Agents Done Right: A Framework Vision for 2026 (blog.bryanl.dev)

A new vision for agent frameworks in AI/ML was announced, aiming to address significant issues with current LLM-based agents. The current landscape is characterized by complexity and inefficiency, leading to problems such as context exhaustion, repetitive loops, and overwhelming options that hinder productivity. The proposed framework emphasizes "convention over configuration," simplifying the user experience by implementing strong defaults for model selection and context management. It encourages the use of subagents as the primary scaling mechanism, allowing tasks to be broken into manageable subtasks, each with its own context, thereby enhancing focus and reducing resource waste. The framework introduces several core principles, including predefined agent archetypes optimized for specific tasks (like searching or writing), mandatory checkpoints for risky operations, and streamlined human-agent workflows that facilitate faster code review. With a curated toolset that avoids overwhelming users with choices and encourages effective tool utilization, the framework aims to make architecture that can efficiently handle complex tasks more accessible. This vision for 2026 is significant as it could revolutionize how developers interact with AI agents, promoting productivity and reducing friction in coding environments.