Large Language Models (LLMs) and intelligent agents have transformed how we interact with technology. However, a fundamental limitation persists: the rigid, predetermined way these systems manage their "thinking" time. This constraint creates an artificial barrier between humans and AI that limits the potential for truly collaborative workflows.
When we interact with an LLM or agent today, the process typically resembles a race to completion. We submit a prompt, and the model generates a response as quickly as possible. While we can influence output length, the underlying processing approach follows one of three preset modes:
These options represent distinct operational models—likely requiring separate codebases—and critically, users must commit to one up front. This creates a clunky, "all or nothing" experience that doesn't mirror how humans naturally collaborate.
This rigid approach forces users to predict the level of effort needed before the task even begins:
What if, instead of choosing between these fixed modes, an AI could dynamically adjust its effort based on the task at hand while maintaining an ongoing dialogue with us?
A truly next-generation AI system would empower agents to dynamically adjust their thinking and response time. Instead of predetermined modes, imagine an agent that can:
When presented with a complex task, the agent provides an initial estimate of the time required. If you request a comprehensive analysis that would typically take an hour, but you need it in five minutes, the agent could: