Observations from Building with AI Agents (tomtunguz.com)

In a reflective post on building AI agent systems, an engineer shares nine insightful observations from a year of hands-on experience. Key highlights include the importance of utilizing state-of-the-art models for prototyping and how fine-tuning smaller models like Qwen 3 can lead to superior performance in specific tasks. The discussion emphasizes the need for employing robust programming languages like Rust to catch errors early, along with advice to leverage multiple AI agents collaboratively for task management and improvement. This approach can create a closed feedback loop that enhances performance over time. Significantly, the article points to a transformative shift in the AI landscape, likening it to the "iPhone 15 era," where powerful models such as Qwen 3 and GLM deliver high performance at lower costs. This shift allows developers to focus on cost-efficiency over sheer accuracy in model selection. The author also notes the crucial role of documentation and automatic prompt management in optimizing agent interactions, empowering developers to iterate and refine their systems continuously without interruption. Overall, these insights provide valuable guidance for those navigating the complexities of AI agent development, highlighting the evolving capabilities and strategic approaches in the field.