Building Reliable AI Agents (parthsareen.com)

A recent announcement highlights significant advancements in developing reliable AI agents, focusing on providing appropriate structure through tool design and context engineering. The insights stem from the experience of the founder of Extensible AI, who emphasizes that building these agents should leverage foundational principles of software engineering. By utilizing directed acyclic graphs (DAGs), modern AI agents can effectively navigate tasks in a dynamic loop, continuously refining their actions towards achieving specified goals. This approach streamlines the execution pattern where models generate responses, execute tools, and iteratively build upon previous results until objectives are met. As the landscape of AI models evolves, the release of various open-source models—such as Kimi-K2 and Minimax M2.1—has transformed the way agents operate, making them suitable for 90% of tasks efficiently. The discussion outlines how these models leverage control theory and reinforcement learning, emphasizing policy optimization techniques like GRPO and PPO for improved task execution. The necessary crafting of system prompts and thoughtful tool design will be pivotal in training models effectively, ensuring agents can adapt to various operational contexts, thus enhancing their reliability and performance in real-world applications.