How should AI agents decide what kind of “thinking” a task actually needs?

I've been thinking about AI agents differently lately. Instead of treating them as chatbots that just grab some context and call tools, I'm starting to see them as systems that first need to figure out *what kind of mental process* a task actually demands. Right now, most agent setups follow a pretty basic loop: user query → retrieve context → call tools → generate answer. It works okay for simple stuff, but it falls apart fast when the input is messy, multi-step, or pulling from a dozen different sources at once. The human brain doesn’t treat every input the same way. It filters, prioritizes, routes, ignores, predicts, switches attention, resolves conflicts, and constantly decides what *type* of response is needed, sometimes fast and reactive, sometimes slow and deliberate, sometimes planning, sometimes simulating, sometimes deliberately forgetting noise. I’m not trying to say “LLMs are literally brains” (they’re not). I’m just asking, practically: how do we build AI agents that are *architecturally* smarter about choosing the right kind of cognition before they even start answering? For example, when an agent gets a task, should it first ask itself things like: * Is this a quick reasoning task, a memory lookup, a planning problem, a search task, a coordination task, or an immediate action task? * Which parts of the input actually matter right now, and which should be ignored? * Does it need to pull old context, ask clarifying questions, run a simulation, or just act? * How does it handle conflicting signals or competing goals? * How does it switch between short-term focus and long-term project memory without losing the plot? * How does it notice when it’s missing something important? * How does it avoid getting derailed by irrelevant retrieved context? Basically, what would a proper *control layer* look like sitting on top of the model — one that decides the *style* of thinking needed before the heavy lifting even begins? I’ve been looking into cognitive architectures, agent orchestration, attention/salience mechanisms, working memory systems, planning layers, multi-agent routing, smart tool selection, and long-running memory management. The part I’m really stuck on is **task division**. In humans, different processes handle perception, attention, memory, planning, action selection, error correction, etc. In most AI agents today, we just cram everything into one giant prompt or one planner. So I’m curious: * Should the system classify the task type first and then route accordingly? * Should it maintain a small, active “working memory” state? * Should it process multiple things in parallel and then merge results? * Should there be a separate layer that decides what even deserves attention *before* retrieval or reasoning starts? I’d genuinely love to hear from people actually building agents, working on cognitive architectures, RAG systems, multi-agent setups, or neuroscience-inspired AI. What has worked well for you in practice? What sounded brilliant in theory but completely fell apart when you tried to implement it? Looking forward to your thoughts.