Robert Ta explains why AI agents struggle when users can't articulate what they want. He contrasts deterministic problems like math with subjective tasks like deciding what to eat, where answers multiply infinitely. Building AI products means modeling each individual user within the context window to reduce tokens and turns.
If you're using AI from the spectrum of like, help me figure out a cooking recipe, to booking a flight, to researching what to buy, or help me be a therapist, be a therapist for me, help me with my relationship problems. Then on the other end, it's like, I know we usually talk about help me build a mobile app, help me build this front end, help me build the server code, whatever, help me make money. And there's trillions of things and intents that people have when they're engaging with one of these agents, whether they be ChatGPT or Claude Code or Claude or whatever. And every human is different and has their own evidence beliefs for their worldviews, right? And that drives the intent, that drives the subjectivity of the goal. And there's this issue with goal in my head might not be goal that's stated cleanly out into the world because there's the perspective taking that must happen in the world from your partner or from the agent to be able to meet you and align on the goal. Me and Jonathan, we're a 2-man team and we pull 1099s to help us with AI-native services right now. We're trying to max revenue per employee, being super AI-forward. And we even have a hard time aligning because so much stuff is happening in the AI space and with our customers and prospects in the sales cycles or in our delivery cycles where we're like, dude, we got to stay aligned. We got to stay aligned, keep building our company brain and stuff. And so, if we're having these problems, I know every company is having the problem of we are a bunch of humans getting together to And then individual humans have their own goals. I got to pay my mortgage. I got to be able to pay for this vacation, or I got to pay for my kids' schooling. How does that intersect with the goals of you working with an agent on behalf of the enterprise or the company towards that bigger goal? That's a very hard problem to solve. One of the analogies or metaphors I've been using towards describing the problem of, hey, we're seeing that we could solve this Deterministically, like deterministic problems, like 2+ 2= 4, mathematics, science, logic. We could find causation and chains of goals. That's more deterministic. So one of the things I always tell people when I'm coaching AI product teams on how to actually objectively and measurably improve their AI, because they're coming from a software development lifecycle where they're like unit tests, integration tests, end-to-end testing, run Maestro, run Playwright, have like a self-hosted GitHub Runner that like runs the mobile app. And every time we ship to it, run that in staging, replicate the database from prod. All these things that you do as a developer to ensure you're compounding on your software asset to go capture market share incrementally, right? Like, that's how you see objective, measurable improvement in the software development lifecycle. And when I coach product teams on, okay, now you're building an AI product, an AI product is not just 2+ 2= 4. 8 billion people in the world hopefully could agree 2+ 2= 4. But if you go ask 8 billion people in the world, hey, What do you want for dinner tonight? You're going to get quadrillions of answers, right? Then you're going to get people that are like, I'm not sure. And then you have to go back and forth. And so that's really the problem of an LLM-based product is the what's for dinner type of problem where it's non-deterministic by nature and it's really dependent on the subjectivity of the human. Just think about that one case. Anybody can relate to this. Like you ask a friend group what's for dinner, like what do you guys want for dinner? All people will pop up and be like, hey, okay, maybe Thai food or Korean food or Mexican food. And then somebody will be like, oh, well, I had Mexican food 2 days ago. And somebody's like, oh, well, I don't really like Korean food, or I'm like a vegan. All of those things are what we mean by subjectivity in the individual that then leads to, if you map it out, the subjectivity of what do we inject into the context window? And then for Clarity, our company, we're really thinking about that. Like, how do we model the self? The person, the human driving the agent to maximize their agency and also minimize the amount of turns they have to get to their goal. So while Anthropic and others have released dynamic workflows with like these goals and loops and stuff like that, I think they missed the mark on the non-deterministic case of you have to model the human to actually get to the subjectivity of their goal, to then maximize what we know about them, to inject the right context at the right time and optimize the token spend to get the human with the agent, whether it's coding or not, to their goal.
