I’ve been thinking about why AI agents keep failing — and I think it’s the same reason humans can’t stick to their goals

So I’ve been sitting with this question for a while now. Why do AI agents that seem genuinely smart still make bafflingly stupid decisions? And why do humans who know what they should do still act against their own goals? I kept coming back to the same answer for both. And it led me to sketch out a mental model I’ve been calling ALHA — Adaptive Loop Hierarchy Architecture. I’m not presenting this as a finished theory. More like… a way of thinking that’s been useful for me and I wanted to see if it resonates with anyone else.

The basic idea Most AI agent frameworks treat the LLM as the brain. The central thing. Everything else — memory, tools, feedback — is scaffolding around it. I think that’s the wrong mental model. And I think it maps onto a mistake we make about ourselves too. The idea that there’s a “self” somewhere in charge. A central controller pulling the levers. What if behavior — human or AI — isn’t commanded from the top? What if it emerges from a stack of interacting layers, each one running its own loop, none of them fully in charge? That’s the core of ALHA.

The layers, as I think about them Layer 0 — Constraints. Your hard limits. Biology for humans, base architecture for AI. Not learned, not flexible. Just the edges of the sandbox. Layer 1 — Conditioning. Habits, associations, patterns built through repetition. This layer runs before you consciously think anything. In AI this is training data, memory, retrieval. Layer 2 — Value System. This is the one I keep coming back to. It’s the scoring engine. Every input gets rated — good, bad, worth pursuing, worth ignoring. It doesn’t feel like calculation. It feels like intuition. But it’s upstream of logic. It fires first. And everything else in the system responds to it. Layer 3 — Want Generation. The value signal becomes a felt urge. This is important: wants aren’t chosen. They emerge from Layer 2. You can’t argue someone out of a want because wants don’t live at the reasoning layer. Layer 4 — Goal Formation. The want gets structured into a defined objective. This is honestly the first place where deliberate thinking can actually do anything useful. Layer 5 — Planning. Goals get broken into steps. In AI, this is where the LLM lives. Not at the top. Just a component. A very capable one, but still just one piece. Layer 6 — Execution. Action happens. Tokens get output. Legs walk. Layer 7 — Feedback. The world responds. That response flows back up and gradually rewires Layers 1 and 2 over time.

The loop Input → Value Evaluation → Want → Goal → Plan → Action → Feedback → [back to Layer 1 & 2] It doesn’t run once. It runs constantly. Multiple loops at different speeds simultaneously. A reflex loop closes in milliseconds. A “should I change my life?” loop runs for months. Same structure, different time constants.

The thing that keeps nagging me about AI agents Current frameworks handle most of this reasonably well. Memory is Layer 1. The LLM is Layer 5. Tool use is Layer 6. Feedback logging is Layer 7. But nobody really has a Layer 2. Goals in today’s agents are set externally by the developer in a system prompt. There’s no internal scoring engine evaluating whether a plan aligns with what the agent should value before it executes. The value system is basically static text. So the agent executes the letter of the goal while violating its spirit. It does what it was told, technically. And it can’t catch the misalignment because there’s no live value evaluation happening between “plan generated” and “action taken.” I don’t think the fix is a smarter planner. I think it’s actually building Layer 2 — a scoring mechanism that runs before execution and feeds back into what the agent prioritizes over time.

Why this also explains human behavior change Same gap, different substrate. You know junk food is bad. That’s Layer 4 cognition. But your value system in Layer 2 was trained through thousands of reward cycles to rate it as highly desirable. Layer 2 doesn’t care what Layer 4 knows. It fired first. Willpower is a Layer 5/6 override. You’re fighting the current while standing in it. The system that built the habit is tireless. You are not. What actually changes behavior isn’t more discipline. It’s working at the right layer. Change the environment so the input never reaches Layer 2. Or build new repetition that gradually retrains Layer 1 associations. Or — hardest of all — do the kind of deep work that actually shifts what Layer 2 finds rewarding.

Where I’m not sure about this Honestly, I’m still working through a few things:

Layer 2 in an AI system — is it a reward model? A judge LLM? A learned classifier? I haven’t settled on the cleanest implementation. The loop implies the value system updates over time from feedback. That’s basically online learning, which has its own mess of problems in production systems. I might be collapsing things that shouldn’t be collapsed. The human behavior layer and the AI architecture layer might just be a convenient analogy, not a real structural parallel.

Would genuinely like to hear if anyone’s thought about this differently or seen research that addresses the Layer 2 gap specifically.

TL;DR Been thinking about why AI agents fail in weirdly predictable ways. My working model: there’s no internal value evaluation layer — just a planner executing goals set by someone else. Same reason humans struggle to change behavior: we try to override execution instead of working at the layer where the values actually live. Calling the framework ALHA for now. Curious if this framing is useful to anyone else or if I’m just reinventing something that already has a name.