Agent-based AI systems have emerged as a powerful layer on top of large language models (LLMs). One such architecture, called OpenClaw, represents a class of systems that orchestrate LLMs through memory, planning, tool use, and feedback loops. This raises a serious question: is an OpenClaw-style system, combined with today’s LLMs, sufficient to develop Artificial General Intelligence (AGI)?

The answer is interesting…

With current functionality, OpenClaw-like systems can already exhibit behavior that appears intelligent in a broad sense. By combining multiple LLMs, task decomposition, long-term memory, self-reflection, and execution tools, these systems can solve complex, multi-step problems across domains. They can plan, revise their own outputs, learn user preferences, and improve their performance over time. This represents a significant leap beyond single-prompt language models.

However, this improvement is primarily behavioral, not cognitive. Today’s LLMs remain static in their core representations. They do not change their internal understanding of the world through experience. OpenClaw can store feedback, adjust prompts, select better strategies, and refine workflows, but the underlying learning mechanism of the LLMs remains frozen. As a result, the system becomes better at using intelligence, not at creating new intelligence.

This distinction is critical for AGI. True general intelligence requires continuous learning that alters internal representations, the formation of new abstractions, and the ability to generalize knowledge in fundamentally novel situations. OpenClaw today compensates for these limitations externally—through memory systems, critics, and planners—rather than internally, through learning at the model level.

That said, this architecture creates strong pressure for change. As feedback loops become tighter and more persistent, more “intelligence” accumulates outside the model itself. Over time, it becomes inefficient to endlessly correct, prompt, and scaffold a static model. The natural next step is to allow limited, controlled updates to representations—through adapters, modular fine-tuning, or other constrained online learning mechanisms. This would mark the beginning of a shift in the learning mechanism itself.

Looking at a plausible timeline, the next years will likely bring increasingly capable agent systems that feel highly autonomous and general, while still relying on static LLMs. We may see partial online learning integrated into agent architectures, allowing experience to influence representations in narrow and reversible ways. Beyond that, more radical forms of meta-learning and representational self-modification could emerge, pushing systems closer to weak or proto-AGI.

In conclusion, OpenClaw combined with today’s LLMs may not be sufficient to produce AGI on its own. However, it may be the most plausible environment in which AGI-enabling learning mechanisms evolve. AGI is unlikely to arrive as a single breakthrough model; instead, it may emerge gradually from agent systems that are forced, by their own success, to learn how to truly learn.