Building agents remains challenging, especially when working with SDKs like OpenAI or Anthropic. Armin Ronacher emphasizes that higher-level abstractions, such as the Vercel AI SDK, often fail to provide the necessary tools for effective agent development. The differences between models necessitate a custom agent abstraction, as existing SDK solutions don't adequately address specific needs like cache management or reinforcement learning. Ronacher notes that direct use of dedicated SDKs offers better control and clarity, particularly when dealing with provider-side tools, which can create complications in message handling. Cache management is another critical aspect. While Anthropic's model requires explicit cache management, Ronacher has come to appreciate this approach. It allows for better control over costs and utilization, enabling strategies like running parallel conversations or editing context. He outlines a straightforward caching system with specific points after the system prompt and at the start of conversations. Reinforcement within the agent loop helps respond to changes and failures, allowing for adjustments based on previous tasks. Techniques like self-reinforcement, where tools echo tasks back to the agent, also help maintain progress without overloading the context with failures. Ronacher's exploration of context editing reveals both its potential and pitfalls. While it could help streamline the agent's understanding by removing unsuccessful attempts, it also invalidates caches, raising questions about whether the trade-off is worth the cost. The agents he's working on, which focus heavily on code execution, require a robust data storage solution; he opts for a virtual file system to better manage different tools. This setup is crucial for subagents and facilitating complex tasks.