This will drive development of systems that error-correct at scale, and orchestration of agents that feed back into those systems at different levels of abstraction to compensate for those modes of failure.
An AI software company will have to have a hierarchy of different agents, some of them writing code, some of them doing QA, some of them doing coordination and management, others taking into account the marketing angles, and so on, and you can emulate the role of a wide variety of users and skill levels all the way through to CEO level considerations. It'd even be beneficial to strategize by emulating board members, the competitors, and take into account market data with a team of emulated quants, and so on.
Right now we use a handful of locally competent agents that augment the performance of single tasks, and we direct them within different frameworks, ranging from vibecoding to diligent, disciplined use of DSL specs and limiting the space of possible errors. Over the next decade, there will be agent frameworks for all sorts of roles, with supporting software and orchestration tools that allow you to use AI with confidence. It won't be one-shot prompts with 15% hallucination rates, but a suite of agents that validate and verify at every stage, following systematic problem solving and domain modeling rules based on the same processes and systems that humans use.
We've got decades worth of product development even if AI frontier model capabilities were to stall out at current levels. To all appearances, though, we're getting far more bang for our buck and progress is still accelerating, and the rate of improvement is still accelerating, so we may get AI so competent that the notion of these extensive agent frameworks for reliable AI companies will end up being as mismatched with market realities as those giant suitcase portable phones, or integrated car phones.
An AI software company will have to have a hierarchy of different agents, some of them writing code, some of them doing QA, some of them doing coordination and management, others taking into account the marketing angles, and so on, and you can emulate the role of a wide variety of users and skill levels all the way through to CEO level considerations. It'd even be beneficial to strategize by emulating board members, the competitors, and take into account market data with a team of emulated quants, and so on.
Right now we use a handful of locally competent agents that augment the performance of single tasks, and we direct them within different frameworks, ranging from vibecoding to diligent, disciplined use of DSL specs and limiting the space of possible errors. Over the next decade, there will be agent frameworks for all sorts of roles, with supporting software and orchestration tools that allow you to use AI with confidence. It won't be one-shot prompts with 15% hallucination rates, but a suite of agents that validate and verify at every stage, following systematic problem solving and domain modeling rules based on the same processes and systems that humans use.
We've got decades worth of product development even if AI frontier model capabilities were to stall out at current levels. To all appearances, though, we're getting far more bang for our buck and progress is still accelerating, and the rate of improvement is still accelerating, so we may get AI so competent that the notion of these extensive agent frameworks for reliable AI companies will end up being as mismatched with market realities as those giant suitcase portable phones, or integrated car phones.