.

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Practical Advice

Simplicity and Composition: The best solutions often do not rely on complex frameworks but on simple, well-thought-out patterns. Start with simple solutions and add complexity only when necessary.

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Choosing between Workflows and Agents:

• Workflows are suitable for tasks with clearly defined steps where predictability and speed are critical.
• Agents are better for tasks that require flexibility, adaptation to changing conditions, or when scaling and model-driven decision-making are needed.

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Optimizing LLM Calls:

• Use retrieval to provide the model with relevant data, which can reduce latency and enhance response accuracy.
• Add contextual examples to improve the model’s understanding of the task's specifics.

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Examples and Case Studies

• Healthcare: Using agents for diagnostic automation, where agents can analyze patient symptoms in real-time, suggesting preliminary diagnoses or even treatment recommendations.
• Financial Services: Agents assist in monitoring transactions for fraud detection, adapting to new fraud schemes.
• Logistics: Automation of supply chain management, where agents dynamically adjust routes and resource allocation based on changing conditions or unforeseen events.

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Technical Details for Developers

For those involved in implementation:

• Integration with existing systems: It's important to consider compatibility with existing databases, APIs, and interfaces.
• Memory Management: Given the resource intensity of LLMs, optimizing memory usage can significantly reduce latency.
• Reducing Latency: Implementing caching or asynchronous execution can improve performance.

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Conclusions and Recommendations

Building agents based on LLMs is a balance between simplicity and complexity, predictability and flexibility. Start with simple solutions but be prepared for scaling and adaptation. For further exploration, we recommend looking into:

• Articles and research from Anthropic and other AI leaders.
• Online courses on machine learning and AI.
• Developer communities on platforms like GitHub or Stack Overflow.

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Additional Resources

• Anthropic - for in-depth study of working with LLMs.
• Hugging Face - for access to a variety of AI models.
• Scholarly articles on arXiv about the latest AI advancements.

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