Sam Altman & OpenAI Building AI Agents Wrong – How to Build AI Agents Correctly

Introduction

Sam Altman, CEO of OpenAI, recently made statements about AI agents, particularly around the use of the 01 model to improve their capabilities. While Altman suggests this will accelerate progress, there are fundamental issues with this approach. In this blog, we’ll break down why relying on Large Language Models (LLMs) for AI agents is problematic and how a more effective approach can be achieved through a software-centric design.

Why OpenAI’s LLM-Centric Approach to AI Agents is Limiting

Altman’s idea that AI agents can be dramatically improved with advancements like the 01 model is misguided. His belief that using LLMs as the core of AI agents will lead to faster and better results overlooks several key limitations:

• LLM-Centric AI Agents Are Inefficient: Many in the AI space treat agents as LLMs that loop through tasks or use tools, but this narrow focus on LLMs creates inefficiencies. It pushes too much of the problem-solving burden onto the LLM itself, which can be overkill for many tasks and results in unnecessarily complex solutions.
• The Real Issue with LLM Dependency: By making AI agents LLM-centric, companies face scalability challenges and need to use the most advanced LLMs for tasks they could solve in simpler ways. The outcome? Higher costs, slower performance, and reduced flexibility in the agents' capabilities.

How to Build AI Agents Correctly: The Software-Centric Approach

To avoid the pitfalls of LLM overuse, a better strategy is to build AI agents around traditional software engineering principles. Here’s how it works:

• Break Problems Into Smaller Parts: Rather than relying on LLMs for everything, split the tasks into smaller, manageable pieces. Use classic coding for most of the problem, pulling in LLMs only where they add specific value. This leads to faster, more reliable solutions.
• LLMs Should Be Part of the Solution, Not the Whole Solution: In a software-centric model, LLMs can contribute, but they don’t need to dominate. By keeping LLMs to around 25-50% of the problem-solving process, the rest can be handled by traditional methods that are often faster, cheaper, and more reliable.
• Higher Reliability, Lower Complexity: One of the key benefits of this approach is the increased reliability. LLMs can be unpredictable, but by limiting their role, you create more stable and manageable AI agents.

Conclusion

In conclusion, Sam Altman’s reliance on LLMs as the foundation of AI agents is a step in the wrong direction. While advancements like the 01 model can help, the real key to building effective AI agents lies in a software-centric approach, which provides better scalability, lower costs, and more reliable results. By focusing on solving problems with traditional software where possible and using LLMs only when necessary, AI agents can be more efficient and effective.

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