Appearance
How Far Is Today’s AI from Being an Agent or AGI?
The question of how close current AI systems are to becoming true agents is often framed in terms of capability: larger models, stronger reasoning benchmarks, better performance than humans in specific tasks. But this framing already misses the point. A calculator surpassed human arithmetic long ago, yet no one considered it intelligent. Capability alone has never defined agency.
What people are increasingly sensing is a mismatch. Models are becoming more powerful, yet they do not feel more autonomous, more responsible, or more reliable. This is not a temporary gap caused by immature engineering. It reflects a structural distance that scaling alone cannot bridge.
Consciousness without stability
If we look at today’s large models honestly, they resemble something closer to an unstable consciousness layer than a complete mind. They can maintain coherent dialogue, reflect context, and respond in ways that appear intentional. In this sense, they already imitate part of what human consciousness looks like from the outside.
But this imitation is fragile. Hallucinations are not rare bugs; they are symptoms. The system has no internal structure that allows it to decide whether it should answer, whether it knows enough, or whether it is drifting away from reality. Humans make mistakes too, but they possess an internal sense of confidence, doubt, and responsibility that constrains behavior. Current AI lacks this stabilizing structure.
Even if we assume today’s models represent an early form of consciousness-like processing, that layer itself is not yet reliable.
The absence of causal self-update
A more fundamental gap appears when we ask how an agent changes itself.
A minimal agent must be able to absorb the consequences of its actions and update its internal state accordingly. In humans, experience modifies judgment, habits, and future behavior through a continuous causal loop. In contrast, today’s AI systems do not truly update themselves. Their behavior changes only through external interventions such as retraining, fine-tuning, or prompt engineering.
This is not an implementation inconvenience. It is a structural limitation. The system does not own its own causal evolution. Errors do not reshape it from within; they are corrected from the outside. Without internal causal update, there is no responsibility, and without responsibility, there is no agency.
Where thinking is missing
The most critical absence is not knowledge, data, or even reasoning speed. It is the lack of a genuine thinking layer.
Current models operate by refining existing structures. They interpolate, extrapolate, and recombine patterns that already exist in their training distribution. This is powerful, but it is not the same as generating new structural hypotheses and actively rejecting incorrect ones.
Human thinking, especially in moments of innovation, works differently. With very little information, a person can reconstruct the underlying structure of a system and discard vast spaces of possibility as wrong. This capacity is not a natural consequence of more data. It is a different mode of computation altogether.
Scaling has never addressed this layer, which is why innovation in AI remains derivative rather than generative.
Memory as a structural limitation
Another overlooked gap lies in memory. Human agency depends on the ability to carry context across long spans of time and experience. People can integrate years of interaction, learning, and personal history into a single continuous sense of situation.
Current AI systems cannot do this. Their memory is constrained by context windows, session limits, and fragmented representations of past interactions. Even when external memory mechanisms are added, they remain shallow indexes rather than deeply integrated experiential history.
This limitation directly affects agency. An entity that cannot sustain long-term context cannot form long-term responsibility. Without persistent memory, there is no enduring consciousness, only a sequence of disconnected responses.
Why outperforming humans proves nothing
It is tempting to argue that because AI systems outperform humans in certain domains, they must be approaching general intelligence. But outperforming humans has never been a valid criterion for agency.
Search engines outperform human memory. Optimization algorithms outperform human planning in narrow spaces. None of these systems are agents. Agency is not about winning benchmarks; it is about being accountable for decisions across time under uncertainty.
Until a system can understand its own limits, update itself causally, and maintain continuity through memory, performance comparisons remain irrelevant.
Why execution is withheld: correctly
The reason modern AI systems are not granted autonomous execution power is not fear or conservatism. It is structural realism. An unstable consciousness layer, lacking thinking and causal self-update, cannot be trusted with irreversible actions.
Granting execution to such a system would not reveal agency; it would amplify risk. The absence of agency is precisely why control remains external.
Distance measured in layers, not years
The distance between today’s AI and true agents is not a matter of time or scale. It is a matter of missing layers.
At minimum, an agent requires a stable consciousness structure, an internal causal update loop, a genuine thinking layer capable of structural innovation, and a memory system that supports long-term continuity. None of these emerge automatically from more data or larger models.
Until these structures exist, calling current systems “agents” confuses computational power with existential responsibility.
Closing
Today’s AI systems are extraordinary tools. They represent an unprecedented concentration of capability. But agency has never been a byproduct of capability.
Further Reading
This essay presents a structural perspective rather than a formal theory. Readers interested in precise definitions and SRT may refer to:
- SRT Overview
- SRT White Paper It is a structural state. And that state has not yet appeared.