Agentic systems: panning for gold - by Oana Olteanu
In the rush to harness AI's potential, it’s essential to distinguish between groundbreaking innovations and overhyped promises. While fully autonomous AI agents, Level 5, are a distant reality, significant value can already be realized with current Level 3 agentic systems. This post explores how businesses can leverage these systems effectively, avoid common pitfalls, and prepare for future advancements.
One can think of agentic system automation as similar to the five levels of automation of self-driving cars. Today, most agentic systems are Level 3, like summarizing a call and searching legal documents for specific use cases, but truly autonomous agents, Level 5, are a long way off. The primary barrier to reaching Level 5 autonomy is training. It could take a decade to address long-tail failures, highlighting the necessity of realistic expectations and incremental advancements. There are no magic shortcuts.
Current AI models excel at understanding through rich ontologies (e.g. that water is wet, things fall down, a son has a mother, etc), but lack true reasoning capabilities. Reasoning implies a self-reflective capacity. Larger models have a deeper/more sophisticated ontology, which they can then use to understand the prompt, not in terms of its text, but the abstract concepts described therein, which map to items in its ontology. The model can guess the likely next item in the output sequence from this understanding.
