Some hard problems in agent design bear a striking resemblance to human cognition. This resemblance does not necessarily mean these solutions were drawn from neuroscience; rather, it shows that good engineering often arrives at the same patterns the human mind reached long ago. Letβs look at three of them.
Focus, not clutter
The human mind does not hold all of the dayβs concerns in working memory at once; at any moment, it focuses only on what is relevant to the task at hand. Agents require the same discipline. When an agent has access to everything, processing costs increase, and β more importantly β irrelevant context causes the model to hallucinate and err.
The solution is to scope context to the userβs intent. First, detect what the user wants, then give each component only the slice of information needed for that intent. A component whose job is distributing tasks does not need the conversation history; it only needs the task list. This is, in effect, the principle of βleast privilegeβ applied to knowledge: each component receives only what its job requires. The result is an agentic system that is both cheaper and more accurate.
Knowing when the job is done
A person usually has a clear sense of whether they have finished a task β a simple self-awareness of where the work stands. A capable agentic system needs the same: a component whose sole responsibility is to evaluate if the work is actually complete.
The subtle point is that this judgement does not require the intelligence of a language model. βAre all the tasks done?β is a matter of arithmetic, not reasoning; you simply count the completed tasks. Handing this to a language model is expensive and needlessly fragile. It is better for this completion monitor to be deterministic and predictable. Separating the responsibility of detecting the end of the work into an independent, deterministic component makes the system both simpler and more robust.
Separating the layers of memory
A human does not have one monolithic memory. They have several, with different lifespans and functions: what is in mind right now is not the same as what is recalled from years ago. Agents benefit from the same separation.
In agent design, you must separate at least two kinds of memory. First is coordination memory: temporary data that only makes sense during a single request β the answer to βhow are we working together right now?β This data is transient and is cleared when the work ends. What is cleared, however, is only the request-local working context β the live scratchpad of how components are cooperating in that moment β not the record of what happened and why; that account of the reasoning is kept in durable memory. Second is domain memory: what we know about the user and the conversation, which must persist over time. This separation carries a significant advantage: even if an error occurs midway through a task, domain memory is untouched and accumulated knowledge is not lost. Set the lifespan of each piece of data by its role, not with a single rule for everything.
The resemblances arenβt accidental
These three patterns β context focus, the completion monitor, and multi-layer memory β are engineering answers to specific problems. But their resemblance to familiar patterns of human cognition is not accidental. When you are dealing with limited attention, the probability of error, and the need for durability, good solutions converge. The human mind is not a bad roadmap for designing agents.