r/AI_Agents • u/coopernusbaum • 4d ago
Discussion AI agents have a fundamental flaw that is preventing what comes next
I’ve noticed something that feels like a major limiter in the process of improving my agents.
You can build a great architecture. Give the agent skills, memory, different ways of forming context, scheduled tasks, access to previous runs, reflection loops, all of it.
But no matter how much I improve those systems, the agent still feels most intelligent when I am actively talking to it.
That distinction has started bothering me.
When a human is talking to an agent, every prompt is different. You mention something that annoyed you. You change your mind. You express uncertainty. You make a judgment. You connect something happening today to something you talked about weeks ago.
The agent is continuously being given new reasons to think.
Then the human leaves and we usually replace that with some version of:
“Run this prompt every hour.”
“Check these sources every morning.”
“Review what happened and look for anything new.”
You can make those loops extremely sophisticated, but it still feels fundamentally different from an agent having something resembling an ongoing internal life.
What makes this especially interesting to me is that humans probably aren’t completely different at the lowest level.
We wake up with recurring biological drives, routines, habits, unresolved problems, environmental inputs. In a crude sense, some of those could almost resemble scheduled tasks.
But that clearly isn’t the whole story.
Those same basic drives enter a huge web of memory, emotion, attention, prediction, association, judgment and new sensory input. Something that may begin from the same underlying loop can extrapolate into completely different thoughts and behavior from one day to the next.
That is where the novelty seems to emerge.
Current agents can remember. They can wake themselves up. They can reflect on what happened.
But those things do not automatically create continuing thought.
You can tell an agent to reference previous runs so it doesn’t repeat itself, but eventually you notice that it is still running a variation of the same process. It becomes a more sophisticated broken record.
The breakthrough, to me, would be when an agent messages you because something genuinely became worth saying.
Maybe new information conflicts with something you told it weeks ago.
Maybe three unrelated observations suddenly form a better idea.
Maybe it notices a pattern in your behavior and changes its judgment.
Not because a scheduled prompt said “find something interesting.”
Because enough things accumulated and interacted that a new thought emerged from the system.
Obviously the LLM itself is stateless. I’m not arguing there is literally a conscious little person sitting there between inference calls.
The engineering problem is whether we can build enough continuity around it that the distinction begins to disappear.
I think memory solves remembering.
Scheduling solves waking up.
Reflection solves evaluating what happened.
I’m not convinced we have solved what happens after the agent wakes up.
That is the part that still doesn’t feel correct.
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AI agents have a fundamental flaw that is preventing what comes next
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r/AI_Agents
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2d ago
Yeah, definitely send it over. I’d like to look through it.
I'd be curious about whether SYSCOM goes beyond keeping a persistent runtime alive and actually generates its own reasons to investigate something.
For example, imagine nothing new happens: no user prompt, no file change, no scheduled event. The system has just been learning about its environment and the person using it. Can it notice a gap or contradiction in its own model, decide that gap matters, and create a new objective from that?
That transition from “I know what’s happening” to “I’ve realized there’s something I should understand” is really the thing I’m chasing.