Are Multi-Agent Systems the Real Path to AGI?

Описание: Everyone is searching for a credible path to artificial general intelligence. While large language models have become extremely powerful at prediction, generation, and multimodal understanding, the bigger question remains: is scaling models enough, or do we need something more?

In this episode, we explore agentic AI and multi-agent systems as a potential path to AGI. We break down the anatomy of an agentic stack, from the base LLM that acts as the cognitive engine, to planning and memory layers, tool usage, environments, and feedback loops. Together, these components enable proactive systems that can plan, act, and execute tasks autonomously rather than waiting for prompts.

We also examine why multi-agent systems feel more intelligent than standalone LLMs. Hierarchical and specialized agents collaborate like a human organization, working toward shared goals and validating each other’s outputs. This makes agentic systems powerful for automation and complex workflows.

However, we end with a critical insight. While agentic AI dramatically enhances what LLMs can do, it does not fundamentally solve machine cognition. Without a shift from probabilistic token prediction to grounded causal world modeling, multi-agent systems remain an orchestration layer rather than true general intelligence.