A reflective discussion on designing AI agents that intelligently choose the type of thinking needed for a task, proposing a control layer for task classification, attention, and memory management, inspired by human cognition.
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.
This position paper argues that incorporating metacognition as a design principle can lead to more accurate, secure, and efficient AI systems, and demonstrates the concept through a Federated Learning case study and a software framework for experimentation.
A discussion on the practical challenges of managing agent memory in AI systems, focusing on avoiding information overload that degrades output quality, and proposing strategies like using workflow state and multi-agent architecture.
An exploration of how AI agent memory systems often miss crucial cognitive processes like working memory, drawing parallels to anterograde amnesia, and offering design guidance for more effective solutions.
The article reflects on the evolving relationship between humans and AI, arguing that as AI becomes more autonomous, the key challenge is understanding human decision-making and purpose, rather than just technical capability. It suggests shifting from using AI as a mere tool to collaborating with it as an instrument that enhances human judgment.
A reflection on agent memory as primarily an infrastructure/data-management problem rather than an AI problem, focusing on practical complexities like permissions, scopes, and revision history.